An automated engineering retrospective skill that transforms git history into actionable delivery reviews and team growth insights.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-retro
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install openclaw-retro using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Openclaw Retro?
Openclaw Retro is a specialized engineering retrospective skill designed for modern development teams. By analyzing git history, code quality metrics, and contribution patterns, it provides a comprehensive view of a team's delivery performance over 24-hour, 7-day, 14-day, or 30-day cycles. This skill is part of the broader ecosystem of Openclaw Skills, helping developers move beyond simple commit logs to deep, data-driven insights.
The tool identifies deep work sessions, commit distributions, and hotspots, ensuring that retrospectives are grounded in technical truth rather than anecdotal evidence. It effectively bridges the gap between raw code changes and high-level project management, making it an essential addition to any developer's toolkit.
Openclaw Retro Use Cases
- Generating weekly delivery reports for engineering managers and team leads to track velocity.
- Identifying high-risk code hotspots and technical debt accumulation within the repository.
- Analyzing individual contributor growth and providing data-backed feedback during performance reviews.
- Tracking project focus and context-switching through Focus Scores during critical development sprints.
- Automating the maintenance of TODOS.md by cross-referencing git diffs with backlog items.
How Openclaw Retro Works
- The user triggers the retrospective using the /retro command with an optional time window like 24h, 7d, 14d, or 30d.
- The skill fetches the latest origin data and identifies the current user versus teammates via local git configuration.
- A series of parallel git commands are executed to extract commit logs, timestamps, file changes, and LOC metrics for the selected window.
- The system analyzes contribution patterns to detect work sessions and classify them as Deep, Medium, or Micro sessions based on commit intervals.
- Data is synthesized into a visual report featuring a metrics summary table, contributor leaderboard, and hourly activity histogram.
- A qualitative analysis is performed to provide specific highlights and growth opportunities for every team member based on their actual code changes.
Openclaw Retro Setup
To start using this Openclaw Skills enhancement, ensure you have a git repository initialized and the skill installed in your agent environment. No complex API keys are required as it leverages your local git environment.
# Trigger a standard 7-day retrospective
/retro
# Trigger a monthly deep-dive review
/retro 30d
# Check the last 24 hours of activity
/retro 24h
Openclaw Retro Data Schema & Taxonomy
The skill processes raw git objects and local metadata files to generate a structured report. It organizes data into the following taxonomy:
| Data Category | Sources | Metadata Extracted |
|---|---|---|
| Delivery Metrics | git log, git shortlog | Commits, PRs, LOC, Test Ratio |
| Activity Trends | Timestamp logs | Hourly distribution, Session density |
| Code Quality | git numstat, find | Test file ratio, Production vs Test changes |
| Documentation | TODOS.md, CHANGELOG | Backlog health, Versioning discipline |
Openclaw Retro Advanced Features
- Intelligent Session Detection: Groups commits within 45-minute intervals to identify deep work patterns versus quick fixes.
- Focus Score Calculation: Quantifies context-switching by analyzing the density of changes across top-level directories.
- Automated Anti-Spam Feedback: Ensures growth recommendations are derived from specific architectural improvements rather than generic AI praise.
- Backlog Cross-Referencing: Automatically validates completed tasks in TODOS.md against actual code diffs to ensure accuracy.
- Midnight-Aligned Windows: Ensures that daily and weekly reports are perfectly aligned with local calendar days for consistent reporting.
SKILL.md
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