sheal-retro
Skill0 starsChanged 7 months agoMITView on GitHub
Run sheal retrospective to extract learnings from the current session. Reviews what worked, what failed, and bridges learnings to JSONL.
- Installs packages
In this repository22 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- aif-analyzeSkill
- aif-enhanceSkill
- aif-evolveSkill
- aif-feedbackSkill
- aif-ingestSkill
- aif-learnSkill
- aif-pulseSkill
- aiframework-learnSkill
- aiframework-reviewSkill
- aiframework-shipSkill
- aif-readySkill
- aif-researchSkill
- aif-reviewSkill
- aif-shipSkill
- aif-statusSkill
- retroSkill
- sheal-askSkill
- sheal-checkSkill
- sheal-driftSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 55
If significant insights were found:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
/sheal-retro — Session Retrospective
Prerequisites
command -v sheal >/dev/null 2>&1 && sheal --version 2>/dev/null || echo "sheal not installed — run: npm install -g @liwala/sheal"Step 0: Telemetry (silent, non-blocking)
aiframework-telemetry skill_invoked skill=sheal-retro 2>/dev/null || trueStep 1: Run Retrospective
sheal retro --project . 2>/dev/null || echo "sheal retro failed — check installation"Step 2: Review Learnings
sheal learn list --project . 2>/dev/null | head -30Review the extracted learnings for:
- Failure loops (repeated errors)
- Missing context (things the agent didn't know)
- Workflow improvements (better approaches discovered)
Step 3: Bridge to aiframework
Sync sheal learnings to aiframework JSONL format:
source lib/bridge/sheal_learnings.sh 2>/dev/null && bridge_sheal_to_jsonl . || echo "Bridge not available"Step 4: Update Vault
If significant insights were found:
source lib/bridge/sheal_learnings.sh 2>/dev/null && bridge_retros_to_vault . || echo "Vault bridge not available"Step 5: Report
Output summary:
- New learnings extracted: N
- Failure loops detected: N
- Learnings synced to JSONL: N
- Vault updated: yes/no
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

