Transform ephemeral session learnings into permanent, compounding capabilities.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncompound-learningsExecute the skills CLI command in your project's root directory to begin installation:
Fetches compound-learnings from parcadei/continuous-claude-v3 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate compound-learnings. Access via /compound-learnings in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Transform ephemeral session learnings into permanent, compounding capabilities.
# List learnings (most recent first)
ls -t $CLAUDE_PROJECT_DIR/.claude/cache/learnings/*.md | head -20
# Count total
ls $CLAUDE_PROJECT_DIR/.claude/cache/learnings/*.md | wc -l
Read the most recent 5-10 files (or specify a date range).
For each learnings file, extract entries from these specific sections:
| Section Header | What to Extract |
|---|---|
## Patterns or Reusable techniques |
Direct candidates for rules |
**Takeaway:** or **Actionable takeaway:** |
Decision heuristics |
## What Worked |
Success patterns |
## What Failed |
Anti-patterns (invert to rules) |
## Key Decisions |
Design principles |
Build a frequency table as you go:
| Pattern | Sessions | Category |
|---------|----------|----------|
| "Check artifacts before editing" | abc, def, ghi | debugging |
| "Pass IDs explicitly" | abc, def, ghi, jkl | reliability |
Before counting, merge patterns that express the same principle:
Example consolidation:
Use the most general formulation. Update the frequency table.
Critical step: Look at what the learnings cluster around.
If >50% of patterns relate to one topic (e.g., "hooks", "tracing", "async"): → That topic may need a dedicated skill rather than multiple rules → One skill compounds better than five rules
Ask yourself: "Is there a skill that would make all these rules unnecessary?"
For each pattern, determine artifact type:
Is it a sequence of commands/steps?
→ YES → SKILL (executable > declarative)
→ NO ↓
Should it run automatically on an event (SessionEnd, PostToolUse, etc.)?
→ YES → HOOK (automatic > manual)
→ NO ↓
Is it "when X, do Y" or "never do X"?
→ YES → RULE
→ NO ↓
Does it enhance an existing agent workflow?
→ YES → AGENT UPDATE
→ NO → Skip (not worth capturing)
Artifact Type Examples:
| Pattern | Type | Why |
|---|---|---|
| "Run linting before commit" | Hook (PreToolUse) | Automatic gate |
| "Extract learnings on session end" | Hook (SessionEnd) | Automatic trigger |
| "Debug hooks step by step" | Skill | Manual sequence |
| "Always pass IDs explicitly" | Rule | Heuristic |
| Occurrences | Action |
|---|---|
| 1 | Note but skip (unless critical failure) |
| 2 | Consider - present to user |
| 3+ | Strong signal - recommend creation |
| 4+ | Definitely create |
Present each proposal in this format:
---
## Pattern: [Generalized Name]
**Signal:** [N] sessions ([list session IDs])
**Category:** [debugging / reliability / workflow / etc.]
**Artifact Type:** Rule / Skill / Agent Update
**Rationale:** [Why this artifact type, why worth creating]
**Draft Content:**
\`\`\`markdown
[Actual content that would be written to file]
\`\`\`
**File:** `.claude/rules/[name].md` or `.claude/skills/[name]/SKILL.md`
---
Use AskUserQuestion to get approval for each artifact (or batch approval).
# Write to rules directory
cat > $CLAUDE_PROJECT_DIR/.claude/rules/<name>.md << 'EOF'
# Rule Name
[Context: why this rule exists, based on N sessions]
## Pattern
[The reusable principle]
## DO
- [Concrete action]
## DON'T
- [Anti-pattern]
## Source Sessions
- [session-id-1]: [what happened]
- [session-id-2]: [what happened]
EOF
Create .claude/skills/<name>/SKILL.md with:
Add triggers to skill-rules.json if appropriate.
Create shell wrapper + TypeScript handler:
# Shell wrapper
cat > $CLAUDE_PROJECT_DIR/.claude/hooks/<name>.sh << 'EOF'
#!/bin/bash
set -e
cd "$CLAUDE_PROJECT_DIR/.claude/hooks"
cat | node dist/<name>.mjs
EOF
chmod +x $CLAUDE_PROJECT_DIR/.claude/hooks/<name>.sh
Then create src/<name>.ts, build with esbuild, and register in settings.json:
{
"hooks": {
"EventName": [{
"hooks": [{
"type": "command",
"command": "$CLAUDE_PROJECT_DIR/.claude/hooks/<name>.sh"
}]
}]
}
}
Edit existing agent in .claude/agents/<name>.md to add the learned capability.
## Compounding Complete
**Learnings Analyzed:** [N] sessions
**Patterns Found:** [M]
**Artifacts Created:** [K]
### Created:
- Rule: `explicit-identity.md` - Pass IDs explicitly across boundaries
- Skill: `debug-hooks` - Hook debugging workflow
### Skipped (insufficient signal):
- "Pattern X" (1 occurrence)
**Your setup is now permanently improved.**
Before creating any artifact:
.claude/rules/ and .claude/skills/ first.claude/cache/learnings/*.md.claude/skills/<name>/SKILL.md.claude/rules/<name>.md.claude/hooks/<name>.sh + src/<name>.ts + dist/<name>.mjs.claude/agents/<name>.md.claude/skills/skill-rules.json.claude/settings.json → hooks sectionMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
parcadei/continuous-claude-v3
mattpocock/skills
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
I recommend compound-learnings for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in compound-learnings — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: compound-learnings is the kind of skill you can hand to a new teammate without a long onboarding doc.
compound-learnings is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in compound-learnings — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
compound-learnings has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: compound-learnings is the kind of skill you can hand to a new teammate without a long onboarding doc.
compound-learnings is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend compound-learnings for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend compound-learnings for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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