terrylica/cc-skills/fork-intelligence

Fork Intelligence

Systematic methodology for discovering valuable work in GitHub fork ecosystems. Stars-only filtering misses 60-100% of substantive forks — this skill uses branch-level divergence analysis, upstream PR cross-referencing, and domain-specific heuristics to find what matters.

Validated empirically across 10 repositories spanning Python, Rust, TypeScript, C++/Python, and Node.js (tensortrade, backtesting.py, kokoro, pymoo, firecrawl, barter-rs, pueue, dukascopy-node, ArcticDB, flowsurface).

Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.

FIRST — TodoWrite Task Templates

MANDATORY: Select and load the appropriate template before any fork analysis.

Template A — Full Analysis (new repository)

1. Get upstream baseline (stars, forks, default branch, last push)
2. List all forks with pagination, note timestamp clusters
3. Filter to unique-timestamp forks (skip bulk mirrors)
4. Check default branch divergence (ahead_by/behind_by)
5. Check non-default branches for all forks with recent push or >1 branch
6. Evaluate commit content, author emails, tags/releases
7. Cross-reference upstream PR history from fork owners
8. Tier ranking and cross-fork convergence analysis
9. Produce report with actionable recommendations

Template B — Quick Scan (triage only)

1. Get upstream baseline
2. List forks, filter by timestamp clustering
3. Check default branch divergence only
4. Report forks with ahead_by > 0

Template C — Targeted Fork Evaluation (specific fork)

1. Compare fork vs upstream on all branches
2. Examine commit messages and changed files
3. Check for tags/releases, open issues, PRs
4. Assess cherry-pick viability

Signal Priority Order

Ranked by empirical reliability across 10 repositories. See signal-priority.md for details.

Pipeline — 7 Steps

Step 1: Upstream Baseline

UPSTREAM="OWNER/REPO"
gh api "repos/$UPSTREAM" --jq '{forks_count, pushed_at, default_branch, stargazers_count}'

Step 2: List All Forks + Timestamp Clustering

# List all forks with activity signals
gh api "repos/$UPSTREAM/forks" --paginate \
  --jq '.[] | {full_name, pushed_at, stargazers_count, default_branch}'

Timestamp clustering: Forks sharing exact pushed_at with upstream are bulk mirrors created by GitHub's fork mechanism and never touched. Group by pushed_at — forks with unique timestamps warrant investigation. This alone eliminates 85%+ of noise.

# Filter to unique-timestamp forks (skip bulk mirrors)
gh api "repos/$UPSTREAM/forks" --paginate \
  --jq '.[] | {full_name, pushed_at, stargazers_count}' | \
  jq -s 'group_by(.pushed_at) | map(select(length == 1)) | flatten'

Step 3: Default Branch Divergence

BRANCH=$(gh api "repos/$UPSTREAM" --jq '.default_branch')
 
# For each candidate fork
gh api "repos/$UPSTREAM/compare/$BRANCH...FORK_OWNER:$BRANCH" \
  --jq '{ahead_by, behind_by, status}'

The status field meanings:

• identical — pure mirror, skip
• behind — stale mirror, skip
• diverged — has original commits AND is behind (interesting)
• ahead — has original commits, up-to-date with upstream (rare, most valuable)

Important: Always compare from the upstream repo's perspective (repos/UPSTREAM/compare/...). The reverse direction (repos/FORK/compare/...) returns 404 for some repositories.

Step 4: Non-Default Branch Analysis (CRITICAL)

This is the single biggest methodology improvement. Across all 10 repos tested, 50%+ of the most valuable fork work lived exclusively on feature branches.

Examples:

• flowsurface/aviu16: 7,000-line GPU shader heatmap only on shader-heatmap
• ArcticDB/DerThorsten: 147 commits across conda_build, clang, apple_changes
• pueue/FrancescElies: Duration display only on cesc/duration
• barter-rs: 6 of 12 top forks had work only on feature branches

# List branches on a fork
gh api "repos/FORK_OWNER/REPO/branches" --jq '.[].name' | head -20
 
# Check divergence on a specific branch
gh api "repos/$UPSTREAM/compare/$BRANCH...FORK_OWNER:FEATURE_BRANCH" \
  --jq '{ahead_by, behind_by, status}'

Heuristics for which forks need branch checks:

• Any fork with pushed_at more recent than upstream but ahead_by == 0 on default branch
• Any fork with more than 1 branch
• Branch count > 10 is suspicious — likely non-trivial work (ArcticDB: Rohan-flutterint had 197 branches)

Step 5: Commit Content Evaluation

gh api "repos/$UPSTREAM/compare/$BRANCH...FORK_OWNER:BRANCH" \
  --jq '.commits[] | {sha: .sha[:8], message: .commit.message | split("\n")[0], date: .commit.committer.date[:10], author: .commit.author.email}'

What to look for:

• Commit email domains reveal institutional contributors (@man.com, @quantstack.net)
• Subtract merge commits from ahead_by count (e.g., akeda2/pueue showed 35 ahead but 28 were upstream merges)
• Build system changes (CMakeLists.txt, Cargo.toml, pyproject.toml) indicate platform enablement
• Protobuf schema changes indicate architectural-level features
• Test files alongside source changes signal production-intent work

Step 6: Fork-Specific Signals

# Tags/releases (strongest independent maintenance signal)
gh api "repos/FORK_OWNER/REPO/tags" --jq '.[].name' | head -10
gh api "repos/FORK_OWNER/REPO/releases" --jq '.[] | {tag_name, name, published_at}' | head -5
 
# Open issues on the fork (signals independent project maintenance)
gh api "repos/FORK_OWNER/REPO/issues?state=open" --jq 'length'
 
# Check if repo was renamed (strong divergence intent signal)
gh api "repos/FORK_OWNER/REPO" --jq '.name'

Step 7: Cross-Fork Convergence + Upstream PR History

# Check upstream PRs from fork owners
gh api "repos/$UPSTREAM/pulls?state=all" --paginate \
  --jq '.[] | select(.head.repo.fork) | {number, title, state, user: .user.login}'

Cross-fork convergence: When multiple forks independently solve the same problem, it signals unmet upstream demand:

• firecrawl: 3 forks adopted Patchright for anti-detection
• flowsurface: 3 forks added technical indicators independently
• kokoro: 2 independent batched inference implementations
• barter-rs: 4 forks added Bybit support

Upstream PR cross-reference catches:

• Rebased/force-pushed work invisible to compare API
• Work that was merged upstream (fork shows 0 ahead but was historically significant)
• Declined PRs with valuable code that the fork still maintains

Tier Classification

After running the pipeline, classify forks into tiers:

Domain-Specific Patterns

Different codebases exhibit different fork behaviors. See domain-patterns.md for full details.

Quick-Scan Pipeline (5-minute triage)

For rapid triage of any new repo:

UPSTREAM="OWNER/REPO"
BRANCH=$(gh api "repos/$UPSTREAM" --jq '.default_branch')
 
# 1. Baseline
gh api "repos/$UPSTREAM" --jq '{forks_count, pushed_at, stargazers_count}'
 
# 2. Forks with unique timestamps (skip mirrors)
gh api "repos/$UPSTREAM/forks" --paginate \
  --jq '.[] | {full_name, pushed_at, stargazers_count}' | \
  jq -s 'group_by(.pushed_at) | map(select(length == 1)) | flatten | sort_by(.pushed_at) | reverse'
 
# 3. Check ahead_by for each candidate
# (loop over candidates from step 2)
 
# 4. Check upstream PRs from fork authors
gh api "repos/$UPSTREAM/pulls?state=all" --paginate \
  --jq '.[] | select(.head.repo.fork) | {number, title, state, user: .user.login}'

Known Limitations