compound-learnings

parcadei/continuous-claude-v3 · updated Apr 8, 2026

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$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill compound-learnings
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summary

Transform ephemeral session learnings into permanent, compounding capabilities.

skill.md

Compound Learnings

Transform ephemeral session learnings into permanent, compounding capabilities.

When to Use

  • "What should I learn from recent sessions?"
  • "Improve my setup based on recent work"
  • "Turn learnings into skills/rules"
  • "What patterns should become permanent?"
  • "Compound my learnings"

Process

Step 1: Gather Learnings

# 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).

Step 2: Extract Patterns (Structured)

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 |

Step 2b: Consolidate Similar Patterns

Before counting, merge patterns that express the same principle:

Example consolidation:

  • "Artifact-first debugging"
  • "Verify hook output by inspecting files"
  • "Filesystem-first debugging" → All express: "Observe outputs before editing code"

Use the most general formulation. Update the frequency table.

Step 3: Detect Meta-Patterns

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?"

Step 4: Categorize (Decision Tree)

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

Step 5: Apply Signal Thresholds

Occurrences Action
1 Note but skip (unless critical failure)
2 Consider - present to user
3+ Strong signal - recommend creation
4+ Definitely create

Step 6: Propose Artifacts

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).

Step 7: Create Approved Artifacts

For Rules:

# 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

For Skills:

Create .claude/skills/<name>/SKILL.md with:

  • Frontmatter (name, description, allowed-tools)
  • When to Use
  • Step-by-step instructions (executable)
  • Examples from the learnings

Add triggers to skill-rules.json if appropriate.

For Hooks:

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"
      }]
    }]
  }
}

For Agent Updates:

Edit existing agent in .claude/agents/<name>.md to add the learned capability.

Step 8: Summary Report

## 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.**

Quality Checks

Before creating any artifact:

  1. Is it general enough? Would it apply in other projects?
  2. Is it specific enough? Does it give concrete guidance?
  3. Does it already exist? Check .claude/rules/ and .claude/skills/ first
  4. Is it the right type? Sequences → skills, heuristics → rules

Files Reference

  • Learnings: .claude/cache/learnings/*.md
  • Skills: .claude/skills/<name>/SKILL.md
  • Rules: .claude/rules/<name>.md
  • Hooks: .claude/hooks/<name>.sh + src/<name>.ts + dist/<name>.mjs
  • Agents: .claude/agents/<name>.md
  • Skill triggers: .claude/skills/skill-rules.json
  • Hook registration: .claude/settings.jsonhooks section
how to use compound-learnings

How to use compound-learnings on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add compound-learnings
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill compound-learnings

The skills CLI fetches compound-learnings from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/compound-learnings

Reload or restart Cursor to activate compound-learnings. Access the skill through slash commands (e.g., /compound-learnings) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.552 reviews
  • Arjun Chawla· Dec 24, 2024

    I recommend compound-learnings for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Shikha Mishra· Dec 20, 2024

    Useful defaults in compound-learnings — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Neel Sethi· Dec 20, 2024

    Keeps context tight: compound-learnings is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Arjun Gupta· Dec 8, 2024

    compound-learnings is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Jin Yang· Nov 27, 2024

    Useful defaults in compound-learnings — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Michael Rahman· Nov 23, 2024

    compound-learnings has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Michael Taylor· Nov 15, 2024

    Keeps context tight: compound-learnings is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Rahul Santra· Nov 11, 2024

    compound-learnings is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Mia Robinson· Nov 11, 2024

    I recommend compound-learnings for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Min Farah· Oct 18, 2024

    I recommend compound-learnings for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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