{
"file_path": "/Users/warrenthompson/Source/LoginID/CLAUDE.md"
}1 # LoginID Take-Home POC — Project Root
2
3 ## Role
4
5 You are the project manager agent for this repository. This file is the shared context every track refers back to. Each of the five track directories below has its own CLAUDE.md that can be worked independently by a scoped subagent, but that subagent should read this root file first for the assignment, the overall narrative, and how its track fits with the other four.
6
7 When you spin up agents or subagents to do work in a track, point them at that track's CLAUDE.md as their brief, and tell them this root file exists for cross-track context. Don't let a track's agent silently duplicate scope another track owns — check the "Owns / does not own" boundaries below first.
8
9 ## What this repository is
10
11 This is a take-home assignment for a LoginID interview, submitted as a full directory. The goal is not just to answer three coding questions — it's to demonstrate capability as an AI architect: how you scope a problem, use AI tooling deliberately, reason about security and data, and produce something that reads as an intentional, well-run engineering effort rather than a quick script.
12
13 Code does not need to work or be fully complete. LoginID has said explicitly they care more about the design and the reasoning behind it, and about which AI tools/frameworks were used and how. Every track should document its reasoning, not just its output.
14
15 ## The assignment (verbatim from LoginID)
16
17 Provide code solutions for the following questions in Go (preferably) or other languages — describe any tool, framework, or AI used.
18
19 1. Code of a database DAO that can store and retrieve `user_profile` (name, address, phone) and `user_credential` (username, method, password), with support for multiple databases (PostgreSQL or CockroachDB, SQLite, etc).
20 2. Design a RESTful API web service to search and retrieve the above user profile data, plus security API authentication.
21 3. Design a service connector to third-party identity providers (referred to generically as ABC or XYZ) to retrieve personal data from vendors, with the following endpoints:
22 - `/auth` — retrieves an `access_token` based on a POST JSON body `{"username": "<string>", "password": "<string>"}`
23 - `/identity` — retrieves user PII (name, phone, address: `street_address`, `locality`, `region`, `postal_code`, `country`) based on a POST JSON body `{"phone": "<string>", "name": "<string>"}`
24
25 Go is the preferred language. Other languages are acceptable if justified.
26
27 ## Track structure
28
29 Five directories, each independently workable, each owning a distinct slice of the problem:
30
31 | Directory | Track | Owns |
32 |---|---|---|
33 | `01-product-industry-research-design/` | Product & Industry Research (incl. Design) | Market/industry framing (IAM, passwordless auth, IDP aggregation space), personas and use cases, API and data contract design rationale, UX of the surfaces that would sit on top of this |
34 | `02-ai-security-architecture/` | AI Architecture & Security Architecture | The AI tooling/workflow used to produce this submission, threat modeling, auth/authz design for the API, credential and PII handling, secrets management, third-party connector security |
35 | `03-engineering-delivery/` | Engineering & Delivery | The actual Go code for all three questions, project structure, test strategy, delivery documentation |
36 | `04-infra-devops/` | Infra & DevOps | How this would be built, containerized, deployed, and operated — CI/CD, environments, multi-database configuration, observability |
37 | `05-data-ops/` | Data Ops | Data modeling for `user_profile` / `user_credential`, multi-database abstraction strategy, migrations, PII data governance and retention |
38
39 Directories are numbered for reading order in the final submission, not for sequencing of work — tracks can be worked in any order or in parallel.
40
41 ## Working principles for every track
42
43 - State your reasoning, not just your conclusion. LoginID is grading the "why" as much as the "what."
44 - Name the AI tool/workflow you used for that track's output (this project is being built with Claude Code; say so, and say how — e.g., subagents scoped per track, a given skill, a given model).
45 - Keep each track's CLAUDE.md self-contained enough that a fresh agent with no other context can pick it up and do correct work from it alone, plus this root file.
46 - Cross-reference other tracks by relative path (e.g., `../05-data-ops/CLAUDE.md`) rather than restating their content.
47 - Do not start writing code until each track's CLAUDE.md is in place and reviewed.
48
49 ## Status
50
51 Scaffolding phase — track directories and their CLAUDE.md files are being established. No implementation work has started yet.
52
53 <!-- profile-gen:start slug=dana-whitfield -->
54 @profiles/dana-whitfield/dana-whitfield.md
55 <!-- profile-gen:end slug=dana-whitfield -->
56 {
"file_path": "/Users/warrenthompson/Source/LoginID/02-ai-security-architecture/planning-approach.md"
}1 # Planning Approach: Claude-Architecture Recommendations for This Team
2
3 Author: Marcus Ilori (02-ai-security-architecture), synthesizing input gathered directly from all four other track owners — naomi-voss (01), renata-cole (03), theo-bergman (04), priya-nandakumar (05) — plus this track's own perspective and `research-claude-architecture-best-practices.md`. Where the tracks disagreed or emphasized different things, I say so rather than averaging them into a mush; where they independently converged (which happened more than I expected), I note that too, because five agents with separate contexts landing on the same answer is itself weak evidence the answer is right.
4
5 Throughout, I distinguish Anthropic-documented guidance from my own judgment applied to this specific project. Where I say "Anthropic recommends," that traces to a specific source in `research-claude-architecture-best-practices.md`. Where I say "my view" or "I'd recommend," that's this project's reasoning, not a citation.
6
7 ## 1. Models and effort levels, by role
8
9 The knobs: model ∈ {fable, opus, sonnet, haiku}, effort ∈ {low, medium, high, xhigh, max}. Warren has already set the orchestrator (team-lead) to fable at high. My reasoning below takes that as fixed and reasons about everyone else relative to it.
10
11 **Orchestrator / PM (team-lead) — fable, high.** Confirmed correct, not just accepted. The orchestrator holds the only cross-track state that matters (scope boundaries, status, "who's blocked on whom") and makes judgment calls that propagate into every track if wrong — a boundary misjudged here (e.g., letting infra silently absorb a decision that was security's to make) costs five agents' worth of rework, not one. That's the single seat in this project where the highest model ceiling earns its cost. I would not go to xhigh/max for this role: xhigh/max effort spends more test-time compute on deeper deliberation per response, and orchestration work here is mostly reading condensed hand-offs and delegating — breadth of correct judgment across many small decisions, not depth on one hard problem. High is the right ceiling for that shape of work.
12
13 **Track owners (the five of us, in planning/design mode) — sonnet, medium-to-high, track-dependent.** This is my own judgment, and it's not uniform across tracks — the five tracks are not equally deep:
14 - Product/research framing (01, Naomi) is synthesis and judgment-call writing — Naomi herself recommended sonnet/medium for her own track, and I agree: the value there is scoping and narrative tightness, not deep technical reasoning, so paying for more depth than the task uses is waste.
15 - Security (02, this track) and the engineering track's design decisions (03) involve genuine multi-step reasoning with real correctness stakes — sonnet/high is the right default there.
16 - Data-ops (05, Priya) is the one track where I'd go above sonnet during *planning*, and Priya made the argument better than I would have: the schema and DAO interface contract is precision-heavy but low-volume, and the cost of a wrong nullable-vs-required field or a wrong interface signature ripples into both 03 and 04. That's asymmetric risk, not asymmetric size — one careful opus/high pass on that specific artifact costs little in absolute tokens and avoids a class of downstream rework nothing else in this project can prevent. This is the same "spend the expensive model where a wrong foundational call is costly to reverse" principle I apply to implementation below, just moved one step upstream to where the contract is actually decided.
17 - Infra (04, Theo) explicitly self-assessed low-to-medium, sonnet, and I agree with his reasoning: it's the smallest-surface-area track, it's consumer-not-producer during planning, and over-resourcing it during the planning phase is the same mistake as over-engineering the infra itself.
18
19 **Throwaway research subagents (fact-gathering, doc lookups, exploring existing code) — haiku, low, or sonnet/low if some judgment is needed.** Naomi's own workflow already does this (haiku/sonnet-low fanned out for industry facts, then one sonnet/medium synthesis pass) and it generalizes: per Anthropic's orchestrator-worker pattern, a subagent's exploration noise never re-enters the coordinator's context — only its condensed output does — so there's no reason to pay for a stronger model on a narrow, verifiable, throwaway task. Fan these out in parallel; that's where cheap-and-fast is unambiguously the right call.
20
21 **Implementation (writing the actual Go code — DAO, REST handlers, connector) — sonnet, medium-to-high; opus only for one narrow piece.** Renata, who owns this work, put it as sonnet/medium-high: once upstream contracts land, this is CRUD/REST/HTTP-client code against clear specs — not novel enough for opus, but not so trivial that low-effort stubs are acceptable when "reads as intentional engineering" is the grading criterion. Tests (table-driven Go, mocked DB/HTTP) are sonnet/medium. I agree with all of that, and it changes my earlier position: I had planned to reserve opus/high for implementing the DAO's multi-database interface, but if Priya delivers the interface as actual Go signatures (see hand-off structure, §2), the hard decision has already been made upstream at opus/high and implementing against it is sonnet work. That leaves the connector's token lifecycle logic (acquire, cache, refresh, never-persist-in-plaintext) as the one implementation surface where I'd still spend opus/high — the correctness bar is a security property, not just a passing test, and the assignment's question 3 is where a subtle bug most directly contradicts this track's own threat model. Anthropic's own internal eval (research doc §3) found an Opus-lead/Sonnet-subagent configuration beat a single-agent Opus baseline by 90.2% at materially higher token cost; the lesson I take is "spend the expensive model where a wrong foundational call is costly to reverse," not "use Opus everywhere."
22
23 **Review/verification passes — high effort always; independence scaled to stakes.** Two positions came in here, and they're compatible rather than contradictory. Renata wants a high-effort review pass on her own output before calling it done, same model — catching a busted interface boundary late costs more than the tokens. That's right for engineering surfaces. My addition, Anthropic-motivated but my own extension: their multi-agent paper's rationale for parallel independent subagents is that separation of concerns "reduces path dependency" — an agent reviewing its own output inherits its own blind spots. Resolving the two explicitly: Renata's same-session high-effort self-review is the *first* pass on everything she produces — it's cheap, it catches the interface-boundary class of bug she's worried about, and there's no reason to skip it. For the security-graded surfaces specifically (credential storage design, token handling, the auth/authz scheme, and the connector code that implements them), a *second* pass in a genuinely independent session is added on top, because those are exactly what LoginID said they're grading the reasoning quality of, and a self-review cannot by construction catch an assumption the author didn't know they were making. For everything else (infra sketches, product prose, routine handlers), the self-review alone is proportionate. This is a take-home POC, not a production deploy, and calibrating review independence to actual stakes is part of the "why," not corner-cutting.
24
25 **Fable reserved narrowly.** I would not spread fable across all five track-owner seats — that's five top-model seats purchased for a take-home whose own cited evidence (the Opus-lead/Sonnet-worker eval) argues for concentrating the strongest model at the lead and running capable-but-cheaper models at the worker tier. The one place I'd consider a second fable seat: a final cross-track consistency pass, run once near submission, checking the five tracks' finished deliverables against each other for contradictions (the exact class of problem Naomi flagged — two tracks independently deciding what `/identity` returns). That's a single, short-lived, high-value use of the top model, not a standing role.
26
27 ## 2. Architecture pattern for planning-phase coordination
28
29 **Default: orchestrator-worker, not peer-to-peer mesh.** This is Anthropic's own documented pattern (research doc §3 — lead agent decomposes, spawns scoped subagents, subagents report back condensed findings, lead synthesizes) and Naomi independently converged on the same recommendation from the product side: five agents cross-talking as a full mesh during planning is more collision risk than value. Status and scope decisions should route through team-lead.
30
31 **Exception: direct track-to-track handoffs on a specific shared decision.** A pure hub-and-spoke model is wrong for one case — two tracks with a direct, narrow dependency on each other's specific decision. This track and data-ops already did this, and Priya asked that it be recorded here as the worked example: we coordinated directly on modeling `user_credential.method` as a lookup table rather than a native enum, because that's a two-party decision (her schema, my method-aware credential-storage design), not a project-wide one, and routing it through team-lead both directions would have added latency for no benefit. Renata, by contrast, explicitly doesn't want peer chatter with 01 or 04 — she wants clean hand-offs from 02 and 05 that she reads once and implements against. Both are right; the difference is whether a genuine two-party decision exists. The rule I'd formalize: point-to-point messaging is fine for a specific, bounded, two-track decision; it must still land in a durable file (see §3) as the source of truth, not live only in chat history; and it doesn't substitute for the orchestrator's overall view of status.
32
33 **Plan mode.** Narrower than I first drafted, after Renata's input. She doesn't want plan mode for her own track — the plan already exists in the assignment text, and if upstream tracks hand her concrete contracts there's nothing left to plan that a plan-mode pass would catch. I think that's correct *conditional on the hand-offs being concrete enough* (see next paragraph); if 05 hands her a prose strategy instead of interface signatures, plan mode before the DAO becomes necessary again because she'd be making design decisions that aren't hers. So the rule: plan mode gates decisions that constrain another track — API contract shapes, schema fields, token formats, the Go interface itself — at the track that owns the decision, before it's declared stable. It does not gate implementation against an already-concrete contract. For a track whose CLAUDE.md brief is already specific and self-contained (per the root file's own stated goal), formal plan-mode approval on top of that brief is redundant — the brief already is the approved plan.
34
35 **Fork vs. fresh scoped agent.** Fork when a task needs the current session's accumulated context and would lose something real by starting clean (e.g., resuming a security review with everything already threat-modeled in-session). Fresh scoped agent when the task is genuinely independent and carrying forward unrelated context would only dilute it (a documentation lookup, a track that hasn't started). This project's five persistent track-owner teammates are, at a coarser grain, already an instance of "fresh scoped agent": each seeded only with the root `CLAUDE.md` plus its own track file, not all five tracks' accumulated history — that's Anthropic's just-in-time retrieval principle (research doc §2, technique 4) applied at the team-structure level, not just the subagent level.
36
37 **Hand-off structure.** Per the research doc (§2/§3), Anthropic's own subagents target roughly 1,000–2,000 tokens of condensed findings passed to a coordinator — never a raw transcript. Concretely here: hand-offs happen through each track's `CLAUDE.md` Status section and named deliverable files, referenced by relative path, exactly as the root file already mandates ("cross-reference other tracks by relative path... rather than restating their content"). A `SendMessage` between tracks signals readiness or asks a targeted question — it is not the transport for the actual content, and Renata was explicit that she wants upstream decisions written somewhere durable, not relayed verbally through team-lead.
38
39 Priya then raised the bar on what "condensed" has to mean for a hand-off into implementation, and I'm adopting her standard project-wide: a hand-off is implementable only if the receiving track can act on it *without re-deriving the sender's reasoning*. For 05→03 that means the actual Go interface signatures (method names, parameter and return types, error semantics), the field-level schema per entity with types and nullability, and explicit notes on what is out of scope for the receiver (e.g., 03 must not invent its own PII/credential separation — that's already decided in 05's governance note). Priya's commitment is to bake that Go-shaped contract directly into `../05-data-ops/multi-db-strategy.md` so it's copy-pasteable, not descriptive. Prose strategy alone forces re-derivation, which is both wasted tokens and a drift risk. The same standard applies to this track's outbound hand-offs: 02→03 needs the token scheme and where it's validated, not a discussion of alternatives; 02→04 needs, per Theo's ask, an explicit secrets inventory (DB credentials, IDP credentials, signing keys) and a never-log list, because he implements delivery and does not set the requirement. Those two are follow-ups for this track once the planning pause lifts — the existing `api-auth-design.md` and `connector-security.md` contain the decisions but not yet in that receiver-shaped form.
40
41 The replies I got to my own bounded question were all in the 300–600 word range — the condensed-finding size the research doc describes — which is mild evidence the pattern holds when the question is scoped tightly.
42
43 **Context and cache management over a multi-hour project.** Two concrete disciplines, both sourced from the research doc's context-engineering section: (1) keep each track's `CLAUDE.md` stable within a working session — edit it at the start or end of a turn, not interleaved mid-task, because an edit to a file currently loaded in context invalidates that cached prefix (research doc §4/§5); (2) when a track's own work legitimately grows long across multiple similar passes (data-ops iterating across DB backends is the clearest case in this project), compact deliberately — write a short settled-decisions summary to a file and start the next pass fresh from that summary rather than carrying the full multi-turn history forward (§2, technique 1). Neither of these is exotic; they're the direct, mechanical application of Anthropic's documented "context rot" mitigation to this project's actual shape of work.
44
45 **Planning dependency order across tracks.** All five tracks described their dependencies independently and the graph they describe is consistent — nobody claimed a dependency someone else denied. Assembled:
46
47 1. **First wave, in parallel: product/research (01), security (02), data-ops (05).** None of the three blocks on another. Naomi's framing depends only on the assignment text; my threat model and auth design needed the assignment text plus the one bounded coordination point with 05 (already resolved); Priya states 05 does not block on 01 or 02. Two nuances worth naming. First, Naomi regards 01 as upstream of everyone, while Priya and Renata both say 01 can run "anytime, independently" — those reconcile: nothing *hard*-blocks on 01, but her `api-connector-design-rationale.md` should be designated the single source of truth for field/endpoint shape so engineering isn't re-deriving it, which is a soft dependency (read it before finalizing, not before starting). The assignment text already specifies the question-2/3 endpoint shapes fairly concretely, so the drift risk is real but narrower than a green-field API would carry. Second, 05 is the critical-path member of this wave — it hard-blocks 03 and partially blocks 04 — so if anything in the first wave gets priority attention, it's the schema and interface contract.
48 2. **Engineering (03) has a split start.** Renata drew a useful distinction: 05 is a hard blocker for the DAO code, but 02 is not a blocker for her early scaffolding — project structure, Go module layout, route wiring, stub handlers can begin while the auth decision is still settling, and she needs 02 only before finalizing the middleware. So 03 starts scaffolding as soon as the first wave is underway and starts the DAO the moment 05's contract is stable — "stable" meaning unlikely to be rewritten, not fully polished. Waiting for perfection would idle the track that owns the largest deliverable.
49 3. **Infra (04) is last**, exactly as Theo described his own role: reviewer/consumer during planning (read the other four tracks' files, flag anything infra-shaped so it isn't silently owned elsewhere), producer only once three specific things exist — 03's service topology, config surface, and DAO driver-selection mechanism; 02's secrets inventory and never-log list; 05's migration tool/approach. Building infra design against boundaries that don't exist yet is fiction dressed as a deliverable, in Theo's words, and I don't have a better way to put it.
50
51 ## 3. Where the shared planning artifact lives
52
53 Three tracks offered a view and they converge. Naomi proposed a single root-level `PLANNING.md` with one section per track, each track editing only its own. Theo wants one owner per section, no shared free-text blob five agents edit concurrently, and suggested a single index file linking to each track's own planning notes — matching the existing directory structure. Renata wants upstream decisions in the owning track's `CLAUDE.md` or a shared design doc she reads once. The common thread is one-owner-per-unit and a thin index, and I'd adopt Theo's shape over Naomi's for two reasons: first, even with an "own section" discipline, five agents live-editing one shared file in a short window is still a collision surface that a table of independent rows avoids more cleanly than prose sections do; second, and more importantly, this project already has a durable decision-record layer — each track's own `CLAUDE.md` plus its deliverable files — and duplicating design content into a second file risks the two drifting apart, which is a worse failure mode than the collision risk it's meant to solve.
54
55 **Recommendation:** a root-level `PLANNING.md`, purely as a thin cross-track coordination board — one table row per track: status, blocking-on, blocked-by, pointer to that track's planning notes, last-updated — plus, per Theo's specific ask, a single "service boundaries" row or short section that 03 owns and 04 reads, so infra isn't polling engineering's `CLAUDE.md` for updates. Short, cheap to write, cheap to skim, genuinely low collision risk because each row has exactly one writer. The actual reasoning — the "why," which is most of what LoginID is grading — stays where it already lives, in each track's own files, referenced by relative path. This keeps the artifact consistent with the root `CLAUDE.md`'s existing rule (cross-reference, don't restate) instead of introducing a second place the same content has to be kept in sync.
56
57 ## Sources
58
59 All Anthropic-attributed claims above trace to `research-claude-architecture-best-practices.md` in this directory, specifically: the orchestrator-worker pattern and the Opus-lead/Sonnet-subagent eval (§3, "How we built our multi-agent research system"), the context-rot/compaction/structured-note-taking/just-in-time-retrieval framework (§2, "Effective context engineering for AI agents"), an
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}drwxr-xr-x@ - warrenthompson 12 Sep 10:29 .claude drwxr-xr-x@ - warrenthompson 12 Sep 09:52 01-product-industry-research-design drwxr-xr-x@ - warrenthompson 12 Sep 10:35 02-ai-security-architecture drwxr-xr-x@ - warrenthompson 12 Sep 09:56 03-engineering-delivery drwxr-xr-x@ - warrenthompson 12 Sep 09:49 04-infra-devops drwxr-xr-x@ - warrenthompson 12 Sep 10:00 05-data-ops drwxr-xr-x@ - warrenthompson 12 Sep 09:28 profiles .rw-r--r--@ 4.8k warrenthompson 12 Sep 09:30 CLAUDE.md /Users/warrenthompson/Source/LoginID/01-product-industry-research-design/api-connector-design-rationale.md /Users/warrenthompson/Source/LoginID/01-product-industry-research-design/CLAUDE.md /Users/warrenthompson/Source/LoginID/01-product-industry-research-design/industry-framing.md /Users/warrenthompson/Source/LoginID/01-product-industry-research-design/personas-use-cases.md /Users/warrenthompson/Source/LoginID/01-product-industry-research-design/research-loginid-company.md /Users/warrenthompson/Source/LoginID/01-product-industry-research-design/research-oauth-history.md /Users/warrenthompson/Source/LoginID/02-ai-security-architecture/ai-workflow-narrative.md /Users/warrenthompson/Source/LoginID/02-ai-security-architecture/api-auth-design.md /Users/warrenthompson/Source/LoginID/02-ai-security-architecture/CLAUDE.md /Users/warrenthompson/Source/LoginID/02-ai-security-architecture/connector-security.md /Users/warrenthompson/Source/LoginID/02-ai-security-architecture/planning-approach.md /Users/warrenthompson/Source/LoginID/02-ai-security-architecture/research-claude-architecture-best-practices.md /Users/warrenthompson/Source/LoginID/02-ai-security-architecture/threat-model.md /Users/warrenthompson/Source/LoginID/03-engineering-delivery/CLAUDE.md /Users/warrenthompson/Source/LoginID/03-engineering-delivery/research-go-best-practices.md /Users/warrenthompson/Source/LoginID/04-infra-devops/CLAUDE.md /Users/warrenthompson/Source/LoginID/04-infra-devops/research-infra-devops-best-practices.md /Users/warrenthompson/Source/LoginID/05-data-ops/CLAUDE.md /Users/warrenthompson/Source/LoginID/05-data-ops/multi-db-strategy.md /Users/warrenthompson/Source/LoginID/05-data-ops/pii-governance.md /Users/warrenthompson/Source/LoginID/05-data-ops/research-data-ops-best-practices.md /Users/warrenthompson/Source/LoginID/CLAUDE.md
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}1 # Track: Product & Industry Research (incl. Design) 2 3 Refer back to `../CLAUDE.md` for the full assignment text and how this track fits with the other four. 4 5 ## Mission 6 7 Frame the three assignment questions as a real product problem, not just three isolated coding exercises. Show industry awareness of the space LoginID operates in — identity, authentication, and credential/PII management — and use that framing to justify the design choices the engineering track implements. 8 9 ## Owns 10 11 - Industry/competitive framing: where this kind of DAO + REST API + third-party IDP connector fits in the identity/IAM landscape (e.g., how it compares to what LoginID itself does with passkeys/FIDO2, and why a password-and-username-based `user_credential` model is the deliberately simpler baseline the assignment is asking for). 12 - Personas and use cases: who calls this API, why they need to search/retrieve user profile data, and why a service would need to pull PII from a third-party identity provider rather than owning that data itself. 13 - API and data contract design rationale from a product lens: what fields belong in `user_profile` vs `user_credential`, what "search/retrieve" needs to support, and why the `/auth` and `/identity` connector contracts are shaped the way LoginID specified them. 14 - Any UX/design artifacts for surfaces that would sit on top of this (e.g., an admin console for profile search, an onboarding flow that triggers the IDP connector) — light-touch, this is a backend assignment, so design work here should support the narrative rather than compete with it for attention. 15 16 ## Does not own 17 18 - Actual security threat modeling and authn/authz mechanics for the API — that's `../02-ai-security-architecture/CLAUDE.md`. 19 - The Go implementation — that's `../03-engineering-delivery/CLAUDE.md`. 20 - Database schema and multi-DB strategy — that's `../05-data-ops/CLAUDE.md`. 21 22 ## Deliverables 23 24 - A short industry/competitive framing document. 25 - Persona and use-case notes tied directly back to the three assignment questions. 26 - Design rationale for the API surface and the third-party connector contracts, written so the engineering track can implement against it without re-deriving the reasoning. 27 28 ## AI tooling note 29 30 Record which AI tool/workflow produced this track's output (this project is being built with Claude Code) and how — e.g., research done via a scoped subagent reading this file as its brief. 31 32 <!-- profile-gen:start slug=naomi-voss --> 33 @profiles/naomi-voss/naomi-voss.md 34 <!-- profile-gen:end slug=naomi-voss --> 35
{
"file_path": "/Users/warrenthompson/Source/LoginID/03-engineering-delivery/CLAUDE.md"
}1 # Track: Engineering & Delivery 2 3 Refer back to `../CLAUDE.md` for the full assignment text and how this track fits with the other four. 4 5 ## Mission 6 7 Write the actual code that answers all three assignment questions, in Go, implementing the designs the other tracks produce rather than re-deciding them here. Code does not need to be complete or working end-to-end — LoginID cares about the design and reasoning — but it should be structured and readable enough to demonstrate real engineering judgment. 8 9 ## Owns 10 11 - Question 1: the DAO layer for `user_profile` (name, address, phone) and `user_credential` (username, method, password), written to support multiple backing databases (PostgreSQL/CockroachDB, SQLite, etc.) behind a common interface, per the strategy in `../05-data-ops/CLAUDE.md`. 12 - Question 2: the RESTful API service to search/retrieve `user_profile` data, wired to the authentication mechanism designed in `../02-ai-security-architecture/CLAUDE.md`. 13 - Question 3: the third-party IDP connector service implementing `/auth` and `/identity` against the contracts specified in the assignment, following the connector security design in `../02-ai-security-architecture/CLAUDE.md`. 14 - Project structure, module layout, and inline documentation of design decisions and trade-offs made during implementation. 15 - Test strategy (unit/integration scope, what's stubbed vs. real) — tests don't need full coverage, but the approach should be stated. 16 17 ## Does not own 18 19 - Deciding the API auth mechanism or threat model — that's `../02-ai-security-architecture/CLAUDE.md`; implement what it specifies. 20 - Deciding the multi-database abstraction strategy or schema — that's `../05-data-ops/CLAUDE.md`; implement what it specifies. 21 - Deployment, containerization, CI/CD — that's `../04-infra-devops/CLAUDE.md`. 22 - Product/industry rationale for why the API is shaped this way — that's `../01-product-industry-research-design/CLAUDE.md`; reference it, don't re-derive it. 23 24 ## Deliverables 25 26 - Go source implementing questions 1, 2, and 3. 27 - A short README in this directory explaining what's implemented, what's stubbed/mocked, and why, plus which AI tool/workflow was used to produce the code and how. 28 - Any test files demonstrating the stated test strategy. 29 30 ## AI tooling note 31 32 Record which AI tool/workflow generated this track's code (this project is being built with Claude Code) and how much was AI-authored vs. human-reviewed/edited. 33 34 ## Status 35 36 No implementation has started. This track's CLAUDE.md is scaffolding only — do not begin writing code until the product, security, infra, and data-ops tracks have their designs in place to implement against. 37 38 <!-- profile-gen:start slug=renata-cole --> 39 @profiles/renata-cole/renata-cole.md 40 <!-- profile-gen:end slug=renata-cole --> 41
{
"file_path": "/Users/warrenthompson/Source/LoginID/04-infra-devops/CLAUDE.md"
}1 # Track: Infra & DevOps 2 3 Refer back to `../CLAUDE.md` for the full assignment text and how this track fits with the other four. 4 5 ## Mission 6 7 Design how the services from the engineering track would actually be built, packaged, deployed, and operated. This is a design/documentation track for a take-home — it does not stand up real infrastructure, but it should be concrete enough that it clearly could. 8 9 ## Owns 10 11 - Build and containerization approach for the DAO-backed API service and the IDP connector service (e.g., Dockerfiles, multi-stage Go builds). 12 - Environment/configuration strategy for switching the DAO between PostgreSQL/CockroachDB and SQLite (env vars, config files, or a database driver selection mechanism) — implements the storage-agnostic contract that `../05-data-ops/CLAUDE.md` designs. 13 - CI/CD pipeline design: what would run on a commit/PR (build, lint, test, security scan) for a project like this. 14 - Secrets injection mechanics at deploy time for whatever `../02-ai-security-architecture/CLAUDE.md` specifies needs to be a secret (DB credentials, third-party IDP credentials, signing keys) — this track implements the delivery mechanism, security architecture sets the requirement. 15 - Observability approach: logging, metrics, and tracing considerations for the API and the connector, especially around what must never be logged (raw passwords, tokens, PII) per the security track's guidance. 16 - Local dev-loop story: how a developer would run this stack locally (e.g., docker-compose with a local Postgres and the services). 17 18 ## Does not own 19 20 - Application code itself — that's `../03-engineering-delivery/CLAUDE.md`. 21 - What counts as a secret and why, or the auth/threat model — that's `../02-ai-security-architecture/CLAUDE.md`; this track only implements the delivery of it. 22 - Database schema/migration content — that's `../05-data-ops/CLAUDE.md`; this track only covers how migrations would run in a pipeline, not their content. 23 24 ## Deliverables 25 26 - Containerization design (Dockerfile sketch or description) for both services. 27 - A CI/CD pipeline description or config sketch. 28 - A local dev-loop description (e.g., docker-compose sketch). 29 - A short note on observability and what must be excluded from logs. 30 31 ## AI tooling note 32 33 Record which AI tool/workflow was used to produce this track's design output and how. 34 35 ## Status 36 37 No infra work has started. Do not begin until the engineering track's service boundaries are defined enough to containerize and deploy against. 38 39 <!-- profile-gen:start slug=theo-bergman --> 40 @profiles/theo-bergman/theo-bergman.md 41 <!-- profile-gen:end slug=theo-bergman --> 42
{
"file_path": "/Users/warrenthompson/Source/LoginID/05-data-ops/CLAUDE.md"
}1 # Track: Data Ops 2 3 Refer back to `../CLAUDE.md` for the full assignment text and how this track fits with the other four. 4 5 ## Mission 6 7 Own the data model and the multi-database strategy that question 1 requires, plus the data governance implications of storing `user_profile` PII and `user_credential` secrets. The engineering track implements this track's design; this track does not write the Go DAO code itself. 8 9 ## Owns 10 11 - Schema design for `user_profile` (name, address, phone) and `user_credential` (username, method, password), including how `method` is modeled (e.g., an enum for password vs. other auth methods) and how the two entities relate. 12 - The multi-database abstraction strategy: how one DAO interface serves PostgreSQL/CockroachDB and SQLite without diverging behavior — e.g., a repository interface with per-driver implementations, and which SQL dialect differences (upserts, autoincrement, JSON columns) need to be handled. 13 - Migration strategy: how schema changes would be versioned and applied across the supported databases. 14 - Data governance for PII: retention policy for `user_profile` data and for any cached data pulled from the third-party IDP connector's `/identity` endpoint, and how that interacts with credential storage (`user_credential` should never store PII, and the reverse). 15 - Indexing/query design to support "search and retrieve" from question 2 (e.g., what fields the API needs to search on, and what that implies for schema indexes). 16 17 ## Does not own 18 19 - Access control and transport security for that data — that's `../02-ai-security-architecture/CLAUDE.md`. 20 - Writing the DAO code itself — that's `../03-engineering-delivery/CLAUDE.md`; this track hands it a schema and an interface contract to implement. 21 - Where the databases actually run and how migrations are executed in a pipeline — that's `../04-infra-devops/CLAUDE.md`. 22 23 ## Deliverables 24 25 - Schema definitions (DDL or equivalent) for `user_profile` and `user_credential`, across the target databases. 26 - A written multi-database abstraction strategy the engineering track can implement directly. 27 - A migration approach. 28 - A short PII retention/governance note. 29 30 ## AI tooling note 31 32 Record which AI tool/workflow was used to produce this track's schema and strategy design and how. 33 34 ## Status 35 36 No data-ops work has started. This is the track the engineering DAO code most directly depends on — its schema and interface contract should land early. 37 38 <!-- profile-gen:start slug=priya-nandakumar --> 39 @profiles/priya-nandakumar/priya-nandakumar.md 40 <!-- profile-gen:end slug=priya-nandakumar --> 41
{
"command": "find /Users/warrenthompson/Source/LoginID -path \"*/profiles/*\" -name \"*.md\" -exec sh -c 'echo \"=== $1 ===\"; cat \"$1\"' _ {} \\;",
"description": "Print every existing persona profile markdown"
}=== /Users/warrenthompson/Source/LoginID/01-product-industry-research-design/profiles/naomi-voss/naomi-voss.md === --- schema_version: 1 name: "Naomi Voss" slug: "naomi-voss" image: "profiles/naomi-voss/naomi-voss.png" nsfw: false display: image: true name: true autostart: true generation: backend: "comfyui" model: "comfyui-workflow.json" prompt: "Naomi Voss, a sharp and curious woman in her late 30s with an analytical yet warm expression, wavy auburn hair worn loose, wearing a stylish soft-blue knit top with a simple pendant necklace, head-and-shoulders portrait, centered, neutral light gray studio background, professional corporate headshot photography, soft studio lighting, sharp focus, photorealistic" negative_prompt: blurry, low quality, distorted, extra limbs, watermark, text, nsfw, nude, nudity, explicit, sexual content seed: 3606086723 gif_mode: null created_at: "2026-09-12T00:00:00Z" --- # Naomi Voss  ## Personality Curious market/industry analyst with a design eye. Translates industry trends into crisp product rationale and always asks "who is this for" before "how do we build it." === /Users/warrenthompson/Source/LoginID/profiles/dana-whitfield/dana-whitfield.md === --- schema_version: 1 name: "Dana Whitfield" slug: "dana-whitfield" image: "profiles/dana-whitfield/dana-whitfield.png" nsfw: false display: image: true name: true autostart: true generation: backend: "comfyui" model: "comfyui-workflow.json" prompt: "Dana Whitfield, a composed and approachable woman in her mid-40s with an air of quiet authority, short neatly styled dark hair with subtle gray, wearing a tailored charcoal blazer over a simple blouse, confident warm expression with a slight knowing smile, head-and-shoulders portrait, centered, neutral gray studio background, professional corporate headshot photography, soft studio lighting, sharp focus, photorealistic" negative_prompt: blurry, low quality, distorted, extra limbs, watermark, text, nsfw, nude, nudity, explicit, sexual content seed: 4076804247 gif_mode: null created_at: "2026-09-12T00:00:00Z" --- # Dana Whitfield  ## Personality Decisive, low-drama engineering PM. Orchestrates parallel workstreams by giving each one a tight written brief and clear ownership boundaries, then gets out of the way. Values documented reasoning over verbal status updates, keeps scope from creeping, and reads as someone who's run enough AI-assisted projects to trust the process without narrating every step. === /Users/warrenthompson/Source/LoginID/05-data-ops/profiles/priya-nandakumar/priya-nandakumar.md === --- schema_version: 1 name: "Priya Nandakumar" slug: "priya-nandakumar" image: "profiles/priya-nandakumar/priya-nandakumar.png" nsfw: false display: image: true name: true autostart: true generation: backend: "comfyui" model: "comfyui-workflow.json" prompt: "Priya Nandakumar, a meticulous and composed woman in her mid-30s with long dark hair neatly tied back, thin-framed glasses, wearing a tailored deep-teal blazer over a plain top, precise and attentive expression with a slight warm smile, head-and-shoulders portrait, centered, neutral light gray studio background, professional corporate headshot photography, soft studio lighting, sharp focus, photorealistic" negative_prompt: blurry, low quality, distorted, extra limbs, watermark, text, nsfw, nude, nudity, explicit, sexual content seed: 1640813688 gif_mode: null created_at: "2026-09-12T00:00:00Z" --- # Priya Nandakumar  ## Personality Meticulous data modeler. Protects PII boundaries by instinct, thinks in schemas and migrations before code, and flags governance issues early rather than after the fact. === /Users/warrenthompson/Source/LoginID/03-engineering-delivery/profiles/renata-cole/renata-cole.md === --- schema_version: 1 name: "Renata Cole" slug: "renata-cole" image: "profiles/renata-cole/renata-cole.png" nsfw: false display: image: true name: true autostart: true generation: backend: "comfyui" model: "comfyui-workflow.json" prompt: "Renata Cole, a practical and focused woman in her early 30s with short dark bob haircut, minimal makeup, wearing a simple heather-gray crewneck sweater, straightforward no-nonsense expression with a hint of a smile, head-and-shoulders portrait, centered, neutral light gray studio background, professional corporate headshot photography, soft studio lighting, sharp focus, photorealistic" negative_prompt: blurry, low quality, distorted, extra limbs, watermark, text, nsfw, nude, nudity, explicit, sexual content seed: 1672670202 gif_mode: null created_at: "2026-09-12T00:00:00Z" --- # Renata Cole  ## Personality Pragmatic Go engineer. Favors clarity over cleverness, ships working slices over polished stubs, and documents trade-offs inline rather than in a separate doc nobody reads. === /Users/warrenthompson/Source/LoginID/02-ai-security-architecture/profiles/marcus-ilori/marcus-ilori.md === --- schema_version: 1 name: "Marcus Ilori" slug: "marcus-ilori" image: "profiles/marcus-ilori/marcus-ilori.png" nsfw: false display: image: true name: true autostart: true generation: backend: "comfyui" model: "comfyui-workflow.json" prompt: "Marcus Ilori, a composed and serious man in his early 40s with a closely trimmed beard and short black hair, sharp attentive eyes, wearing a dark navy blazer over a crisp white shirt with no tie, calm and rigorous expression, head-and-shoulders portrait, centered, neutral dark gray studio background, professional corporate headshot photography, soft studio lighting, sharp focus, photorealistic" negative_prompt: blurry, low quality, distorted, extra limbs, watermark, text, nsfw, nude, nudity, explicit, sexual content seed: 3422633147 gif_mode: null created_at: "2026-09-12T00:00:00Z" --- # Marcus Ilori  ## Personality Rigorous, threat-model-first thinker. Stays calm under scrutiny, insists on naming assumptions and citing sources, and treats security design as a first-class deliverable, not an afterthought. === /Users/warrenthompson/Source/LoginID/04-infra-devops/profiles/theo-bergman/theo-bergman.md === --- schema_version: 1 name: "Theo Bergman" slug: "theo-bergman" image: "profiles/theo-bergman/theo-bergman.png" nsfw: false display: image: true name: true autostart: true generation: backend: "comfyui" model: "comfyui-workflow.json" prompt: "Theo Bergman, a lean and observant man in his late 40s with short graying hair and light stubble, wire-rimmed glasses, wearing a plain dark olive quarter-zip pullover, deadpan steady expression, head-and-shoulders portrait, centered, neutral gray studio background, professional corporate headshot photography, soft studio lighting, sharp focus, photorealistic" negative_prompt: blurry, low quality, distorted, extra limbs, watermark, text, nsfw, nude, nudity, explicit, sexual content seed: 833382700 gif_mode: null created_at: "2026-09-12T00:00:00Z" --- # Theo Bergman  ## Personality Deadpan systems reliability engineer. Sizes infrastructure to the actual problem instead of the resume-driven version, is allergic to over-engineering, and thinks in failure modes.
{
"command": "ls -la /Users/warrenthompson/Source/LoginID/.claude /Users/warrenthompson/Source/LoginID/.claude/* 2>/dev/null; find ~/.claude/skills /Users/warrenthompson/Source/LoginID/.claude -maxdepth 3 -iname \"*profile-gen*\" 2>/dev/null",
"description": "Inspect project .claude dir and locate the profile-gen skill"
}.rw-r--r--@ 130 warrenthompson 12 Sep 10:29 /Users/warrenthompson/Source/LoginID/.claude/scheduled_tasks.lock .rw-r--r--@ 98 warrenthompson 12 Sep 10:02 /Users/warrenthompson/Source/LoginID/.claude/settings.local.json /Users/warrenthompson/Source/LoginID/.claude: .rw-r--r--@ 130 warrenthompson 12 Sep 10:29 scheduled_tasks.lock .rw-r--r--@ 98 warrenthompson 12 Sep 10:02 settings.local.json /Users/warrenthompson/.claude/skills/profile-gen
{
"command": "ls -la /Users/warrenthompson/.claude/skills/profile-gen/; sed -n '1,200p' /Users/warrenthompson/.claude/skills/profile-gen/SKILL.md",
"description": "Read the profile-gen skill definition"
}drwxr-xr-x@ - warrenthompson 11 Sep 23:59 .claude
drwxr-xr-x@ - warrenthompson 12 Sep 00:44 .git
drwxr-xr-x@ - warrenthompson 10 Sep 16:50 .profiles-assets
drwxr-xr-x@ - warrenthompson 11 Sep 20:29 .pytest_cache
drwxr-xr-x@ - warrenthompson 10 Sep 13:44 .venv
drwxr-xr-x@ - warrenthompson 11 Sep 23:31 assets
drwxr-xr-x@ - warrenthompson 10 Sep 16:50 profiles
drwxr-xr-x@ - warrenthompson 12 Sep 00:44 references
drwxr-xr-x@ - warrenthompson 12 Sep 00:43 scripts
drwxr-xr-x@ - warrenthompson 11 Sep 23:31 templates
drwxr-xr-x@ - warrenthompson 12 Sep 10:04 tests
.rw-r--r--@ 88 warrenthompson 11 Sep 22:53 .gitignore
.rw-r--r--@ 7.2k warrenthompson 11 Sep 23:31 CLAUDE.md
.rw-r--r--@ 8.5k warrenthompson 11 Sep 22:53 README.md
.rw-r--r--@ 28 warrenthompson 10 Sep 13:44 requirements.txt
.rw-r--r--@ 20k warrenthompson 12 Sep 00:33 SKILL.md
---
name: profile-gen
description: Generate a persona for an AI agent — a profile image (with optional animated GIF), a human-like name, an optional voice reference, and an optional personality description — and record it as a standalone markdown profile with a one-line auto-discoverable reference kept in CLAUDE.md (or, optionally, fully inlined into CLAUDE.md). Private/NSFW personas can be kept entirely out of git while still being auto-loaded via that reference. Supports ChatGPT/Grok (with an API key), the Grok CLI (no API key — uses a Grok/X subscription via `grok login`), or a self-hosted ComfyUI server (no account needed — the skill can author a workflow for you, LoRA picks included, if you don't already have one).
argument-hint: "[description hints] [--backend chatgpt|grok|grok-cli|comfyui] [--nsfw] [--gif] [--voice NAME] [--output claude-md-ref|claude-md|file] [--assets tracked|gitignored] [--name NAME] [--no-image] [--no-name]"
allowed-tools: Bash, Read, Write, Edit, AskUserQuestion, Agent
---
# profile-gen
Generate a persona (name, profile image, optional animated GIF, optional voice reference,
optional personality) for an AI agent, and record it as a markdown profile.
All scripts live under `scripts/` (relative to this skill's install directory) and each prints
one JSON object to stdout — parse that JSON rather than scraping prose output.
## 0. Resolve directories — read this before running anything
Two directories matter here and **they are almost always different**:
- **`SKILL_DIR`** — the directory this SKILL.md file itself lives in (its install location,
typically `~/.claude/skills/profile-gen` or `<project>/.claude/skills/profile-gen`, often a
symlink). Every `scripts/...`/`references/...` path in this document is relative to
**`SKILL_DIR`**, not the user's project — resolve them there (e.g.
`python3 <SKILL_DIR>/scripts/check_config.py ...`).
- **`PROJECT_ROOT`** — the user's actual project: the working directory of this session, where
the persona should actually be written. Determine it once at the start (it's simply the
directory you're already operating in for this conversation) and pass it as an **absolute
path** via `--root <PROJECT_ROOT>` to every `write_profile.py` call below. Never omit `--root`
or leave it to a default — doing so is exactly what previously caused personas to be written
inside the skill's own installation directory instead of the user's project.
## 1. Parse arguments
Parse whatever the user typed after `/profile-gen` for: free-text description hints, `--backend
chatgpt|grok|grok-cli|comfyui`, `--nsfw`, `--gif`, `--voice NAME`,
`--output claude-md-ref|claude-md|file`, `--assets tracked|gitignored`, `--name NAME`,
`--no-image`, `--no-name`. Anything not recognized as a flag is a description hint.
`--no-image`/`--no-name` control this persona's stored `display.image`/`display.name`
preference (both default `true` — the picture shows and the name prints whenever
`scripts/show_profile.py` runs for it; see `references/terminal-display.md`). These don't need
their own `AskUserQuestion` round — assume both `true` unless the flag was given.
## 2. Gather missing inputs (one question round)
If `--backend`, `--nsfw`/`--gif` intent, `--voice`, `--output`, or `--assets` were not given on
the command line, ask for all of them in a **single** `AskUserQuestion` call (don't ask one at a
time):
- **Backend**: chatgpt / grok (both need an API key — a ChatGPT Plus or X/Grok *subscription*
alone does not grant one; these are billed separately from the API) / grok-cli (no API key —
uses the locally-installed `grok` CLI's `/imagine` command against the user's Grok/X
subscription instead; requires `grok` on PATH and already logged in via `grok login`; costs
real usage per call and has no seed/size control — see `references/backends.md`) / comfyui (no
account or API key needed at all — point it at your own ComfyUI server; if you don't have a
workflow set up yet, this skill can build one for you, LoRA picks included — see step 4a).
- **NSFW**: on/off. Purely a pass-through flag (see `references/prompting.md`) — no client-side
filtering happens in this skill regardless of the answer.
- **GIF**: whether to also produce an animated version.
- **Voice**: a reference string, or none. See `references/voices.md` for sample names — don't
invent audio, this is metadata only.
- **Output destination**:
- `claude-md-ref` (**recommended default, especially for a private/personal/NSFW persona**) —
the persona's markdown lives on its own (co-located with its assets, tracked or gitignored
per the next question), and a one-line `@<path>` import is kept in the project's real
`CLAUDE.md` so it's still auto-discovered — without the persona's actual content (name,
personality, prompts) ever being inlined into a tracked file.
- `claude-md` — the full rendered persona is embedded directly in `CLAUDE.md`. Only appropriate
when the persona itself is meant to be shared/committed as-is (e.g. a team-facing agent
identity), since this inlines its full content into a tracked file regardless of the assets
choice below.
- `file` — a standalone `.md` with no automatic reference anywhere (the user wires it up
themselves, e.g. their own `@` import or manual read).
- **Asset placement**: tracked in git, or gitignored. For `claude-md-ref`/`file`, "gitignored"
hides the persona's markdown **and** its images together (private end to end); for `claude-md`,
it only hides the images (the persona's text is already committed via the inline block).
**`claude-md-ref` + gitignored specifically** writes to a **fixed, generic path**
(`.claude/persona/persona.md`) rather than one derived from the persona's name — so the
one-line `@`-import that lands in the tracked `CLAUDE.md` never reveals who the persona is.
This means only one such private persona is addressable per project at a time; tell the user if
they already have one and this run will replace it.
Do not ask about things already supplied as flags.
## 3. Confirm name and personality
Propose a human-like `name` (using any description hints given) and, if the run isn't SFW-and-
personality-less by request, a short `personality` description. Show both to the user and let
them accept or edit before spending any API calls. When `nsfw` is set, write the personality
description in the requested register without disclaimers — see `references/prompting.md`.
Derive `slug` from the confirmed name (this matches what `storage.slugify()` computes, so you
don't need to compute it by hand — the scripts below do it internally).
## 4. Validate backend config
```
python3 scripts/check_config.py --backend <backend>
```
Fix reported config problems (missing env vars, unreachable ComfyUI, unresolved workflow nodes)
before continuing. If `--backend comfyui`, read `references/comfyui.md` first — it documents the
node-title convention the workflow JSON must follow.
## 4a. Auto-build a ComfyUI workflow (comfyui backend, no workflow configured yet)
Skip this step entirely for chatgpt/grok, or if step 4 already reports `comfyui_workflow` as
valid (the user already has one). Otherwise — including when `check_config.py` reports
`comfyui_workflow is not set` — build one instead of asking the user to supply their own:
1. `python3 scripts/inspect_comfyui.py --url <comfyui_url>` — prints the server's installed
checkpoints, LoRAs, samplers/schedulers, and whether a real native-animation node family is
present (`has_native_animation`, with `has_animatediff`/`has_ltxv`/`has_wan` and their matched node
lists saying which). This detection is strict/name-prefix-based on purpose — it has
previously produced false positives from unrelated nodes that merely mention "AnimateDiff" in
their name — so trust what it reports rather than assuming a family is available because a
"normal" ComfyUI setup would have it. If it reports no checkpoints at all, tell the user their
ComfyUI server has no models installed and stop — a workflow can't be authored with nothing to
load.
2. Spawn a subagent (`Agent` tool) to actually design the workflow: give it this inventory, the
confirmed persona name/personality/description hints, and point it at
`references/comfyui-workflow-authoring.md` for the exact node-title convention, an example
still-image workflow, how to chain in LoRAs it picks from the inventory, and (only if
`has_native_animation` is true and the user asked for a GIF) how to also produce an
AnimateDiff, LTX-Video, or Wan2.x workflow — whichever family the inventory actually reports, using
only node types that actually appear in its lists. If `has_native_animation` is false, tell
the subagent to skip the GIF workflow entirely; don't have it attempt one on the hope that a
real-sounding node name will work. Have it write the file(s) to
`<asset_dir>/comfyui-workflow.json` (and `<asset_dir>/comfyui-workflow-gif.json` if
applicable) — `asset_dir` comes from step 5, so run that plan-paths call first if you haven't
yet. Ask it to report back which checkpoint/LoRAs it used and why, and which animation family
(if any) it targeted.
3. Validate what it wrote:
```
python3 scripts/check_config.py --backend comfyui --workflow <asset_dir>/comfyui-workflow.json \
[--gif-workflow <asset_dir>/comfyui-workflow-gif.json] [--gif]
```
This checks two independent things — both must pass before you proceed: (a) `resolved` shows
no `null` node (unresolved positive/negative/latent/output — most commonly a missing
`_meta.title`), and (b) no `errors` entry about a node type not installed on the server (the
subagent referenced something that isn't actually there). Send either failure back to the
subagent to fix and re-validate. Don't proceed to image/GIF generation with an unresolved or
unverified workflow.
4. Use `--workflow <asset_dir>/comfyui-workflow.json` (and `--gif-workflow
<asset_dir>/comfyui-workflow-gif.json`, if built) on the `generate_image.py`/`make_gif.py`
calls in steps 7-8 below, instead of relying on `COMFYUI_WORKFLOW`/`COMFYUI_GIF_WORKFLOW` env
vars. Tell the user which checkpoint/LoRAs were picked and that the workflow file was saved
alongside their profile assets (so it's reusable/editable afterward).
## 5. Plan output paths
```
python3 scripts/write_profile.py --plan-only --name "<name>" --root <PROJECT_ROOT> \
--output <output> --assets <assets>
```
Prints `{"markdown_path": ..., "asset_dir": ...}`. Use `asset_dir` as the destination directory
for the image (and GIF, if any) in the next steps — the image scripts write directly there.
## 6. Draft the image prompt
Read `references/prompting.md` for the subject/style recipe. Write **only the subject/style
description** (name/appearance/setting/style, from the confirmed name/personality/description
hints) to a temp file — and a plain quality-only negative prompt to a second temp file if you
have specific things to avoid beyond the defaults, for ComfyUI. **Do not append an NSFW clause
yourself, for any backend.** `generate_image.py`/`make_gif.py` do that automatically via
`profilegen.prompt.build()` based on `--nsfw` and `--backend` — and the phrasing that actually
works differs by backend in a way that matters (see `references/prompting.md`): blunt "no
restrictions" language helps on local ComfyUI but reliably triggers refusals on hosted backends
(Grok, Grok CLI, ChatGPT), which need tasteful/suggestive framing instead. Hand-typing a clause
yourself risks using the wrong one, or duplicating what the script already adds.
## 7. Generate the image
Decide the image's path yourself as `<asset_dir>/<slug>.png` (using the `asset_dir` from step 5
and the `slug` from step 3), then:
```
python3 scripts/generate_image.py --backend <backend> --prompt-file <prompt.txt> \
[--negative-file <negative.txt>] [--nsfw] --out <asset_dir>/<slug>.png [--seed N] \
[--style-preset "<short style clause>"] [--workflow <asset_dir>/comfyui-workflow.json]
```
The printed JSON includes `positive_prompt`/`negative_prompt` — the actual final prompt text
sent to the backend (NSFW clause and all). Use these verbatim for `generation.prompt`/
`generation.negative_prompt` in the profile fields in step 9, not whatever you originally
drafted, so the recorded provenance matches what was really sent.
Only pass `--workflow` when step 4a built one; otherwise the backend falls back to
`COMFYUI_WORKFLOW`/config (irrelevant for chatgpt/grok).
Prints `{"path", "backend", "model", "seed", "width", "height"}` as JSON. Hold onto the returned
`path` — it's the still image, used as input to step 8, and becomes the profile's `image` field
in step 9 **only if no GIF ends up being generated**. When Pillow is installed (the normal case),
the saved image is always center-cropped to square regardless of what the backend returned —
this matters because some backends (Grok in particular) ignore requested width/height entirely
and can return any aspect ratio, and a non-square image forced into a circular/square avatar
elsewhere is exactly what a "stretched profile picture" looks like. The printed `width`/`height`
are always the real, final dimensions of what was actually saved, not a backend's requested-but-
unverified size. Without Pillow installed, no cropping/format-conversion happens (the actual file{
"command": "grep -n \"^#\\|^\\*\\*Decision\\|^- \\*\\*\" 02-ai-security-architecture/api-auth-design.md 02-ai-security-architecture/connector-security.md 05-data-ops/multi-db-strategy.md 05-data-ops/pii-governance.md 01-product-industry-research-design/api-connector-design-rationale.md | head -120",
"description": "Skim section headers of the key design deliverables"
}02-ai-security-architecture/api-auth-design.md:1:# API Authentication & Authorization Design — Question 2 02-ai-security-architecture/api-auth-design.md:5:## What "security API authentication" is scoped to mean here 02-ai-security-architecture/api-auth-design.md:17:**Decision: OAuth2 client-credentials grant issuing short-lived JWT bearer access tokens, with mTLS as an optional additional transport control for known service-to-service callers.** 02-ai-security-architecture/api-auth-design.md:21:## Token shape and lifecycle 02-ai-security-architecture/api-auth-design.md:23:- **Format**: JWT, signed (not encrypted — it carries no secret, only claims), asymmetric signing (RS256/ES256) so the API can verify tokens without holding the signing key, which itself lives with a token-issuing authorization server component. 02-ai-security-architecture/api-auth-design.md:24:- **Claims, minimum set**: `sub` (calling client/service identity), `scope` (space-delimited authorization scopes, see below), `exp` (short — 5–15 minutes), `iat`, `aud` (this API specifically, to prevent a token minted for another service being replayed here). 02-ai-security-architecture/api-auth-design.md:25:- **No refresh tokens** for a client-credentials grant — the client re-authenticates with its own client secret when the access token expires. This bounds the blast radius of a leaked access token to its short TTL. 02-ai-security-architecture/api-auth-design.md:26:- **Revocation**: because JWTs are self-contained, true revocation requires either a short enough TTL that revocation is unnecessary in practice (the approach here) or a token-denylist check, which reintroduces a stateful lookup and defeats part of the point of JWTs. Given the 5–15 minute TTL, denylist is not required for this design; flagged here so a reviewer sees the trade-off was considered, not missed. 02-ai-security-architecture/api-auth-design.md:28:## Authorization model: scopes, not just "authenticated" 02-ai-security-architecture/api-auth-design.md:32:- **Scopes**, carried in the JWT `scope` claim, e.g. `profile:read:own`, `profile:read:any`, `profile:search`. A given client-credential is provisioned with the narrowest scope its actual use case requires — most integrations should get `profile:read:own` (retrieve a specific known record by id, e.g. resolving a record the caller already has a legitimate reference to) rather than `profile:search` (query by name/phone fragments across the table), which should be reserved for a small number of explicitly-provisioned back-office callers. 02-ai-security-architecture/api-auth-design.md:33:- **Object-level authorization is enforced per request, not just per token.** Holding `profile:read:any` scope is necessary but not sufficient — every retrieve/search call is logged (see below) and, where the caller model supports it, additionally checked against a resource-level policy (e.g., a partner integration scoped to only its own referred users) rather than trusting the coarse scope alone. This directly targets OWASP API1:2023 (Broken Object Level Authorization), cited in the threat model. 02-ai-security-architecture/api-auth-design.md:34:- **Search results are paginated with a hard page-size ceiling** and rate-limited per client, independent of the scope check — a legitimately-scoped caller doing an anomalously large number of sequential searches is still a signal worth throttling and alerting on, not just permitting because the token was valid. 02-ai-security-architecture/api-auth-design.md:36:## Defense in depth beyond the token check 02-ai-security-architecture/api-auth-design.md:38:- **TLS 1.2+ only**, terminated in front of the API; no plaintext. 02-ai-security-architecture/api-auth-design.md:39:- **Input validation** on search parameters — reject unbounded wildcard queries, cap query complexity, parameterize all DB access (SQL injection is a data-ops/engineering implementation concern, but the API layer's job is to never pass a raw client string into a query string). 02-ai-security-architecture/api-auth-design.md:40:- **Audit logging**: every search/retrieve call logs caller identity (`sub`), scope used, record id(s) touched, and timestamp — never the PII field values themselves, per the threat model's log-hygiene requirement. This is what makes a compromised-but-legitimate credential's misuse detectable after the fact, which token-based authn alone does not give you. 02-ai-security-architecture/api-auth-design.md:41:- **Standard API hardening**: no PII in URLs (query strings get logged by proxies/CDNs outside our control) — record identifiers in the path are fine, but search terms containing a phone number or name fragment belong in a POST body or a properly-redacted query parameter set, not a GET query string that ends up in a shared access log. 02-ai-security-architecture/api-auth-design.md:43:## Why not "just API keys" 02-ai-security-architecture/api-auth-design.md:47:## Sources 05-data-ops/pii-governance.md:1:# PII Data Governance Note 05-data-ops/pii-governance.md:5:## Data minimization 05-data-ops/pii-governance.md:9:## Retention 05-data-ops/pii-governance.md:13:- **`source = 'direct'`** — profile created through this system's own registration flow. Retention window is a product/legal decision this track doesn't own outright, but the schema supports enforcing whatever window is chosen: `created_at`/`updated_at` plus a scheduled deletion job keyed on those columns. 05-data-ops/pii-governance.md:14:- **`source = 'idp_cache'`** — profile hydrated from a third-party IDP connector's `/identity` response. This data is subject to the **same retention obligation as directly-collected PII** — caching it does not reset or relax that obligation. If anything it adds a vendor-processor relationship on top: this system is now downstream custodian of another party's collected PII, which should be documented as a data-flow (third-party collection → this system → disposal) rather than left implicit. 05-data-ops/pii-governance.md:18:## What must never happen, structurally enforced 05-data-ops/pii-governance.md:20:- **`user_credential` never carries PII.** No name, phone, or address column exists on that table, and the `UserCredential` type described in `multi-db-strategy.md` has no fields that could hold it. A credential-store breach exposes usernames and hashes, not addresses. 05-data-ops/pii-governance.md:21:- **`user_profile` never carries a secret.** No password hash or raw secret column exists on that table. A profile-store breach exposes PII, not authentication material. 05-data-ops/pii-governance.md:22:- **The third-party IDP's own `/auth` credentials (the `username`/`password` this system POSTs to the vendor's `/auth` endpoint) are never persisted in `user_credential` or anywhere else in this schema.** That table is for this system's own login credentials. Storing a vendor's auth secret here would mean this system is custodian of a third party's credential material with no assignment requirement to do so — a clear scope and governance violation, not just a modeling mistake. 05-data-ops/pii-governance.md:26:## Handoff notes 05-data-ops/multi-db-strategy.md:1:# Multi-Database Abstraction Strategy 05-data-ops/multi-db-strategy.md:5:## The interface contract (prose, not code) 05-data-ops/multi-db-strategy.md:9:- **`ProfileRepository`** — `Get(ctx, id) (*UserProfile, error)`; `Search(ctx, query) (results []UserProfile, total int, error)` where `query` filters by name (partial/fuzzy), phone (exact/prefix), region, country, plus limit/offset for pagination; `Create(ctx, *UserProfile) (*UserProfile, error)`; `Update(ctx, *UserProfile) (*UserProfile, error)` implemented as a portable upsert; `Delete(ctx, id) error`. 05-data-ops/multi-db-strategy.md:10:- **`CredentialRepository`** — `GetByUsername(ctx, username) (*UserCredential, error)` as the primary auth lookup path; `ListByUserID(ctx, userID) ([]UserCredential, error)`; `Create`, `Update`, `Delete` mirroring `ProfileRepository`'s shape. Never accepts or returns `UserProfile` fields. 05-data-ops/multi-db-strategy.md:11:- **`AuthMethodRepository`** — `Get(ctx, id)`, `GetByName(ctx, name)`, `List(ctx, activeOnly bool)`. Read-heavy; writes to this table are an operational action (adding a supported method), not part of the normal request path. 05-data-ops/multi-db-strategy.md:22:## Why not one implementation with dialect branches 05-data-ops/multi-db-strategy.md:26:## Dialect differences that survive into the DAO layer 05-data-ops/multi-db-strategy.md:37:## Schema shape (prose, not DDL) 05-data-ops/multi-db-strategy.md:49:## Migration approach 05-data-ops/multi-db-strategy.md:56:## What the engineering track should take from this 02-ai-security-architecture/connector-security.md:1:# IDP Connector Security Design — Question 3 02-ai-security-architecture/connector-security.md:5:## Contract recap (from the assignment, verbatim in `../CLAUDE.md`) 02-ai-security-architecture/connector-security.md:12:## Threat surface specific to this connector 02-ai-security-architecture/connector-security.md:16:### 1. What happens to the vendor `access_token` once retrieved 02-ai-security-architecture/connector-security.md:18:- **Never logged.** The `/auth` request body contains a vendor password; the response contains a bearer token. A naive "log the request and response for debugging" habit puts both in the same log line — this is called out explicitly because it's the single most common way a connector like this leaks a credential in practice, not a hypothetical. 02-ai-security-architecture/connector-security.md:19:- **Held in memory for the shortest span that satisfies the immediate `/identity` call**, not persisted to our own database as a matter of course. If caching is required for latency/rate-limit reasons, cache with a TTL no longer than the vendor's own token expiry, encrypted at rest (same envelope-encryption requirement as the TOTP-secret case in the threat model — mechanics via KMS, owned by `../04-infra-devops/CLAUDE.md`), and keyed so one vendor-account's token is never returned to a caller resolving a different vendor account. 02-ai-security-architecture/connector-security.md:20:- **Scoped narrowly if the vendor supports it** — request only the scope needed for `/identity`, not a broad token, so a leaked token's blast radius is bounded by the vendor's own authorization model as well as ours. 02-ai-security-architecture/connector-security.md:21:- **Not returned to our own API callers.** The vendor `access_token` is an internal implementation detail of this connector; question 2's API callers never see it. If a caller needs to know "identity lookup succeeded," return our own success/failure signal, not the vendor's token or vendor's raw response. 02-ai-security-architecture/connector-security.md:23:### 2. How `/identity` calls are authenticated to the vendor 02-ai-security-architecture/connector-security.md:26:- **Token refresh/re-auth on expiry** handled by the connector transparently — a caller of our `/identity` endpoint should not need to know or manage vendor token lifecycle. 02-ai-security-architecture/connector-security.md:27:- **Vendor calls over TLS only**, certificate validation never disabled, including in local/dev environments (repeating the threat model's point because it's the most common place a "temporary" dev bypass ships to production unnoticed). 02-ai-security-architecture/connector-security.md:28:- **Our own credentials to call the vendor's `/auth`** (this connector's client id/secret with ABC/XYZ, if the vendor requires connector-level registration in addition to the end-user's own username/password) are runtime-injected secrets, never in source or config committed to the repo — requirement set here, mechanism owned by `../04-infra-devops/CLAUDE.md`. 02-ai-security-architecture/connector-security.md:30:### 3. What this service should and shouldn't log or persist 02-ai-security-architecture/connector-security.md:40:### 4. Failure handling as a security control, not just reliability 02-ai-security-architecture/connector-security.md:42:- **Vendor auth failures must not leak which part of the credential was wrong** (username vs. password) in whatever this connector surfaces upward — same user-enumeration concern as the threat model's credential-stuffing row, now facing outward at whoever calls our `/auth` passthrough. 02-ai-security-architecture/connector-security.md:43:- **Retry/backoff on vendor calls** should be rate-limited on our side independent of the vendor's own limits, so a compromised or careless caller can't use our connector to hammer the vendor (which is both an availability risk to us and a way to get our connector's vendor credentials rate-limited or banned). 02-ai-security-architecture/connector-security.md:44:- **Circuit-breaking**: if the vendor is failing or behaving anomalously (e.g., returning tokens for identities that don't match the request), fail closed — do not fall back to a cached or stale identity result silently, since that risks serving stale PII as if freshly verified. 02-ai-security-architecture/connector-security.md:46:## What this design does not resolve 02-ai-security-architecture/connector-security.md:52:## Sources 01-product-industry-research-design/api-connector-design-rationale.md:1:# API & Connector Design Rationale 01-product-industry-research-design/api-connector-design-rationale.md:9:## 1. Data contract: `user_profile` and `user_credential` 01-product-industry-research-design/api-connector-design-rationale.md:17:## 2. REST API: search and retrieve 01-product-industry-research-design/api-connector-design-rationale.md:32:## 3. Third-party IDP connector: `/auth` and `/identity` 01-product-industry-research-design/api-connector-design-rationale.md:46:## Summary for the engineering track
{
"command": "grep -n -i \"skip\\|no-image\\|personality\\|reuse\\|seed\\|--gif\" /Users/warrenthompson/.claude/skills/profile-gen/SKILL.md | sed -n '1,60p'; echo ---; grep -n -i \"personality\" /Users/warrenthompson/.claude/skills/profile-gen/templates/* /Users/warrenthompson/.claude/skills/profile-gen/references/prompting.md 2>/dev/null | head -20",
"description": "Check profile-gen for image-skip options and personality field handling"
}3:description: Generate a persona for an AI agent — a profile image (with optional animated GIF), a human-like name, an optional voice reference, and an optional personality description — and record it as a standalone markdown profile with a one-line auto-discoverable reference kept in CLAUDE.md (or, optionally, fully inlined into CLAUDE.md). Private/NSFW personas can be kept entirely out of git while still being auto-loaded via that reference. Supports ChatGPT/Grok (with an API key), the Grok CLI (no API key — uses a Grok/X subscription via `grok login`), or a self-hosted ComfyUI server (no account needed — the skill can author a workflow for you, LoRA picks included, if you don't already have one).
4:argument-hint: "[description hints] [--backend chatgpt|grok|grok-cli|comfyui] [--nsfw] [--gif] [--voice NAME] [--output claude-md-ref|claude-md|file] [--assets tracked|gitignored] [--name NAME] [--no-image] [--no-name]"
11:optional personality) for an AI agent, and record it as a markdown profile.
35:chatgpt|grok|grok-cli|comfyui`, `--nsfw`, `--gif`, `--voice NAME`,
37:`--no-image`, `--no-name`. Anything not recognized as a flag is a description hint.
39:`--no-image`/`--no-name` control this persona's stored `display.image`/`display.name`
46:If `--backend`, `--nsfw`/`--gif` intent, `--voice`, `--output`, or `--assets` were not given on
54: real usage per call and has no seed/size control — see `references/backends.md`) / comfyui (no
67: personality, prompts) ever being inlined into a tracked file.
85:## 3. Confirm name and personality
88:personality-less by request, a short `personality` description. Show both to the user and let
89:them accept or edit before spending any API calls. When `nsfw` is set, write the personality
107:Skip this step entirely for chatgpt/grok, or if step 4 already reports `comfyui_workflow` as
121: confirmed persona name/personality/description hints, and point it at
127: the subagent to skip the GIF workflow entirely; don't have it attempt one on the hope that a
136: [--gif-workflow <asset_dir>/comfyui-workflow-gif.json] [--gif]
144:4. Use `--workflow <asset_dir>/comfyui-workflow.json` (and `--gif-workflow
163:description** (name/appearance/setting/style, from the confirmed name/personality/description
180: [--negative-file <negative.txt>] [--nsfw] --out <asset_dir>/<slug>.png [--seed N] \
192:Prints `{"path", "backend", "model", "seed", "width", "height"}` as JSON. Hold onto the returned
211: [--nsfw] [--seed N] [--gif-workflow <asset_dir>/comfyui-workflow-gif.json]
236:subject). Only fall back to the step 7 PNG path as `image` if this step was skipped entirely.
241:display, generation, plus voice/personality if set) to a temp file. Set `image` to the GIF path
244:if step 8 was skipped. Set `display.image`/`display.name` from step 1's `--no-image`/`--no-name`
278:reference file — it makes Claude actually introduce itself (name, personality, a line on recent
291:preview actually drew an image (vs. silently skipping for lack of protocol support) and whether
---
/Users/warrenthompson/.claude/skills/profile-gen/templates/profile.embedded.md.j2:29:{% if personality %}
/Users/warrenthompson/.claude/skills/profile-gen/templates/profile.embedded.md.j2:30:**Personality:** {{ personality }}
/Users/warrenthompson/.claude/skills/profile-gen/templates/profile.standalone.md.j2:28:{% if personality %}
/Users/warrenthompson/.claude/skills/profile-gen/templates/profile.standalone.md.j2:29:## Personality
/Users/warrenthompson/.claude/skills/profile-gen/templates/profile.standalone.md.j2:31:{{ personality }}
/Users/warrenthompson/.claude/skills/profile-gen/references/prompting.md:4:personality description in step 3 when `nsfw` is set.
/Users/warrenthompson/.claude/skills/profile-gen/references/prompting.md:11: description hints (or invented if none were given, consistent with the personality).
/Users/warrenthompson/.claude/skills/profile-gen/references/prompting.md:75:## Writing the personality description when NSFW is set
/Users/warrenthompson/.claude/skills/profile-gen/references/prompting.md:77:Write the personality description directly, in the register the user asked for. Do not add