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retrospective
// After completing a significant task or experiment, extract lessons learned and update the project knowledge base. Captures what worked, what failed, and what to remember for next time.
// After completing a significant task or experiment, extract lessons learned and update the project knowledge base. Captures what worked, what failed, and what to remember for next time.
| name | retrospective |
| description | After completing a significant task or experiment, extract lessons learned and update the project knowledge base. Captures what worked, what failed, and what to remember for next time. |
| argument-hint | ["task-description"] |
| disable-model-invocation | true |
| allowed-tools | Read, Edit, Write, Bash, Grep, Glob |
After completing "$ARGUMENTS", extract insights and update the project knowledge base.
Gather context: Read recent changes, results, and any errors encountered during the task. Look at:
git log --oneline -10git diff --name-only HEAD~5logs/Extract lessons in these categories:
What worked โ approaches, tools, or patterns that were effective What failed โ approaches that were tried and didn't work, with WHY Surprises โ unexpected findings or behaviors For next time โ specific recommendations for similar future tasks
Update knowledge base:
If there are failed approaches: Append to docs/failures.md following the existing format:
### Title
**When**: Date
**What happened**: Description
**Root cause**: Why it failed
**Fix/workaround**: What to do instead
If there's a decision: Append to docs/decisions.jsonl using the established schema.
If there are experiment findings: Consider whether docs/findings.md needs updating.
Summarize the retrospective in 3-5 bullet points for the user.
Search past decisions, failures, and experiment logs for relevant context before starting a task. Use this before any significant implementation or experiment to avoid repeating mistakes.
Analyze sweep results after completion. Computes accuracy, CFR, signal statistics, and generates comparison tables. Use after a sweep completes to understand the data.
Compare results across models (Qwen vs Llama) at matching compression configurations. Generates side-by-side tables and identifies cross-model patterns.
Prepare and run a KV-cache compression sweep. Loads sweep configuration, validates prerequisites, and provides the exact commands needed. Use before starting any GPU experiment.