project-profiler

name	project-profiler
description	Generate an LLM-optimized project profile for any git repository. Outputs docs/{project-name}.md covering architecture, core abstractions, usage guide, design decisions, and recommendations. 觸發詞：/project-profiler、profile this project、為專案建側寫、專案側寫、codebase profile
user_invocable	true
argument_hint	[target directory, default .]

project-profiler

Generate an LLM-optimized project profile — a judgment-rich document that lets any future LLM answer within 60 seconds:

1. What are the core abstractions?
2. Which modules to modify for feature X?
3. What is the biggest risk/debt?
4. When should / shouldn't you use this?

This is NOT a codebase map (directory + module navigation) or a diff schematic. This is architectural judgment: design tradeoffs, usage patterns, and when NOT to use.

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Model Strategy

• Opus: Orchestrator — runs all phases, writes the final profile. Does NOT read source code directly (except in direct mode).
• Sonnet: Subagents — read source code files, analyze patterns, report structured findings.
• All subagents launch in a single message (parallel, never sequential).

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Phase 0: Preflight

0.1 Target & Project Name

Determine the target directory (use argument if provided, else .).

Extract project name from the first available source:

1. package.json → name
2. pyproject.toml → [project] name
3. Cargo.toml → [package] name
4. go.mod → module path (last segment)
5. Directory name as fallback

0.2 Run Scanner

uv run {SKILL_DIR}/scripts/scan-project.py {TARGET_DIR} --format summary

Capture the summary output. This provides:

• Project metadata (name, version, license, deps count)
• Tech stack (languages, frameworks, package manager)
• Language distribution (top 5 by tokens)
• Entry points (CLI, API, library)
• Project features (dockerfile, CI, tests, codebase_map)
• Detected conditional sections (Storage, Embedding, Infrastructure, etc.)
• Workspaces (monorepo packages, if any)
• Top 20 largest files
• Directory structure (depth 3)

For debugging or when full file details are needed, use --format json instead.

0.3 Git Metadata

Run these commands (use Bash tool):

# Recent commits
git -C {TARGET_DIR} log --oneline -20

# Contributors
git -C {TARGET_DIR} log --format="%aN" | sort -u | head -20

# Version tags
git -C {TARGET_DIR} tag --sort=-v:refname | head -5

# First commit date
git -C {TARGET_DIR} log --format="%aI" --reverse | head -1

0.4 Check Existing CODEBASE_MAP

If docs/CODEBASE_MAP.md exists, note its presence. The profile will reference it rather than duplicating directory structure.

0.5 Token Budget → Execution Mode

Based on total_tokens from scanner, choose execution mode:

Total Tokens	Mode	Strategy
≤ 80k	Direct	Skip subagents. Opus reads all files directly and performs all analysis in a single context.
80k – 200k	2 agents	Agent AB (Core + Architecture + Design), Agent C (Usage + Patterns + Deployment)
200k – 400k	3 agents	Agent A (Core + Design), Agent B (Architecture + Patterns), Agent C (Usage + Deployment)
> 400k	3 agents	Agent A, Agent B, Agent C — each ≤150k tokens, with overflow files assigned to lightest agent

Why 80k threshold: Opus has 200k context. At ≤80k source tokens, loading all files + scanner output + git metadata + writing the profile all fit comfortably. Subagent overhead (spawn + communication + wait) adds 2-3 minutes for zero benefit.

Direct mode workflow: Skip Phase 2 entirely. After Phase 0+1, proceed to Phase 3 (read scanner detected_sections directly), then Phase 4, then Phase 5. Read files on-demand during synthesis — do NOT pre-read all files; read only what's needed for each section.

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Phase 1: Community & External Data

Run in parallel with Phase 2 subagent launches (or with Phase 3 in direct mode).

1.1 GitHub Stats

Parse owner/repo from .git/config remote origin URL:

git -C {TARGET_DIR} remote get-url origin

Extract owner/repo from the URL. Then:

gh api repos/{owner}/{repo} --jq '{stars: .stargazers_count, forks: .forks_count, open_issues: .open_issues_count}'

If gh is unavailable or not a GitHub repo → fill with N/A. Do not fail.

1.2 Package Downloads

npm (if package.json exists):

WebFetch https://api.npmjs.org/downloads/point/last-month/{package_name}

PyPI (if pyproject.toml exists):

WebFetch https://pypistats.org/api/packages/{package_name}/recent

If fetch fails → fill with N/A.

1.3 License

Read from (in order): LICENSE file → package metadata field → N/A.

1.4 Maturity Assessment

Calculate from:

• Git history length: first commit date → now
• Release count: number of version tags
• Contributor count: unique authors

Criteria	Score
< 3 months, < 3 releases, 1-2 contributors	experimental
3-12 months, 3-10 releases, 2-5 contributors	growing
1-3 years, 10-50 releases, 5-20 contributors	stable
> 3 years, > 50 releases, > 20 contributors	mature

Use the lowest matching tier (conservative estimate).

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Phase 2: Parallel Deep Exploration

Direct mode (≤80k tokens): SKIP this entire phase. Proceed to Phase 3. Opus reads files directly during synthesis.

Launch Sonnet subagents using the Task tool. All subagents must be launched in a single message.

Assign files to each agent based on the token budget from Phase 0.5. Use the scanner output to determine which files go to which agent.

File Assignment Strategy

If workspaces detected (monorepo):

1. Group files by workspace package
2. Assign complete packages to agents (never split a package across agents)
3. Agent A gets packages with core business logic
4. Agent B gets packages with infrastructure/shared libraries
5. Agent C gets packages with CLI/API/SDK surface + docs

If no workspaces (single project):

1. Sort all files by path
2. Group by top-level directory
3. Assign groups to agents based on their responsibility:
   • Agent A gets: core source files (src/lib, core/, models/, types/) + README, CHANGELOG
   • Agent B gets: architecture files (routes/, middleware/, config/, entry points) + tests/
   • Agent C gets: integration files (API, CLI, SDK, examples/, docs/) + .github/
4. If files don't fit neatly, distribute remaining to agents under budget

Agent Prompts

Read references/agent-prompts.md for the full prompt template of each agent (A, B, C). Substitute {LIST_OF_ASSIGNED_FILES} with the files assigned per the strategy above.

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Phase 3: Conditional Section Detection

Read the scanner's detected_sections output from Phase 0.2. This is the primary detection source — the scanner checks dependency manifests and file presence automatically.

Cross-reference with subagent reports (skip in direct mode) for additional evidence richness. If a subagent reports a pattern not caught by the scanner (e.g., concurrency via raw Promise.all without a library dependency), add it.

Refer to references/section-detection-rules.md for the full pattern reference.

Record results as a checklist:

- [x] Storage Layer — scanner detected: prisma in dependencies
- [ ] Embedding Pipeline — not detected
- [x] Infrastructure Layer — scanner detected: Dockerfile present
- [ ] Knowledge Graph — not detected
- [ ] Scalability — not detected
- [x] Concurrency — Agent B reported: Promise.all pattern in src/worker.ts

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Phase 4: Synthesis & Draft

4.1 Merge Reports

Subagent mode: Combine all subagent outputs into a working document. Direct mode: Read key files on-demand as you write each section. Do NOT pre-read all files. For each section, read only the files relevant to that section's analysis.

Cross-validate:

• Core abstractions ↔ Architecture layers: each abstraction belongs to a layer
• Architecture data flow ↔ Usage interfaces: flows end at documented interfaces
• Design decisions ↔ Code evidence: decisions are backed by found patterns

4.2 Generate Mermaid Diagrams + Structured Dependencies

Using Agent B's raw data (or direct file analysis in direct mode), create:

Architecture Topology (graph TB):

• Each node = actual module/directory
• Each edge = import/dependency relationship
• Label edges with relationship type
• Group nodes by layer using subgraph

Data Flow (sequenceDiagram):

• Each participant = actual module
• Each arrow = actual function call or event
• Cover the primary user-facing operation

Structured Module Dependencies (text, below each Mermaid diagram):

• Provide a machine-parseable dependency list as fallback for LLM readers
• Format: - **{module_name}** (\{path}`): imports [{dep1}, {dep2}, ...]`

4.3 Fill Output Template

Follow references/output-template.md exactly. Fill each section:

Section	Primary Source	Secondary Source
1. Project Identity	Scanner metadata + Phase 1	Git metadata
2. Architecture	Agent B (Parts 1-6)	Agent A (abstractions per layer)
3. Core Abstractions	Agent A (Part 1)	Agent B (layer context)
4. Conditional	Phase 3 detection + relevant agents	—
5. Usage Guide	Agent C (Parts 1-4)	Scanner entry_points
6. Performance & Cost	Agent C (Part 6) + Agent B	—
7. Security & Privacy	Agent C (Part 5)	—
8. Design Decisions	Agent A (Part 2)	Agent B (architecture context)
8.5 Code Quality & Patterns	Agent B (Part 7)	Agent A (supporting observations)
9. Recommendations	Agent A (Part 4)	Agents B/C (supporting evidence)

4.4 Write Output

Write the profile to docs/{project-name}.md using the Write tool.

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Phase 5: Quality Gate

Read references/quality-checklist.md and verify the output.

5.1 Banned Language Scan

Search the written file for any word from the banned list:

English:

well-designed, elegant, elegantly, robust, clean, impressive,
state-of-the-art, cutting-edge, best-in-class, beautifully,
carefully crafted, thoughtfully, well-thought-out, well-architected,
nicely, cleverly, sophisticated, powerful, seamless, seamlessly,
intuitive, intuitively

Chinese:

優雅、完美、強大、直觀、無縫、精心、巧妙、出色、卓越、先進、高效、靈活、穩健、簡潔

If found → replace with verifiable descriptions and re-write.

5.2 Number Audit

Scan for all numeric claims. Each must have a traceable source. Remove or fix any "approximately", "around", "roughly", "several", "many", "numerous".

5.3 Structure Verification

• Every ## section starts with > blockquote summary
• No directory tree duplicated from CODEBASE_MAP.md
• No file extension enumeration (use percentages)
• No generic concluding paragraph
• At least one Mermaid diagram in Architecture section
• Structured module dependency list below each Mermaid diagram
• All Mermaid nodes reference actual modules

5.4 Core Question Test

For each of the 4 core questions, locate the specific answer in the output:

1. Core abstractions → Section 3
2. Module to modify → Section 2 Layer Boundaries table
3. Biggest risk → Section 9 first recommendation
4. When to use/not use → Section 1 positioning line

5.5 Evidence Audit

• Section 3: every abstraction has file:SymbolName reference
• Section 8: every decision has file:SymbolName + alternative + tradeoff
• Section 8.5: code quality patterns have framework names + coverage facts
• Section 9: every recommendation has file_path + specific problem + concrete fix

If any check fails → fix the issue in the file and re-verify.

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Output

After all phases complete, report to the user:

Profile generated: docs/{project-name}.md
- {total_files} files scanned ({total_tokens} tokens)
- {N} core abstractions identified
- {N} design decisions documented
- {N} recommendations
- Conditional sections: {list of included sections or "none"}