Add skills, learnings & memory updates (2026-01-26)
- New skills: clawddocs, claude-code-usage, summarize, homeassistant, humanizer, self-improving-agent - Add .learnings/ for self-improvement tracking - Document proaktive cron config (LRN-20260126-001) - Update USER.md: Löchgau as former residence - Update TOOLS.md: Peekaboo workaround - Memory files for 2026-01-25 and 2026-01-26
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skills/self-improving-agent/SKILL.md
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skills/self-improving-agent/SKILL.md
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---
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name: self-improvement
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description: "Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks."
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---
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# Self-Improvement Skill
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Log learnings and errors to markdown files for continuous improvement. Coding agents can later process these into fixes, and important learnings get promoted to project memory.
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## Quick Reference
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| Situation | Action |
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|-----------|--------|
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| Command/operation fails | Log to `.learnings/ERRORS.md` |
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| User corrects you | Log to `.learnings/LEARNINGS.md` with category `correction` |
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| User wants missing feature | Log to `.learnings/FEATURE_REQUESTS.md` |
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| API/external tool fails | Log to `.learnings/ERRORS.md` with integration details |
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| Knowledge was outdated | Log to `.learnings/LEARNINGS.md` with category `knowledge_gap` |
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| Found better approach | Log to `.learnings/LEARNINGS.md` with category `best_practice` |
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| Similar to existing entry | Link with `**See Also**`, consider priority bump |
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| Broadly applicable learning | Promote to `CLAUDE.md`, `AGENTS.md`, and/or `.github/copilot-instructions.md` |
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## Setup
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Create `.learnings/` directory in project root if it doesn't exist:
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```bash
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mkdir -p .learnings
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```
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Copy templates from `assets/` or create files with headers.
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## Logging Format
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### Learning Entry
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Append to `.learnings/LEARNINGS.md`:
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```markdown
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## [LRN-YYYYMMDD-XXX] category
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**Logged**: ISO-8601 timestamp
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**Priority**: low | medium | high | critical
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**Status**: pending
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**Area**: frontend | backend | infra | tests | docs | config
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### Summary
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One-line description of what was learned
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### Details
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Full context: what happened, what was wrong, what's correct
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### Suggested Action
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Specific fix or improvement to make
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### Metadata
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- Source: conversation | error | user_feedback
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- Related Files: path/to/file.ext
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- Tags: tag1, tag2
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- See Also: LRN-20250110-001 (if related to existing entry)
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---
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```
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### Error Entry
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Append to `.learnings/ERRORS.md`:
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```markdown
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## [ERR-YYYYMMDD-XXX] skill_or_command_name
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**Logged**: ISO-8601 timestamp
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**Priority**: high
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**Status**: pending
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**Area**: frontend | backend | infra | tests | docs | config
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### Summary
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Brief description of what failed
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### Error
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```
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Actual error message or output
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```
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### Context
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- Command/operation attempted
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- Input or parameters used
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- Environment details if relevant
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### Suggested Fix
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If identifiable, what might resolve this
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### Metadata
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- Reproducible: yes | no | unknown
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- Related Files: path/to/file.ext
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- See Also: ERR-20250110-001 (if recurring)
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---
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```
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### Feature Request Entry
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Append to `.learnings/FEATURE_REQUESTS.md`:
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```markdown
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## [FEAT-YYYYMMDD-XXX] capability_name
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**Logged**: ISO-8601 timestamp
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**Priority**: medium
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**Status**: pending
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**Area**: frontend | backend | infra | tests | docs | config
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### Requested Capability
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What the user wanted to do
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### User Context
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Why they needed it, what problem they're solving
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### Complexity Estimate
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simple | medium | complex
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### Suggested Implementation
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How this could be built, what it might extend
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### Metadata
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- Frequency: first_time | recurring
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- Related Features: existing_feature_name
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---
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```
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## ID Generation
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Format: `TYPE-YYYYMMDD-XXX`
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- TYPE: `LRN` (learning), `ERR` (error), `FEAT` (feature)
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- YYYYMMDD: Current date
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- XXX: Sequential number or random 3 chars (e.g., `001`, `A7B`)
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Examples: `LRN-20250115-001`, `ERR-20250115-A3F`, `FEAT-20250115-002`
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## Resolving Entries
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When an issue is fixed, update the entry:
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1. Change `**Status**: pending` → `**Status**: resolved`
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2. Add resolution block after Metadata:
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```markdown
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### Resolution
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- **Resolved**: 2025-01-16T09:00:00Z
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- **Commit/PR**: abc123 or #42
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- **Notes**: Brief description of what was done
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```
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Other status values:
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- `in_progress` - Actively being worked on
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- `wont_fix` - Decided not to address (add reason in Resolution notes)
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- `promoted` - Elevated to CLAUDE.md, AGENTS.md, or .github/copilot-instructions.md
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## Promoting to Project Memory
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When a learning is broadly applicable (not a one-off fix), promote it to permanent project memory.
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### When to Promote
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- Learning applies across multiple files/features
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- Knowledge any contributor (human or AI) should know
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- Prevents recurring mistakes
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- Documents project-specific conventions
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### Promotion Targets
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| Target | What Belongs There |
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|--------|-------------------|
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| `CLAUDE.md` | Project facts, conventions, gotchas for all Claude interactions |
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| `AGENTS.md` | Agent-specific workflows, tool usage patterns, automation rules |
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| `.github/copilot-instructions.md` | Project context and conventions for GitHub Copilot |
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### How to Promote
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1. **Distill** the learning into a concise rule or fact
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2. **Add** to appropriate section in target file (create file if needed)
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3. **Update** original entry:
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- Change `**Status**: pending` → `**Status**: promoted`
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- Add `**Promoted**: CLAUDE.md`, `AGENTS.md`, or `.github/copilot-instructions.md`
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### Promotion Examples
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**Learning** (verbose):
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> Project uses pnpm workspaces. Attempted `npm install` but failed.
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> Lock file is `pnpm-lock.yaml`. Must use `pnpm install`.
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**In CLAUDE.md** (concise):
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```markdown
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## Build & Dependencies
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- Package manager: pnpm (not npm) - use `pnpm install`
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```
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**Learning** (verbose):
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> When modifying API endpoints, must regenerate TypeScript client.
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> Forgetting this causes type mismatches at runtime.
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**In AGENTS.md** (actionable):
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```markdown
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## After API Changes
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1. Regenerate client: `pnpm run generate:api`
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2. Check for type errors: `pnpm tsc --noEmit`
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```
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## Recurring Pattern Detection
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If logging something similar to an existing entry:
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1. **Search first**: `grep -r "keyword" .learnings/`
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2. **Link entries**: Add `**See Also**: ERR-20250110-001` in Metadata
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3. **Bump priority** if issue keeps recurring
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4. **Consider systemic fix**: Recurring issues often indicate:
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- Missing documentation (→ promote to CLAUDE.md or .github/copilot-instructions.md)
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- Missing automation (→ add to AGENTS.md)
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- Architectural problem (→ create tech debt ticket)
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## Periodic Review
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Review `.learnings/` at natural breakpoints:
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### When to Review
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- Before starting a new major task
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- After completing a feature
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- When working in an area with past learnings
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- Weekly during active development
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### Quick Status Check
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```bash
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# Count pending items
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grep -h "Status\*\*: pending" .learnings/*.md | wc -l
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# List pending high-priority items
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grep -B5 "Priority\*\*: high" .learnings/*.md | grep "^## \["
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# Find learnings for a specific area
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grep -l "Area\*\*: backend" .learnings/*.md
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```
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### Review Actions
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- Resolve fixed items
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- Promote applicable learnings
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- Link related entries
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- Escalate recurring issues
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## Detection Triggers
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Automatically log when you notice:
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**Corrections** (→ learning with `correction` category):
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- "No, that's not right..."
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- "Actually, it should be..."
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- "You're wrong about..."
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- "That's outdated..."
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**Feature Requests** (→ feature request):
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- "Can you also..."
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- "I wish you could..."
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- "Is there a way to..."
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- "Why can't you..."
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**Knowledge Gaps** (→ learning with `knowledge_gap` category):
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- User provides information you didn't know
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- Documentation you referenced is outdated
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- API behavior differs from your understanding
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**Errors** (→ error entry):
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- Command returns non-zero exit code
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- Exception or stack trace
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- Unexpected output or behavior
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- Timeout or connection failure
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## Priority Guidelines
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| Priority | When to Use |
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|----------|-------------|
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| `critical` | Blocks core functionality, data loss risk, security issue |
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| `high` | Significant impact, affects common workflows, recurring issue |
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| `medium` | Moderate impact, workaround exists |
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| `low` | Minor inconvenience, edge case, nice-to-have |
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## Area Tags
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Use to filter learnings by codebase region:
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| Area | Scope |
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|------|-------|
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| `frontend` | UI, components, client-side code |
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| `backend` | API, services, server-side code |
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| `infra` | CI/CD, deployment, Docker, cloud |
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| `tests` | Test files, testing utilities, coverage |
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| `docs` | Documentation, comments, READMEs |
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| `config` | Configuration files, environment, settings |
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## Best Practices
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1. **Log immediately** - context is freshest right after the issue
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2. **Be specific** - future agents need to understand quickly
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3. **Include reproduction steps** - especially for errors
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4. **Link related files** - makes fixes easier
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5. **Suggest concrete fixes** - not just "investigate"
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6. **Use consistent categories** - enables filtering
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7. **Promote aggressively** - if in doubt, add to CLAUDE.md or .github/copilot-instructions.md
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8. **Review regularly** - stale learnings lose value
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## Gitignore Options
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**Keep learnings local** (per-developer):
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```gitignore
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.learnings/
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```
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**Track learnings in repo** (team-wide):
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Don't add to .gitignore - learnings become shared knowledge.
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**Hybrid** (track templates, ignore entries):
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```gitignore
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.learnings/*.md
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!.learnings/.gitkeep
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```
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## Hook Integration
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Enable automatic reminders through agent hooks. This is **opt-in** - you must explicitly configure hooks.
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### Quick Setup (Claude Code / Codex)
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Create `.claude/settings.json` in your project:
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```json
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{
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"hooks": {
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"UserPromptSubmit": [{
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"matcher": "",
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"hooks": [{
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"type": "command",
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"command": "./skills/self-improvement/scripts/activator.sh"
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}]
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}]
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}
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}
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```
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This injects a learning evaluation reminder after each prompt (~50-100 tokens overhead).
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### Full Setup (With Error Detection)
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```json
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{
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"hooks": {
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"UserPromptSubmit": [{
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"matcher": "",
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"hooks": [{
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"type": "command",
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"command": "./skills/self-improvement/scripts/activator.sh"
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}]
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}],
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"PostToolUse": [{
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"matcher": "Bash",
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"hooks": [{
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"type": "command",
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"command": "./skills/self-improvement/scripts/error-detector.sh"
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}]
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}]
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}
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}
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```
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### Available Hook Scripts
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| Script | Hook Type | Purpose |
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|--------|-----------|---------|
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| `scripts/activator.sh` | UserPromptSubmit | Reminds to evaluate learnings after tasks |
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| `scripts/error-detector.sh` | PostToolUse (Bash) | Triggers on command errors |
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See `references/hooks-setup.md` for detailed configuration and troubleshooting.
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## Automatic Skill Extraction
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When a learning is valuable enough to become a reusable skill, extract it using the provided helper.
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### Skill Extraction Criteria
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A learning qualifies for skill extraction when ANY of these apply:
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| Criterion | Description |
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|-----------|-------------|
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| **Recurring** | Has `See Also` links to 2+ similar issues |
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| **Verified** | Status is `resolved` with working fix |
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| **Non-obvious** | Required actual debugging/investigation to discover |
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| **Broadly applicable** | Not project-specific; useful across codebases |
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| **User-flagged** | User says "save this as a skill" or similar |
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### Extraction Workflow
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1. **Identify candidate**: Learning meets extraction criteria
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2. **Run helper** (or create manually):
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```bash
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./skills/self-improvement/scripts/extract-skill.sh skill-name --dry-run
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./skills/self-improvement/scripts/extract-skill.sh skill-name
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```
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3. **Customize SKILL.md**: Fill in template with learning content
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4. **Update learning**: Set status to `promoted_to_skill`, add `Skill-Path`
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5. **Verify**: Read skill in fresh session to ensure it's self-contained
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### Manual Extraction
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If you prefer manual creation:
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1. Create `skills/<skill-name>/SKILL.md`
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2. Use template from `assets/SKILL-TEMPLATE.md`
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3. Follow [Agent Skills spec](https://agentskills.io/specification):
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- YAML frontmatter with `name` and `description`
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- Name must match folder name
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- No README.md inside skill folder
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### Extraction Detection Triggers
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Watch for these signals that a learning should become a skill:
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**In conversation:**
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- "Save this as a skill"
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- "I keep running into this"
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- "This would be useful for other projects"
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- "Remember this pattern"
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**In learning entries:**
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- Multiple `See Also` links (recurring issue)
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- High priority + resolved status
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- Category: `best_practice` with broad applicability
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- User feedback praising the solution
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### Skill Quality Gates
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Before extraction, verify:
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- [ ] Solution is tested and working
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- [ ] Description is clear without original context
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- [ ] Code examples are self-contained
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- [ ] No project-specific hardcoded values
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- [ ] Follows skill naming conventions (lowercase, hyphens)
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## Multi-Agent Support
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This skill works across different AI coding agents with agent-specific activation.
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### Claude Code
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**Activation**: Hooks (UserPromptSubmit, PostToolUse)
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**Setup**: `.claude/settings.json` with hook configuration
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**Detection**: Automatic via hook scripts
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### Codex CLI
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**Activation**: Hooks (same pattern as Claude Code)
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**Setup**: `.codex/settings.json` with hook configuration
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**Detection**: Automatic via hook scripts
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### GitHub Copilot
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**Activation**: Manual (no hook support)
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**Setup**: Add to `.github/copilot-instructions.md`:
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```markdown
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## Self-Improvement
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After solving non-obvious issues, consider logging to `.learnings/`:
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1. Use format from self-improvement skill
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2. Link related entries with See Also
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3. Promote high-value learnings to skills
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Ask in chat: "Should I log this as a learning?"
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```
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**Detection**: Manual review at session end
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### Agent-Agnostic Guidance
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Regardless of agent, apply self-improvement when you:
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1. **Discover something non-obvious** - solution wasn't immediate
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2. **Correct yourself** - initial approach was wrong
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3. **Learn project conventions** - discovered undocumented patterns
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4. **Hit unexpected errors** - especially if diagnosis was difficult
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5. **Find better approaches** - improved on your original solution
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### Copilot Chat Integration
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For Copilot users, add this to your prompts when relevant:
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> After completing this task, evaluate if any learnings should be logged to `.learnings/` using the self-improvement skill format.
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Or use quick prompts:
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- "Log this to learnings"
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- "Create a skill from this solution"
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- "Check .learnings/ for related issues"
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Reference in New Issue
Block a user