CLAUDE.md

Project

Japanese Learning Avatar

An animated 3D avatar tutor that teaches Japanese through spoken conversation, structured lessons, and immersion role-play — hosted as a Hugging Face Space. A VRM anime-style character listens to the learner speak Japanese, responds aloud with lip-synced speech and expressions, drills vocab and grammar, and adapts to the learner's level (N5 through N2). Built both as a daily-use learning tool for the author and as the flagship AI/ML engineering showcase on the WolfDavid Hugging Face profile.

Core Value: A learner can hold a real, level-appropriate spoken Japanese conversation with an animated avatar that talks back — and measurably improve over time because the avatar remembers them.

Constraints

• Platform: Hugging Face Space (Gradio) — portfolio strategy requires it; free-tier visitors must get a working experience
• Cost: Free baseline for visitors — small open model default; frontier quality only via visitor's own API key (BYOK)
• Tech: In-browser avatar rendering (three.js/VRM) — no server-side video generation
• Quality bar: Flagship portfolio piece — "would a Google AI/ML hiring manager be impressed?" drives polish decisions

Technology Stack

⚠️ Read This First: Two Findings That Reshape the Project

1. "Free CPU tier" is no longer the free path for a Gradio Space

2. ZeroGPU quota is consumed by the visitor, not the owner

Visitor account type	Included daily GPU quota	Queue priority
Unauthenticated	2 minutes/day	Low
Free HF account	5 minutes/day	Medium
PRO account	40 minutes/day (extensible)	Highest

Recommended Stack

Core Technologies

Technology	Version	Purpose	Why Recommended
Gradio	6.22.0 (pin exactly)	App framework + Space SDK	Gradio 6 is the only maintained major line; the team has stated only v6 gets future releases. Crucially, ZeroGPU is exclusively compatible with the Gradio SDK — no Docker, no Static. Gradio 6 also ships the gr.HTML templating system (below) that makes the three.js avatar viable without a build step.
Python	3.12.12 (declare in README)	Runtime	ZeroGPU provides only Python 3.10.13 and 3.12.12. Pick 3.12.12. Do not use 3.13 — this is the recurring "Python 3.13 / Gradio pin" gotcha from prior Spaces work, and here it's a hard platform limit, not a soft one (spaces requires >=3.10,<3.15, but ZeroGPU only builds those two images).
spaces	0.51.1	ZeroGPU allocation	Provides @spaces.GPU(duration=...). Effect-free off-ZeroGPU, so local dev is unaffected. Use dynamic durations (duration=callable) — shorter declared durations improve queue priority for your visitors.
three.js	0.185.1 (MIT)	3D renderer	The only realistic in-browser 3D engine with first-class VRM support. Pin the exact version — three.js makes breaking renderer changes at a ~6-week cadence and three-vrm tracks it loosely.
@pixiv/three-vrm	3.5.5 (MIT)	VRM avatar loading, humanoid bones, expressions, spring-bone physics	The reference implementation from the company that runs VRoid Hub. Supports both VRM 0.0 and VRM 1.0, so community VRoid models load without conversion. Peer dep is three >= 0.137, so 0.185.1 is safe.
VOICEVOX CORE	0.16.4 (+cpu abi3 wheel)	Japanese TTS with per-mora phoneme timing	See "The lip-sync decision" below. This is the highest-leverage choice in the whole stack.
whisper-large-v3-turbo	via transformers 5.x on ZeroGPU	Japanese ASR	Best measured accuracy/speed balance for Japanese conversational audio in the Feb-2026 benchmark: CER 0.184, RTF 0.013. MIT-licensed.
Qwen3.5-4B	Apache-2.0, released 2026-03-02	Free-default tutor LLM	4B dense, 201 languages incl. Japanese, 256K context, hybrid thinking (disable thinking for latency). Apache-2.0 means no license story to explain to a hiring manager. Fits easily in 48 GB with room for KV cache.
transformers	5.14.1	Model loading	v5 is current. Note: v5 was a breaking release — do not copy v4-era snippets from blogs.
huggingface_hub	1.27.0	Hub + Inference Providers client	v1.x. Provides InferenceClient for the BYOK/routed paths.
Neon Serverless Postgres	Free plan	User accounts + progress persistence	See "The database decision" below.

The lip-sync decision (do not skip this)

• No audio-amplitude analysis. ChatVRM and most AITuber kits drive the aa blendshape from a Web Audio AnalyserNode RMS — the mouth just flaps open and shut. Mora-timed visemes are visibly better and are the thing that makes the demo look expensive.
• No forced alignment, no viseme ML model, no GPU.
• Zero ZeroGPU quota consumed for speech output, which is what makes the free tier survivable.
• The same mora timeline doubles as pedagogical data: mora-by-mora pitch accent is already in the payload, so "here is where your pitch accent was wrong" is nearly free.
• voicevox_core is dual-licensed LGPL v3 + a commercial no-source-disclosure license. Dynamic linking via the Python wheel keeps you clear of copyleft on your own code.
• Each character voice has its own terms. The general rule is free for commercial and non-commercial use provided you display credit in the form VOICEVOX:キャラクター名. Without credit, per-character paid licensing is reportedly ¥400,000/character.
• Read the specific character's terms before shipping, and render the credit string persistently in the UI (a footer line next to the avatar). Prefer a character with permissive, well-documented terms (ずんだもん / VOICEVOX Nemo voices are the usual safe picks).
• Also verify the terms permit offering synthesis to third parties via a web app, not merely using generated audio — this is the one clause a portfolio Space most plausibly trips.

The avatar-embedding decision

Approach	Verdict
gr.HTML with html_template/js_on_load	✅ Recommended. No build step, no npm publish, hot-reloads with the app, server_functions gives direct JS→Python calls, watch('value') gives Python→JS push. Ships as one app.py.
Full Gradio custom component (gradio cc)	❌ Requires Svelte, Node 18+, npm 9+, a build/publish cycle, and a separate package on PyPI. Justified only if you intend to publish the VRM component for others — a nice-to-have, not a v1 need.
Raw <iframe> + postMessage	❌ You hand-roll the entire bridge, fight Space iframe sandboxing and cookie issues, and lose Gradio's event system.
Server-rendered talking-head video	❌ Already correctly in PROJECT.md Out of Scope. Would also incinerate ZeroGPU quota.

The database decision

Option	Verdict
Neon serverless Postgres (free plan)	✅ Recommended. 0.5 GB storage + 100 compute-hours/project/month, up to 100 projects, no credit card. Compute scales to zero after 5 min idle and auto-resumes in a few hundred milliseconds on the next query — no manual intervention, ever.
Supabase (free plan)	⚠️ Only if you need its Auth/Storage too. Free projects are auto-paused after 7 days of inactivity and require a manual dashboard unpause. For a portfolio piece whose entire value is "a hiring manager clicks the link and it works," a silent 7-day dead-man's-switch on your user database is a project-killing failure mode.
SQLite on Space disk	❌ Space disk is ephemeral — wiped on every restart/rebuild, and Spaces rebuild on every git push. You would lose user progress every deploy.
HF Storage Buckets (S3-like, Xet-backed)	⚠️ Great for assets (VRM files, cached TTS audio, lesson media) — mountable as a Space volume, mutable, free allowance. Not a transactional database; SQLite over a network-mounted bucket invites corruption under concurrency.
HF Dataset repo + CommitScheduler	⚠️ The classic HF persistence pattern, fine for append-only analytics/telemetry. Wrong shape for per-user read-modify-write of progress records (git commits, no transactions, no concurrent-write story).

Supporting Libraries

Library	Version	Purpose	When to Use
SudachiPy	0.6.11 (Apache-2.0)	Japanese tokenization + reading_form() for furigana + lemmas	Primary Japanese NLP engine. Chosen over MeCab/fugashi for the clean Apache-2.0 story and pure-wheel install (no MeCab system dependency to fight in a Space build). Three splitting modes (A/B/C) map neatly onto "vocab item" vs "phrase" granularity for a tutor.
SudachiDict-core	20260723 (Apache-2.0)	Dictionary for SudachiPy	Required. -core is the right size/coverage tradeoff; -full bloats the image.
jaconv	0.5.0 (MIT)	hiragana ⇄ katakana ⇄ half-width conversion	Normalizing learner input, generating kana drills, feeding the viseme mapper. Tiny, zero-dependency, MIT.
cutlet	0.5.2	Romaji generation	N5 learners need romaji scaffolding. Built on fugashi; use only if you also pull fugashi, otherwise derive romaji from SudachiPy readings + jaconv.
@huggingface/transformers (transformers.js)	4.2.0 (Apache-2.0)	Browser-side Whisper ASR fallback	v4 (Feb 2026) shipped a C++-rewritten WebGPU runtime (~4× faster, 53% smaller bundles). Running ASR here costs zero ZeroGPU quota — this is how anonymous visitors get more than four conversational turns.
anthropic	0.121.0	BYOK frontier path (Claude)	Visitor pastes their key; nothing is stored server-side.
openai	2.53.0	BYOK frontier path (OpenAI) — and reused as the client for HF's OpenAI-compatible router (https://router.huggingface.co/v1)	One client library covers two providers.
psycopg[binary]	3.x	Postgres driver	Neon over TLS. Use a pooled connection string; a Space restart should not leak connections.
SQLAlchemy	2.x	ORM / schema	Optional but recommended once progress modelling gets past ~4 tables (level, vocab-seen SRS state, mistakes, sessions).
onnxruntime	pulled by voicevox_core downloader	VOICEVOX inference backend	CPU build. Managed by the VOICEVOX downloader, do not hand-install.
fastrtc	0.0.34	Deferred. True barge-in / VAD / turn-taking over WebRTC	HF's real-time library, Gradio-team maintained, with built-in VAD and turn detection + Cloudflare TURN. Still 0.0.x — pre-1.0 API churn is not what you want under a flagship portfolio piece in v1. Revisit for a "real-time conversation" milestone once turn-based voice is solid.

Development Tools

Tool	Purpose	Notes
uv	Dependency resolution + venv	Fast, and its lockfile makes the "works locally, breaks on Space rebuild" class of bug reproducible. Export to requirements.txt for the Space (Spaces do not read uv.lock).
ruff	Lint + format	Pre-commit hook. Prior GSD work in this account has been blocked by accumulated ruff debt — enforce from commit #1 here.
pytest	Unit tests	Focus on the viseme-timeline builder and the JLPT-level content selector — both are pure functions with high bug density.
Playwright	E2E	The avatar is a canvas; assert on trigger() events and audio playback state, not pixels. Screenshot-diff the VRM render at your own risk.
hf CLI	Space + bucket management	hf buckets sync for asset deploys.
Space README front-matter	Config	Must pin sdk: gradio, sdk_version: 6.22.0, python_version: 3.12.12, hf_oauth: true.

Authentication

README.md front-matter

no extra scopes needed for progress tracking — "openid profile" is always included

• Request only openid profile (always included) since progress lives in your own DB. Every extra scope is a consent-screen deterrent.
• Add the inference-api scope only if you implement the optional "spend my own HF credits" path (lets you call Inference Providers on the visitor's behalf — free users get ~$0.10/month of credits, PRO $2.00, so treat it as a nicety, not a tier).
• hf_oauth_expiration_minutes maxes at 43200 (30 days). Use it — re-authing daily kills a daily-habit app.
• ⚠️ gr.LogoutButton was removed in Gradio 6. Build your own sign-out. (gr.LoginButton / gr.OAuthProfile / gr.OAuthToken are believed intact — verify on first spike, MEDIUM confidence.)
• ⚠️ Use target="_blank" on the sign-in button, per HF docs, or third-party-cookie policies break the flow inside the Space iframe.

Installation

requirements.txt (Hugging Face Space)

--- inference ---

--- Japanese TTS (CPU, zero GPU quota) ---

--- Japanese NLP ---

--- persistence ---

--- BYOK ---

One-time, in the Space build (voicevox_core needs runtime + dict + voice models)

The official downloader fetches onnxruntime, the Open JTalk dictionary, and .vvm voice models.

Alternatives Considered

Recommended	Alternative	When to Use Alternative
VOICEVOX (TTS)	Qwen3-TTS-12Hz-0.6B / 1.7B (Apache-2.0, Jan 2026, JA among 10 langs, voice cloning from 3s)	If VOICEVOX character terms turn out to prohibit third-party web synthesis, or you want a custom/cloned tutor voice. Cost: runs on GPU (burns visitor quota), and gives you no phoneme timings — you fall back to amplitude-driven lip-sync or add a forced aligner.
VOICEVOX (TTS)	Style-Bert-VITS2 (2.5.0)	If you want maximum expressive/emotional Japanese prosody. Cost: AGPL-3.0, which would infect your whole app unless isolated behind a separate network service. For a public portfolio repo, that is a conversation you do not want to have in an interview.
VOICEVOX (TTS)	Kokoro-82M (Apache-2.0, kokoro-js 1.2.1 runs in-browser)	English-first fallback, or if you need TTS with literally zero server cost. Rejected for Japanese: its own VOICES.md grades the five JA voices C- to C+ on <10 hours of training data and warns "support for non-English languages may be absent or thin due to weak G2P." A pronunciation-teaching app cannot ship C-grade Japanese.
whisper-large-v3-turbo (ASR)	Qwen3-ASR-1.7B	Best measured Japanese accuracy (CER 0.140 vs 0.184) — use if transcription errors are visibly hurting the correction/scoring feature. Cost: RTF 0.036 vs 0.013 (~3× slower), which matters when GPU-seconds are the visitor's scarcest resource.
whisper-large-v3-turbo (ASR)	transformers.js Whisper in-browser (WebGPU)	Recommend shipping both. Browser-side = zero quota, works while the Space is queued, and is a genuinely impressive "runs on your GPU" demo. Server-side = the accuracy fallback for weak devices / no-WebGPU browsers.
whisper-large-v3-turbo (ASR)	nvidia/parakeet-tdt-0.6b-v3	Only if latency is the binding constraint (RTF 0.003) and you can tolerate CER 0.321 — too error-prone to grade a learner's pronunciation against.
Qwen3.5-4B (LLM)	Nemotron Nano 9B JP (NVIDIA)	Ranked #1 in the sub-10B category on Nejumi Leaderboard 4 (the Japanese LLM leaderboard). Swap in if Qwen3.5-4B's Japanese pedagogy/naturalness disappoints in evaluation. Cost: 2× the size (more GPU-seconds per turn) and the NVIDIA Open Model License rather than Apache-2.0.
Qwen3.5-4B (LLM)	Qwen3.5-2B / 0.8B	If per-turn GPU-seconds prove to be the binding UX constraint. Cheapest lever for stretching an anonymous visitor's 2-minute daily quota.
Qwen3.5-4B (LLM)	LLM-jp-4 (32B MoE, 3.8B active, Apache-2.0, NII)	The Japanese-sovereign, research-credible choice — reportedly MT-Bench JA 7.82 vs GPT-4o's 7.29. MoE with 3.8B active params is surprisingly viable on a 48 GB Blackwell. Strong "I evaluated Japanese-specific models" story for a Google interview. Cost: 32B of weights to load = slow cold starts.
Neon (DB)	Supabase	If you later want its Auth, Realtime, or Storage as a bundle and will pay for Pro (which removes the 7-day pause).
Gradio gr.HTML	Gradio custom component (gradio cc)	If you decide to publish gradio-vrm-avatar as a reusable package — genuinely good portfolio surface area, but a Phase-N+1 concern.

What NOT to Use

Avoid	Why	Use Instead
CPU Basic as the free hosting plan	Gradio Spaces on compute now require PRO for personal accounts. CPU Basic is not the free path, and its 2 vCPU could not run a 4B LLM anyway.	ZeroGPU Gradio Space (free for up to 2 Spaces on an account in good standing).
Python 3.13	ZeroGPU images provide only 3.10.13 and 3.12.12. This is the known "Python 3.13 / gradio pin" gotcha, and on ZeroGPU it is a hard wall, not a warning.	python_version: 3.12.12 in README front-matter.
Unpinned sdk_version	Spaces silently rebuild on push. An unpinned Gradio floats you into a breaking release while you sleep — and Gradio 6 removed/renamed a lot (show_api→api_visibility, tuple chatbot messages, gr.LogoutButton, mirror_webcam, cache_examples="lazy").	Pin sdk_version: 6.22.0 and bump deliberately.
Gradio 5-era tutorials and code	Gradio 6 moved theme/css/js/head from the Blocks() constructor to Blocks.launch(), consolidated all show_*_button params into buttons, and removed tuple-format chatbot messages entirely. Copy-pasted v5 snippets will fail in non-obvious ways.	The Gradio 6 migration guide as the primary reference.
torch.compile on ZeroGPU	Explicitly unsupported.	PyTorch ahead-of-time compilation (torch >= 2.8), documented in HF's zerogpu-aoti guide. Meaningful latency win = meaningful quota win.
Lazy .to('cuda') inside @spaces.GPU	Docs are explicit that CUDA transfers are optimized for startup placement; lazy loading is "significantly less efficient." Every wasted second is billed to your visitor's daily quota.	Load models to cuda at module level (a CUDA emulation layer makes this work outside GPU context).
pykakasi	GPL-3.0-or-later. For kana/romaji conversion — a trivially replaceable function — it would impose GPL on a flagship public portfolio repo.	SudachiPy (Apache-2.0) readings + jaconv (MIT) + cutlet for romaji.
Style-Bert-VITS2 embedded in-process	AGPL-3.0. §13 means network use triggers source disclosure for the embedding server.	VOICEVOX (LGPL + credit) or Qwen3-TTS (Apache-2.0).
edge-tts / unofficial Microsoft endpoints	Excellent Japanese voices, but it's an undocumented consumer endpoint used against its ToS, and it breaks without notice. A hiring manager finding a ToS-violating dependency in a flagship repo is a pure downside.	VOICEVOX.
Web Speech API (SpeechRecognition) as primary ASR	Chromium-only in practice, silently ships the learner's audio to Google, and returns no usable confidence/timing data — which is exactly the data pronunciation scoring needs.	transformers.js Whisper (browser) + whisper-large-v3-turbo (ZeroGPU). Web Speech is acceptable as a last-resort tertiary fallback only.
kotoba-whisper (v1/v2)	Fast (6.3× large-v3) and great in-domain, but the Feb-2026 benchmark flags it as struggling with unscripted/natural conversation — which is 100% of this app's input.	whisper-large-v3-turbo.
SQLite on Space disk	Space disk is ephemeral; every git push rebuilds and wipes it.	Neon Postgres.
Supabase free tier for the user DB	Auto-pauses after 7 days idle, requires manual dashboard unpause. Guaranteed to be paused exactly when someone finally clicks your portfolio link.	Neon (scale-to-zero, auto-resume in ~hundreds of ms).
fastrtc in v1	0.0.34 — pre-1.0, active API churn, and it wraps the Gradio version you must pin.	Turn-based gr.Audio(sources=["microphone"]); adopt fastrtc in a later real-time milestone.
AI talking-head video generation	Already Out of Scope in PROJECT.md — and it would consume a visitor's entire daily GPU quota in a single utterance.	VRM + mora-timed visemes.

Stack Patterns by Variant

• Hardware: ZeroGPU, max 2 such Spaces on the account — budget that slot deliberately against the other 4 HF-profile projects.
• Design every turn to fit inside ~3–6 GPU-seconds so an anonymous visitor gets 20+ turns from 2 minutes.
• Push ASR to the browser by default; use ZeroGPU ASR only as fallback.
• Free Spaces sleep after inactivity → expect a cold start on the recruiter's first click. Make the loading state part of the show: render the VRM avatar immediately (it's client-side and needs no backend) with a "waking up" idle animation while the Python backend boots.
• Up to 10 ZeroGPU Spaces; the owner's own testing gets 40 min/day at highest queue priority.
• $2.00/month Inference Provider credits make an Inference Providers–routed default LLM viable, removing cold-start and quota concerns entirely for light traffic.
• Visitor quotas are unchanged — PRO does not fix the anonymous visitor's 2 minutes. Design for free-tier visitors regardless.
• Swap to Qwen3-TTS-12Hz-0.6B (Apache-2.0) on ZeroGPU.
• You lose free phoneme timings → recover lip-sync by deriving the mora sequence from SudachiPy readings → katakana → vowel sequence and distributing it proportionally across the audio duration, refined by a Web Audio RMS envelope. Noticeably worse than VOICEVOX timings, still far better than pure amplitude flapping.
• Move the default LLM to HF Inference Providers routed with the visitor's OAuth token (inference-api scope) — their credits, their latency, no queue.
• Keep BYOK (Anthropic/OpenAI) as the premium path. This is the honest three-tier story: free/queued → your-HF-credits → your-frontier-key.

Version Compatibility

Package A	Compatible With	Notes
gradio==6.22.0	Python >=3.10 (3.10–3.13 classifiers)	But ZeroGPU narrows this to 3.10.13 / 3.12.12. The platform is stricter than the package.
spaces==0.51.1	Python >=3.10,<3.15	Consistent with the above.
ZeroGPU	torch 2.8.0 → 2.11.0, Gradio 4+	Pin torch explicitly; a transitive bump outside this range breaks GPU allocation.
ZeroGPU	Gradio SDK only	Not Docker, not Static. This makes Gradio non-negotiable, which conveniently matches the portfolio requirement.
@pixiv/three-vrm@3.5.5	three >= 0.137 (peer)	Verified against three@0.185.1. Load both from the same CDN origin via importmap — mismatched three.js instances cause the classic "multiple instances of three.js imported" breakage.
@pixiv/three-vrm-animation@3.5.5	same peer range	Only needed if you use .vrma animation clips for idle/gesture motion. Recommended for idle breathing/blinking.
transformers==5.14.1	huggingface_hub>=1.x	Both are v-major releases from the last cycle; v4-era transformers snippets will not run.
voicevox_core 0.16.4 cp310-abi3 wheel	Python >=3.10 incl. 3.12	abi3 wheels are forward-compatible across minor versions — the cp310 tag is not a 3.10-only restriction. Linux wheel: manylinux_2_34_x86_64.
SudachiPy 0.6.11	SudachiDict-core 20260723	Dict packages are date-versioned and must match the SudachiPy major line; pin both together.
Neon Postgres	psycopg[binary] 3.x	Requires TLS + SNI. Use the pooled endpoint; Spaces restart often and unpooled connections leak.

Open Questions for the Roadmap

Sources

• HF Spaces Overview — hardware tiers, PRO requirement for Gradio/Docker Spaces, ephemeral disk, sleep behavior, built-in env vars
• HF Spaces ZeroGPU — RTX Pro 6000 Blackwell, Gradio-only compatibility, torch 2.8–2.11, Python 3.10.13/3.12.12, per-visitor daily quotas, @spaces.GPU, no torch.compile
• HF Spaces OAuth — hf_oauth metadata, scopes, expiration limits, target=_blank caveat
• HF Spaces disk usage + Storage Buckets — ephemerality, bucket volumes
• HF Inference Providers pricing — $0.10 free / $2.00 PRO monthly credits, OpenAI-compatible router
• HF pricing — PRO $9/mo
• Gradio 6 migration guide — breaking changes, removals
• Gradio custom HTML components + HF blog: gr.HTML one-shot apps — html_template, head, js_on_load, server_functions, three.js precedent
• VRM 1.0 expressions spec — aa/ih/ou/ee/oh presets
• VOICEVOX core releases — 0.16.4, manylinux abi3 CPU wheel
• Kokoro-82M VOICES.md — Japanese voice grades C- to C+
• Qwen3.5-4B model card — Apache-2.0, 201 languages, 256K context, thinking mode
• PyPI/npm registry APIs queried directly for every pinned version in this document
• Japanese ASR benchmark, Feb 2026 (Neosophie) — CER/WER/RTF table; single benchmark, not independently replicated
• Japanese LLMs compared, Apr 2026 (lilting.ch) — LLM-jp-4, Nemotron Nano 9B JP, PLaMo, Swallow, Namazu; Nejumi 4 rankings quoted secondhand
• VOICEVOX engine API reference (DeepWiki) — AudioQuery/mora structure
• VOICEVOX commercial-use and credit requirements — Japanese-language secondary sources; must be confirmed against the official 利用規約 per character
• Transformers.js v4 release — WebGPU runtime rewrite
• Neon vs Supabase free-tier behavior — multiple 2026 sources agree, vendor docs corroborate scale-to-zero

Conventions

Conventions not yet established. Will populate as patterns emerge during development.

Architecture

Architecture not yet mapped. Follow existing patterns found in the codebase.

GSD Workflow Enforcement

Before using Edit, Write, or other file-changing tools, start work through a GSD command so planning artifacts and execution context stay in sync.

Use these entry points:

• /gsd:quick for small fixes, doc updates, and ad-hoc tasks
• /gsd:debug for investigation and bug fixing
• /gsd:execute-phase for planned phase work

Do not make direct repo edits outside a GSD workflow unless the user explicitly asks to bypass it.

Developer Profile

Profile not yet configured. Run /gsd:profile-user to generate your developer profile. This section is managed by generate-claude-profile -- do not edit manually.