| name | github-repo-insights |
| description | Analyze a GitHub repository's current traffic, visitors, clones, star momentum, referrers, popular paths, releases, and community signals. Use when asked where repository growth data came from, how a repository is growing, which channels or README pages drive attention, whether a launch or community post changed momentum, or for a current GitHub repository growth/status report. |
GitHub Repo Insights
Collect current GitHub data first, then separate observed facts from interpretation. Do not reuse traffic numbers from an older conversation when live access is available.
Collect a snapshot
Run the bundled collector from this skill directory:
python3 scripts/collect_github_repo_insights.py OWNER/REPO --output /tmp/github-repo-insights.json
Require Python 3.10+ and an authenticated GitHub CLI (gh auth status).
Omit OWNER/REPO inside a local checkout to detect it with gh repo view.
The script uses authenticated gh access and collects:
- repository metadata and current stars, forks, watchers, issues, and latest release;
- the rolling 14-day views and unique visitors;
- the rolling 14-day clones and unique cloners;
- popular referrers and popular repository paths;
- recent daily star events through GitHub GraphQL;
- recent-seven-day versus previous-seven-day count comparisons.
Traffic endpoints require push access. Preserve access.traffic.status: "unavailable" as unavailable; never report it as zero. Public metadata and star momentum can still be analyzed.
Read references/metrics.md before interpreting traffic, attribution, conversion proxies, or missing data.
Analyze in this order
- Reach: Report 14-day views and unique visitors with the exact window and source.
- Usage intent: Report clones and unique cloners separately from views.
- Momentum: Compare the latest seven daily counts with the previous seven for views, clones, and stars. Name the peak dates.
- Acquisition: Rank referrers. Distinguish GitHub-internal, search, social, community, and direct/unknown traffic.
- Content demand: Explain which README, docs, releases, issues, or other paths received attention.
- Context: Connect releases, README changes, Trending appearances, posts, or launches only when dates align and evidence exists.
- Next action: Recommend one or two changes tied to the observed bottleneck, such as onboarding, localization, release packaging, search capture, or community follow-up.
Reporting rules
- Lead with the growth conclusion, then show the evidence.
- State that traffic data comes from GitHub's repository Traffic API and covers a rolling 14-day window.
- Never sum daily unique visitor or unique cloner values; GitHub's top-level unique count is the valid window total.
- Treat stars-per-view and clones-per-view only as directional ratios, not user conversion rates.
- Do not claim a referrer caused growth merely because dates overlap. Label causal explanations as inference.
- Separate unavailable, zero, and incomplete data.
- Compare publish dates with event dates when discussing launches, Trending, releases, or community posts.
- Avoid vanity-only summaries. Explain what changed, why it likely changed, and what to do next.
Default output
Use this compact structure unless the user requests a dashboard or historical report:
## Current momentum
One-sentence conclusion.
- 14-day views / unique visitors
- 14-day clones / unique cloners
- recent 7d vs previous 7d views, clones, and stars
- peak dates
## Where attention comes from
Ranked referrers and what they imply.
## What people inspect
Popular paths and onboarding/content implications.
## Interpretation
Proven facts, explicit inference, and important limitations.
## Next move
One or two evidence-linked actions.
For recurring monitoring, save each JSON snapshot outside the skill directory with an ISO-date filename. GitHub does not provide long-term Traffic history after the rolling window expires.
