ai-retrospective
AI Collaboration Retrospective — a tool-agnostic post-session analysis framework. After each AI-assisted coding/development session, it systematically reviews the entire conversation across eight dimensions to identify improvement opportunities and generate a structured retrospective report. Core goal: make every AI session better than the last.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/amoshc/ai-retrospective-skillAI Collaboration Retrospective
Post-session systematic review tool. Eight-dimension deep analysis drives a continuous improvement loop for AI-assisted development.
Core Principles
- Conversation context is the data source: The complete conversation history of the current session is already in context — no external data fetching needed
- Progressive loading: Detailed evaluation criteria live in
references/analysis_dimensions.md— load on demand - Self-reflection first: Examine the AI's own shortcomings before analyzing user-side improvements. This is NOT about criticizing the user — it's about finding efficiency gains in the "AI + Human" collaboration
- Quantify everything: Every finding must reference specific conversation turns, wasted operations, and include counterfactual reasoning ("If X had been done, Y turns could have been saved")
- Dig deep: Don't settle for "no findings." Complete the self-check list for each dimension before declaring it clean
Execution Model
This skill is pure LLM instruction-driven — no scripts, no external dependencies. It works on any AI assistant that can:
- Access the current conversation history
- Read reference files from this skill's directory
- Write output files to the workspace
Capability adaptation: The workflow below references file operations and memory updates. If your AI tool doesn't support a specific capability, skip that step and note it in the report. The analysis itself only requires conversation context access.
Workflow (Six Steps)
Step 1: Conversation Review — Extract Key Events + Tag Waste Points
Scan the entire conversation context and extract these key events into a timeline:
| Event Type | Recognition Signal |
|---|---|
| Tool invocations | Command execution, file reading/writing, web searches, code generation |
| File changes | Files created, modified, or deleted |
| Errors & fixes | Error messages, lint failures, debugging cycles |
| Repeated modifications | Same file/feature modified multiple times, user providing multiple clarifications |
| Decision points | Technology choices, architecture decisions, trade-offs |
| Automation/plugin usage | Any skill, agent, plugin, or extension triggered during the session |
| User clarifications | User adding context because the AI misunderstood intent |
| Verification rounds | User providing test data/feedback, AI analyzing verification results |
| AI misjudgments | AI providing wrong conclusions, missing critical issues, or jumping to premature conclusions |
Filter rule: System initialization events (bootstrap files, identity setup, etc.) are excluded from analysis.
Critical step — Waste point tagging:
After building the timeline, interrogate each event in reverse:
- Could this step have been avoided? If something had been done earlier, would this step be unnecessary?
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-amoshc-ai-retrospective-skill": {
"enabled": true,
"auto_update": true
}
}
}