Study github repo
Skill allanbian1017/skills/skills/personal/study-github-repo
Skills for automate workflows, assist in daily tasks, and enhance overall productivity.
npx -y skills add allanbian1017/skills --skill study-github-repoAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Analyze a GitHub repository and generate a comprehensive study report covering architecture, design decisions, code quality, and repo health. Use this skill whenever the user provides a GitHub repo URL and wants to understand it deeply, says 'study this repo', 'analyze this GitHub project', 'what does this repo do', 'how is this repo designed', or any request to understand a GitHub repository's architecture, codebase, or engineering quality. Also use when the user wants a repo breakdown, code review, or architecture analysis of a GitHub project.
SKILL.md
7.0 KB, ~1.6k tokens by cl100k_base, as published. Nobody here has run it
study-github-repo
Clone a GitHub repository locally, study it systematically across 14 dimensions, and produce a comprehensive report in the configured output language (defaulting to English).
Prerequisite:
ghCLI installed and authenticated. Verify withgh auth status.
Procedure
Step 1 — Parse Input
Extract {owner} and {repo} from the GitHub URL. Accept any of these formats:
https://github.com/{owner}/{repo}github.com/{owner}/{repo}{owner}/{repo}
Step 2 — Gather Repo Metadata
Fetch metadata via gh api before cloning (lightweight, fast):
# Repo overview
gh api 'repos/{owner}/{repo}' --jq '{
description, stargazers_count, forks_count,
language, license: .license.spdx_id,
topics, created_at, updated_at, open_issues_count,
default_branch, archived, size
}'
# Language breakdown
gh api 'repos/{owner}/{repo}/languages'
# Recent releases (last 5)
gh api 'repos/{owner}/{repo}/releases?per_page=5' --jq '.[].tag_name'
# Contributors (top 10)
gh api 'repos/{owner}/{repo}/contributors?per_page=10' --jq '.[] | {login, contributions}'
# Recent commits (last 30)
gh api 'repos/{owner}/{repo}/commits?per_page=30' --jq '.[] | {date: .commit.author.date, message: .commit.message | split("\n")[0]}'
# Recent issues (last 10, all states)
gh api 'repos/{owner}/{repo}/issues?state=all&per_page=10' --jq '.[] | {number, title, state, labels: [.labels[].name]}'
# Recent PRs (last 10, all states)
gh api 'repos/{owner}/{repo}/pulls?state=all&per_page=10' --jq '.[] | {number, title, state, merged_at}'
Step 3 — Clone Locally
mkdir -p .tmp/github_study
gh repo clone {owner}/{repo} .tmp/github_study/{owner}_{repo} -- --depth=1 \
-c url.https://github.com/[email protected]:
Use --depth=1 for a shallow clone (faster, smaller). The -c url...insteadOf flag forces HTTPS to avoid SSH key issues. Full history is not needed — commit metadata is already fetched via API.
Step 4 — Phase 1: Orientation
Build a rough mental map of the repo. Read these in order:
- README — Extract the problem statement, claimed solution, and target audience
- Directory tree — Run
find .tmp/github_study/{owner}_{repo} -not -path '*/.git/*' -not -path '*/node_modules/*' -not -path '*/__pycache__/*' -not -path '*/dist/*' -not -path '*/build/*' -not -path '*/.venv/*' | head -200to see the structure (or usetree -L 3 -I '...'if available) - Config files — Read package.json / pyproject.toml / Cargo.toml / go.mod / Makefile / docker-compose.yml (whichever exist) to understand dependencies, scripts, and build setup
- Docs directory — If
docs/or similar exists, scan for architecture docs, guides, or design documents - Examples — If
examples/exists, read a representative example to understand typical usage
Step 5 — Phase 2: Trace Execution Flow
Follow one real execution path from input to output:
- Find entry points — Look for
main.py,index.ts,server.js,app.ts,src/index.*,cmd/,bin/, CLI entry files, API routers - Trace the critical path — Starting from the primary entry point, follow the code through orchestration → business logic → infrastructure layer. Read every file along this path.
- Identify the data flow — Map: Input → Processing → Output. What transformations happen? What side effects occur?
There is no artificial file cap. Use engineering judgment to determine when you have enough understanding to explain how the repo delivers on its README promise. For large repos, focus on the core module rather than peripheral utilities.
Step 6 — Phase 3: Deep Analysis
Using your understanding from Phases 1-2, now systematically investigate each dimension:
- Core Abstractions — Find interfaces, base classes, schemas, protocols, type definitions. What primitives is the system built on?
- State Management — Where does state live? Who owns it? How is it updated? Is there persistence/checkpointing?
- Decision Logic — Find planners, routers, evaluators, scoring systems, retry policies, fallback mechanisms
- Feedback Loops — Look for logging, telemetry, metrics, evaluation pipelines, retry/correction loops
- Tests — Read test files. What types exist (unit, integration, e2e)? What do they reveal about intended behavior and edge cases?
- DX signals — Evaluate setup instructions, documentation quality, naming conventions, type safety, scripts
- Hidden Architectural Flaws — Identify exactly 5 architectural flaws hidden in plain sight. Pinpoint the exact file paths and line numbers, and evaluate their impact on future maintainability.
Use grep_search / ripgrep to search for patterns across the codebase without reading full files. Examples:
grep -r "class.*Base\|class.*Abstract\|interface " --include="*.py" --include="*.ts"grep -r "log\.\|logger\.\|console\.log\|logging\." --include="*.py" --include="*.ts" -lgrep -r "test\|describe\|it(" --include="*test*" --include="*spec*" -l
Step 7 — Write Report
Check data/user_preferences.md for the Preferred Output Language configuration, and write the report in that language (defaulting to English if not configured).
📄 Read
assets/output_template.mdfor the full report template.
Write the report to reports/GitHub/{owner}_{repo}.md. Every section must contain substantive content — no empty sections. If a dimension is not applicable (e.g., "State Management" for a pure utility library), explain why it's not relevant rather than leaving it blank.
The four executive summary questions must be answered:
- What problem does this repo solve?
- How is it designed?
- Can I trust/use/extend it?
- What can I learn from it?
For private/internal repos with sparse community metrics (0 stars, no releases), note this factually in Repo Health: "This appears to be a private/internal repository; community health metrics are not applicable."
Step 8 — Cleanup
rm -rf .tmp/github_study/{owner}_{repo}
Confirm to the user: "✅ Report saved to reports/GitHub/{owner}_{repo}.md. Clone cleaned up."
Error Handling
ghnot authenticated: Stop with"⚠️ GitHub CLI is not authenticated. Run 'gh auth login' first."- Repo not found / no access: Stop with
"⚠️ Repository {owner}/{repo} not found or you don't have access." - Clone fails: Report the error and suggest the user check network/permissions.
What ships with it: 2 files
6.9 KB alongside SKILL.md
assets/
- output_template.md5.2 KB
- README.md1.7 KB