Agentic Engineering GitHub Tape Review

Watch your own game tape. Study recent GitHub activity to find what is helping and what is slowing you down as an agentic engineer.

Use This Skill When

• You want a structured retrospective on your GitHub throughput.
• You suspect hidden bottlenecks in review, merge, rework, or agent-attributed activity.
• You are doing deliberate practice on agentic engineering and need concrete evidence to calibrate on.
• Weekly or biweekly check-in on contribution patterns.

Context

• Your current KPI is >1000 contributions/day.
• This is an imperfect but concrete, easy-to-measure target — preferred over vague ones.
• Your GitHub account may reflect work done by coding agents using your credentials, so account-attributed activity is not the same as personally-written code.
• What matters is understanding throughput, bottlenecks, rework, review/merge load, and how work is being shaped.

Data Source

• Your GitHub activity from the last 7-14 days.
• Default to 14 days if that gives better signal. Do not go further back unless truly necessary.
• Use the GitHub CLI (gh) and GitHub API to pull activity: PRs opened/merged/closed, commits, reviews, issues, comments.

How To Gather Data

Use gh to collect raw activity. Useful starting points:

# PRs authored in the last 14 days
gh pr list --author @me --state all --limit 200 --json number,title,state,createdAt,mergedAt,closedAt,additions,deletions,reviews

# Recent commits across repos
gh api graphql -f query='
{
  viewer {
    contributionsCollection(from: "<14-days-ago-ISO>", to: "<now-ISO>") {
      totalCommitContributions
      totalPullRequestContributions
      totalPullRequestReviewContributions
      totalIssueContributions
      contributionCalendar {
        weeks {
          contributionDays {
            date
            contributionCount
          }
        }
      }
    }
  }
}'

# Review activity
gh pr list --reviewed-by @me --state all --limit 200 --json number,title,state,createdAt,mergedAt

# Issues
gh issue list --author @me --state all --limit 100 --json number,title,state,createdAt,closedAt

Adapt queries to the user's GitHub username and relevant repos. Pull from multiple repos if the user works across several.

Analysis

Study the activity and report what seems to be happening. Do not give generic productivity advice. Focus on patterns in the evidence.

What to look for

• Where work moved cleanly from open to merge.
• Where work got stuck, recut, or superseded.
• Where review/merge burden seems high.
• Where visible activity may be hiding a different real bottleneck.
• What kinds of work units seem to create leverage vs drag.
• Daily contribution counts relative to the >1000/day target.
• Ratio of agent-attributed vs manually-driven work if distinguishable.

Approach

• Be exploratory rather than forcing everything into a predefined framework.
• Let patterns emerge from the evidence.
• Use exact examples and citations when making claims — link to specific PRs, commits, or issues.
• State tentative hypotheses, not fake certainty.

Output

Structure the output as:

1. Window analyzed — exact date range and repos covered.
2. Main patterns — the 3-5 most prominent patterns you see.
3. Strongest examples — concrete PRs, commits, or issues that support each pattern, with links.
4. What seems to be helping — things that correlate with clean throughput.
5. What seems to be slowing things down — things that correlate with drag, rework, or stalls.
6. Tentative hypotheses — your best interpretation, clearly flagged as provisional.
7. Worth tracking next week — anything that warrants follow-up in the next tape review.

Failure Recovery

• If gh is not authenticated, prompt the user to run gh auth login.
• If the user works across many repos, ask which repos to focus on rather than trying to pull everything.
• If the GraphQL contributions API returns empty data, fall back to per-repo PR and commit queries.
• If the 14-day window has too little data, extend to 21 days and note the adjustment.