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Agent skill

Repo Activity Summary

by CodeAlive-AI in CodeAlive-AI/ai-driven-development

Summarize a repository's recent engineering activity from git history — technologies, work types, churn hotspots, contributor patterns, and velocity.

MITAuto-check passedDevelopment

Install Repo Activity Summary

skills CLI
$ npx skills add CodeAlive-AI/ai-driven-development --skill repo-activity-summary -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install CodeAlive-AI/ai-driven-development repo-activity-summary --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repo-activity-summary .claude/skills/repo-activity-summary && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
repo-activity-summary
GitHub stars
158
Token cost
~1.2k tokens
SKILL.md length
535 words
Files
4 (incl. scripts)
Skills in repo
22
Repo updated
First seen
Licence
MIT

At a glance

Summarize a repository's recent engineering activity from git history — technologies, work types, churn hotspots, contributor patterns, and velocity.

  • Works in 8 steps: Overview — commit count, date range,… → Technologies — languages and frameworks… → Work type breakdown — feature / bugfix /… → …
  • Asking what has this repo been working on
  • SKILL.md covers Trigger conditions, Quick start, What it produces and How it works, plus 2 more sections
  • Runs Python scripts from its folder; calls python3 and git

What it does

Repo Activity Summary is an agent skill from CodeAlive-AI/ai-driven-development. Summarize a repository's recent engineering activity from git history — technologies, work types, churn hotspots, contributor patterns, and velocity. Use when asking "what has this repo been working on", "is this project active", "who contributes what", "where are the hotspots", or before onboarding onto an unfamiliar codebase.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts (for example `README.md`, `scripts/activity_summary.py` and `tests/test_activity_summary.py`). Compatibility notes: Any coding agent with shell access. Requires git and Python 3.9+. No API keys or network access needed.

It sits in Development, covering Git workflow. The repository describes itself as: Practices, protocols, and skills for AI-driven software development. Skills and safety hooks for Claude Code, Codex, OpenCode, Cursor, Antigravity, and any agent supporting the… The licence is MIT.

When your agent uses it

  • Asking what has this repo been working on
  • Is this project active
  • Who contributes what
  • Where are the hotspots

Example prompts

  • “what has this repo been working on”
  • “is this project active”
  • “who contributes what”
  • “/repo-activity-summary”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Any coding agent with shell access. Requires git and Python 3.9+. No API keys or network access needed.
  • Pre-approved tools (allowed-tools): Bash(git:*), Bash(python3:*), Read

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Overview — commit count, date range, contributors, lines added/deleted
  2. Technologies — languages and frameworks inferred from file paths (heuristic)
  3. Work type breakdown — feature / bugfix / refactor / docs / infra / release / … (keyword heuristic)
  4. Unclassified subjects — when keyword classification is unreliable, the raw subjects so the agent can classify them
  5. Churn hotspots — most frequently modified files and directories (lockfiles/generated paths excluded)
  6. Contributor patterns — per-author commits, lines, and primary focus
  7. Velocity — commits/week, active days, average commit size (same noise filter as churn)
  8. Project health — test modules (by basename/path convention), CI, docs, recency

What it can do on your machine

Read from SKILL.md and the folder at commit 4cfeb10. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(git:*)
    • Bash(python3:*)
    • Read

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Any coding agent with shell access. Requires git and Python 3.9+. No API keys or network access needed.

    From compatibility in the SKILL.md frontmatter.

Context cost

Repo Activity Summary loads about 1.2k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 535 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from CodeAlive-AI/ai-driven-development at commit 4cfeb10, republished under its MIT licence (© CodeAlive-AI). 535 words, ~1,209 tokens.

Download SKILL.mdSave it as .claude/skills/repo-activity-summary/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
repo-activity-summary
description
Summarize a repository's recent engineering activity from git history — technologies, work types, churn hotspots, contributor patterns, and velocity. Use when asking "what has this repo been working on", "is this project active", "who contributes what", "where are the hotspots", or before onboarding onto an unfamiliar codebase.
allowed-tools
Bash(git:*), Bash(python3:*), Read
compatibility
Any coding agent with shell access. Requires git and Python 3.9+. No API keys or network access needed.
license
MIT
metadata.version
1.1.0
metadata.methodology
commit-signal-extraction

Repo Activity Summary

Answer "what's been happening in this repo" from local git history — no network, no tokens, no setup.

Trigger conditions

Use this skill when the user asks:

  • "What has this repo been working on recently?"
  • "What technologies does this project use?"
  • "Where are the high-churn files / hotspots?"
  • "Who's contributing and what are they working on?"
  • "Is this project actively maintained?"
  • Before onboarding onto an unfamiliar codebase.

Do not use this skill for file-level blame or provenance — use investigating-repository-history for that.

Quick start

bash
python3 scripts/activity_summary.py --repo-dir . --days 90
FlagDefaultDescription
--repo-dir.Path to the git repository
--days90Days of history to analyze
--author(all)Filter to one author
--formatmarkdownmarkdown, json, or text
--max-commits500Cap for very active repos
--branch(current)Branch / revision to analyze (must not start with -)
--output / -o(stdout)Write the report to a file (UTF-8)
--classify-threshold0.25If the share of unclassified commits exceeds this fraction, mark work-type classification unreliable and emit subject samples
--max-unclassified-samples40Cap on unclassified commit subjects included in the report
--raw-subjectsoffEmit every filtered commit subject with sha and date for agent-side classification
--version—Print version (1.1.0) and exit

What it produces

A structured report with:

  1. Overview — commit count, date range, contributors, lines added/deleted
  2. Technologies — languages and frameworks inferred from file paths (heuristic)
  3. Work type breakdown — feature / bugfix / refactor / docs / infra / release / … (keyword heuristic)
  4. Unclassified subjects — when keyword classification is unreliable, the raw subjects so the agent can classify them
  5. Churn hotspots — most frequently modified files and directories (lockfiles/generated paths excluded)
  6. Contributor patterns — per-author commits, lines, and primary focus
  7. Velocity — commits/week, active days, average commit size (same noise filter as churn)
  8. Project health — test modules (by basename/path convention), CI, docs, recency
Show full SKILL.md (246 more words)Show less

How it works

  1. Collects commits via git log --numstat --no-renames with an explicit UTC --since timestamp
  2. Filters bots (GitHub-style [bot] names and known automation identities) and keeps the filtered count
  3. Detects technologies from extensions, exact manifest filenames, and path segments — never bare path substrings; requires a manifest hit or at least two source files
  4. Classifies work types with precompiled word-boundary regexes and scored multi-label tie-break; does not present a large "Other" bucket as a finding
  5. Ranks files/directories by modification frequency
  6. Aggregates per-author stats using the same classifier
  7. Checks for test modules, CI config, documentation, and last activity date

Agent guidance

  • Prefer the facts in the report (counts, paths, subjects, dates) over the keyword labels.
  • When classification.unreliable is true, or the unclassified share is high, classify from the emitted subjects (or re-run with --raw-subjects) instead of trusting the work-type percentages.
  • Technology labels are path heuristics, not an import graph — verify against manifests when it matters.

Limitations

  • Shallow clones (--depth 1) limit the analysis window.
  • Technology detection is a heuristic (extensions, manifests, path segments — not imports or lockfile graphs). Labels are hints.
  • Work-type classification is a keyword heuristic, not ground truth. Conventional-commit prefixes classify well; free-form subjects often do not. When the unclassified share exceeds --classify-threshold (default 25%), the report says so and emits subjects for the agent to classify.
  • Average commit size and churn exclude lockfiles and common generated/vendor paths; the report states this filter.
  • Summarizes activity, not code quality.

© CodeAlive-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts) in skills/repo-activity-summary of CodeAlive-AI/ai-driven-development.

  • SKILL.md
  • README.md
  • scripts/activity_summary.py
  • tests/test_activity_summary.py

Open the folder on GitHubat commit 4cfeb10

Compare with similar skills

Repo Activity Summary next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Repo Activity Summary compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Repo Activity Summary this skillCodeAlive-AI/ai-driven-development158—~1.2kAutomated safety check: PassMIT
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Code Design Rationale Investigatorcursor/plugins10k9 repos~2.6kAutomated safety check: PassNone
Contributor-First PR MergeHKUDS/OpenHarness16k1 repos~847Automated safety check: PassMIT
Migrate Internal Package into GhostTryGhost/Ghost56k—~3.8kAutomated safety check: PassMIT
Create Pull Requestcline/cline70k1 repos~1.6kAutomated safety check: PassApache-2.0

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Categories

Questions about Repo Activity Summary

What does Repo Activity Summary do?

Summarize a repository's recent engineering activity from git history — technologies, work types, churn hotspots, contributor patterns, and velocity. Repo Activity Summary is an agent skill from CodeAlive-AI/ai-driven-development. Summarize a repository's recent engineering activity from git history — technologies, work types, churn hotspots, contributor patterns, and velocity.

When should I use Repo Activity Summary?

Repo Activity Summary fits situations like: asking what has this repo been working on; is this project active; who contributes what; where are the hotspots.

How do I install Repo Activity Summary in Claude Code?

Run `npx skills add CodeAlive-AI/ai-driven-development --skill repo-activity-summary -a claude-code`. Or copy the skill folder (skills/repo-activity-summary in CodeAlive-AI/ai-driven-development) into .claude/skills/repo-activity-summary in your project. Claude Code loads it when a task matches its description.

How do I install Repo Activity Summary in Codex?

Run `npx skills add CodeAlive-AI/ai-driven-development --skill repo-activity-summary -a codex`. Or copy the skill folder (skills/repo-activity-summary in CodeAlive-AI/ai-driven-development) into .agents/skills/repo-activity-summary in your project. Codex loads it when a task matches its description.

Can I use Repo Activity Summary in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add CodeAlive-AI/ai-driven-development --skill repo-activity-summary -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/repo-activity-summary, .gemini/skills/repo-activity-summary, .github/skills/repo-activity-summary and .opencode/skills/repo-activity-summary in your project.

What does Repo Activity Summary need to run?

Going by SKILL.md and its folder, Repo Activity Summary needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and git). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash(git:*), Bash(python3:*), Read. Compatibility (from SKILL.md): Any coding agent with shell access. Requires git and Python 3.9+. No API keys or network access needed..

Does Repo Activity Summary access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Repo Activity Summary safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Repo Activity Summary use?

Repo Activity Summary is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Repo Activity Summary use?

About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Repo Activity Summary?

Skills that share tags, products or a category with Repo Activity Summary: Finishing a Development Branch (obra/superpowers, 297k stars), Code Design Rationale Investigator (cursor/plugins, 10k stars), Contributor-First PR Merge (HKUDS/OpenHarness, 16k stars) and Migrate Internal Package into Ghost (TryGhost/Ghost, 56k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Repo Activity Summary?

CodeAlive-AI (a GitHub organization) maintains it in CodeAlive-AI/ai-driven-development, which has 158 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 8, 2026.

Source: CodeAlive-AI/ai-driven-development on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.