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skill-reinforcement

Always and Automatically improve skills after each use by capturing learnings and anti-patterns

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different-ai/agent-bank
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2026년 1월 13일 21:51
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SKILL.md
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name
skill-reinforcement
description
Always and Automatically improve skills after each use by capturing learnings and anti-patterns
license
MIT
compatibility
opencode
metadata
{"audience":"ai-agents","workflow":"meta-learning","trigger":"after-any-skill-use"}
## What I Do After ANY skill is used, this meta-skill triggers to: 1. Analyze what worked and what didn't 2. Identify new patterns or shortcuts discovered 3. Update the skill file with learnings 4. Prevent knowledge loss between sessions **Relationship with `self-improve`:** - This skill (`skill-reinforcement`) = **WHEN** to update (post-use triggers) - The `self-improve` skill = **HOW** to update (templates, structures, decision trees) ## When to Trigger **Invoke this skill automatically when:** - Any skill from `.opencode/skill/*/SKILL.md` completes - A workflow succeeds or fails in a notable way - New shortcuts or anti-patterns are discovered - Token usage could be reduced with better patterns - API behavior differs from documentation - Commands fail and I find the fix - User confirms something works - I do the same task twice (should become a skill/tool) ## Reinforcement Process ### Step 1: Capture the Context After using a skill, note: ``` - Skill used: [skill-name] - Task: [what was being done] - Outcome: [success/partial/failure] - Token cost: [high/medium/low] - Time taken: [fast/normal/slow] ``` ### Step 2: Identify Learnings Ask these questions: 1. **What took longer than expected?** → Document the fix 2. **What failed unexpectedly?** → Add to "Common Issues" 3. **What shortcut was discovered?** → Add to "Token Saving Tips" 4. **What assumption was wrong?** → Correct in documentation 5. **What worked better than documented?** → Update the workflow ### Step 3: Categorize the Learning | Category | Where to Add | Example | | ----------------- | -------------------------- | ---------------------------- | | New shortcut | "Token Saving Tips" | OTP visible in email preview | | Failure mode | "Common Issues" | Popup blocker breaks flow | | Better pattern | Main workflow | Check login state first | | Anti-pattern | "Anti-Patterns to Avoid" | Don't snapshot spam | | Environment quirk | "Prerequisites" or "Notes" | Session persists | ### Step 4: Update the Skill File ```bash # Read current skill cat .opencode/skill/[skill-name]/SKILL.md # Edit to add learning in appropriate section # Use the Edit tool to append or modify ``` ### Step 5: Validate the Update Ensure updates are: - **Actionable** - Not vague observations - **Specific** - Include exact commands/patterns - **Formatted** - Match existing style - **Non-redundant** - Don't duplicate existing content ## Learning Templates ### For New Shortcuts ```markdown ### [Shortcut Name] **Discovery**: [How it was found] **Before**: [Old approach] **After**: [New approach] **Savings**: [Token/time reduction] ``` ### For Failure Modes ```markdown | Issue | Symptom | Fix | | ------ | -------------- | ---------------- | | [Name] | [What you see] | [How to resolve] | ``` ### For Anti-Patterns ````markdown ### Don't: [Bad Pattern Name] ```javascript // BAD - [explanation] [bad code] ``` ```` ### Do: [Good Pattern Name] ```javascript // GOOD - [explanation] [good code] ``` ```` ### For Workflow Improvements ```markdown ## Updated: [Section Name] [New content that replaces or augments existing] > **Note**: Updated [date] after discovering [context] ```` ## Real Examples ### Example 1: Session Persistence Discovery **Context**: Testing staging branch, went through full login flow **Learning**: Chrome MCP persists sessions - was already logged in **Action**: Added "Session Persistence is Your Friend" section with check-first pattern ### Example 2: OTP Extraction Optimization **Context**: Opened email, waited for load, extracted OTP **Learning**: OTP visible in Gmail list preview without opening email **Action**: Updated OTP section with faster "search + list preview" method ### Example 3: Deployment URL Pattern **Context**: Manually constructed URL, got it wrong **Learning**: Can extract URL directly from Vercel bot's PR comment **Action**: Added `gh pr view` command to get URL automatically ## Skill File Structure Convention Every skill should have these sections (add if missing): ```markdown ## What I Do [Core purpose] ## Prerequisites [Requirements] ## Workflow [Main steps] ## Common Issues [Failure modes and fixes] ## Token Saving Tips [Efficiency patterns] ## Anti-Patterns to Avoid [What NOT to do] ## Real Examples [Actual usage examples] ## Learnings Log [Append-only log of discoveries - optional] ``` ## Integration with Other Skills When reinforcing a skill, check if learnings apply to related skills: | Skill | Related Skills | | ------------------- | ------------------------------- | | test-staging-branch | Any Chrome MCP skill | | skill-reinforcement | All skills (meta) | | self-improve | skill-reinforcement (companion) | | chrome-devtools-mcp | test-staging-branch, gmail | | safe-infrastructure | new-vault-implementation | Cross-pollinate learnings when applicable. ### Deciding What to Create If reinforcement reveals a need for new capability, use the `self-improve` skill's decision tree: ``` Need to extend capabilities? │ ├─ Just need docs/commands? → SKILL ├─ Need specialized AI persona? → AGENT ├─ Need event hooks? → PLUGIN ├─ Need callable function? → TOOL └─ Need external integration? → MCP SERVER ``` ## Automation Hooks ### Post-Skill Trigger After completing any skill, automatically ask: 1. "Did anything unexpected happen?" 2. "Was there a faster way to do this?" 3. "What would I do differently next time?" If answers exist, invoke skill-reinforcement. ### Periodic Review Every ~10 skill uses, review: - Most frequently used skills (prioritize improvements) - Skills with most "Common Issues" (need better documentation) - Skills with outdated information (need refresh) ## Meta: Reinforcing This Skill This skill should also improve itself. Track: - How often it triggers - Quality of captured learnings - Whether skills actually improve over time - Time cost of reinforcement vs value gained ## Quick Reinforcement Checklist ``` [ ] Skill completed [ ] Outcome noted (success/fail/partial) [ ] Any surprises? → Document [ ] Any shortcuts found? → Add to tips [ ] Any failures? → Add to issues [ ] Could be faster? → Add anti-pattern [ ] Update skill file [ ] Validate formatting [ ] Done ``` ## Immediate Update Rule **UPDATE IMMEDIATELY** - Don't wait until end of conversation. When any of these happen, stop and update the relevant skill: 1. API format is wrong (like `-d` vs `-F` for curl) 2. Commands fail and I find the fix 3. User confirms something works 4. I discover a better/faster way 5. Something is missing from docs Example: ``` ❌ "I'll note this for later" ✅ *immediately edits skill file* ``` ## Learnings Log - 2026-01-12: Bulk remote branch cleanup can hit stale refs and timeouts; run `git fetch --prune origin` first, delete with a loop that tolerates missing refs, then prune again to verify only `origin/main` remains. - 2026-01-13: When extending agent policy docs, renumber numbered headings after inserts and place testing tools + real-funds protocol near the Testability section for clarity. - 2026-01-13: When refactoring MCP tool handlers into a registry, remove the legacy switch and align handler arg types with tool schemas to avoid type errors. - 2026-01-13: When adding a new Next.js route handler export, ensure it sits outside existing handler functions to avoid "Modifiers cannot appear here" errors.
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