continuous-learning-v2
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Installation Guide
How to use continuous-learning-v2 on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
continuous-learning-v2
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches continuous-learning-v2 from affaan-m/everything-claude-code and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate continuous-learning-v2. Access via /continuous-learning-v2 in your agent's command palette.
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Continuous Learning v2.1 - Instinct
-Based Architecture
An advanced learning system that turns your Claude Code sessions into reusable knowledge through atomic "instincts" - small learned behaviors with confidence scoring.
v2.1 adds project-scoped instincts — React patterns stay in your React project, Python conventions stay in your Python project, and universal patterns (like "always validate input") are shared globally.
When to Activate
- Setting up automatic learning from Claude Code sessions
- Configuring instinct-based behavior extraction via hooks
- Tuning confidence thresholds for learned behaviors
- Reviewing, exporting, or importing instinct libraries
- Evolving instincts into full skills, commands, or agents
- Managing project-scoped vs global instincts
- Promoting instincts from project to global scope
What's New in v2.1
| Feature | v2.0 | v2.1 |
|---|---|---|
| Storage | Global (~/.claude/homunculus/) | Project-scoped (projects//) |
| Scope | All instincts apply everywhere | Project-scoped + global |
| Detection | None | git remote URL / repo path |
| Promotion | N/A | Project → global when seen in 2+ projects |
| Commands | 4 (status/evolve/export/import) | 6 (+promote/projects) |
| Cross-project | Contamination risk | Isolated by default |
What's New in v2 (vs v1)
| Feature | v1 | v2 |
|---|---|---|
| Observation | Stop hook (session end) | PreToolUse/PostToolUse (100% reliable) |
| Analysis | Main context | Background agent (Haiku) |
| Granularity | Full skills | Atomic "instincts" |
| Confidence | None | 0.3-0.9 weighted |
| Evolution | Direct to skill | Instincts -> cluster -> skill/command/agent |
| Sharing | None | Export/import instincts |
The Instinct Model
An instinct is a small learned behavior:
---
id: prefer-functional-style
trigger: "when writing new functions"
confidence: 0.7
domain: "code-style"
source: "session-observation"
scope: project
project_id: "a1b2c3d4e5f6"
project_name: "my-react-app"
---
# Prefer Functional Style
## Action
Use functional patterns over classes when appropriate.
## Evidence
- Observed 5 instances of functional pattern preference
- User corrected class-based approach to functional on 2025-01-15
Properties:
- Atomic -- one trigger, one action
- Confidence-weighted -- 0.3 = tentative, 0.9 = near certain
- Domain-tagged -- code-style, testing, git, debugging, workflow, etc.
- Evidence-backed -- tracks what observations created it
- Scope-aware --
project(default) orglobal
How It Works
Session Activity (in a git repo)
|
| Hooks capture prompts + tool use (100% reliable)
| + detect project context (git remote / repo path)
v
+---------------------------------------------+
| projects/<project-hash>/observations.jsonl |
| (prompts, tool calls, outcomes, project) |
+---------------------------------------------+
|
| Observer agent reads (background, Haiku)
v
+---------------------------------------------+
| PATTERN DETECTION |
| * User corrections -> instinct |
| * Error resolutions -> instinct |
| * Repeated workflows -> instinct |
| * Scope decision: project or global? |
+---------------------------------------------+
|
| Creates/updates
v
+---------------------------------------------+
| projects/<project-hash>/instincts/personal/ |
| * prefer-functional.yaml (0.7) [project] |
| * use-react-hooks.yaml (0.9) [project] |
+---------------------------------------------+
| instincts/personal/ (GLOBAL) |
| * always-validate-input.yaml (0.85) [global]|
| * grep-before-edit.yaml (0.6) [global] |
+---------------------------------------------+
|
| /evolve clusters + /promote
v
+---------------------------------------------+
| projects/<hash>/evolved/ (project-scoped) |
| evolved/ (global) |
| * commands/new-feature.md |
| * skills/testing-workflow.md |
| * agents/refactor-specialist.md |
+---------------------------------------------+
Project Detection
The system automatically detects your current project:
CLAUDE_PROJECT_DIRenv var (highest priority)git remote get-url origin-- hashed to create a portable project ID (same repo on different machines gets the same ID)git rev-parse --show-toplevel-- fallback using repo path (machine-specific)- Global fallback -- if no project is detected, instincts go to global scope
Each project gets a 12-character hash ID (e.g., a1b2c3d4e5f6). A registry file at ~/.claude/homunculus/projects.json maps IDs to human-readable names.
Quick Start
1. Enable Observation Hooks
Add to your ~/.claude/settings.json.
If installed as a plugin (recommended):
{
"hooks": {
"PreToolUse": [{
"matcher": "*",
"hooks": [{
"type": "command",
"command": "${CLAUDE_PLUGIN_ROOT}/skills/continuous-learning-v2/hooks/observe.sh"
}]
}],
"PostToolUse": [{
"matcher": "*",
"hooks": [{
"type": "command",
"command": "${CLAUDE_PLUGIN_ROOT}/skills/continuous-learning-v2/hooks/observe.sh"
}]
}]
}
}
If installed manually to ~/.claude/skills:
{
"hooks": {
"PreToolUse": [{
"matcher": "*",
"hooks": [{
"type": "command",
"command": "~/.claude/skills/continuous-learning-v2/hooks/observe.sh"
}]
}],
"PostToolUse": [{
"matcher": "*",
"hooks": [{
"type": "command",
"command": "~/.claude/skills/continuous-learning-v2/hooks/observe.sh"
}]
}]
}
}
2. Initialize Directory Structure
The system creates directories automatically on first use, but you can also create them manually:
# Global directories
mkdir -p ~/.claude/homunculus/{instincts/{personal,inherited},evolved/{agents,skills,commands},projects}
# Project directories are auto-created when the hook first runs in a git repo
3. Use the Instinct Commands
/instinct-status # Show learned instincts (project + global)
/evolve # Cluster related instincts into skills/commands
/instinct-export # Export instincts to file
/instinct-import # Import instincts from others
/promote # Promote project instincts to global scope
/projects # List all known projects and their instinct counts
Commands
| Command | Description |
|---|---|
/instinct-status |
Show all instincts (project-scoped + global) with confidence |
/evolve |
Cluster related instincts into skills/commands, suggest promotions |
/instinct-export |
Export instincts (filterable by scope/domain) |
/instinct-import <file> |
Import instincts with scope control |
/promote [id] |
Promote project instincts to global scope |
/projects |
List all known projects and their instinct counts |
Configuration
Edit config.json to control the background observer:
{
"version": "2.1",
"observer": {
"enabled": false,
"run_interval_minutes": 5,
"min_observations_to_analyze": 20
}
}
| Key | Default | Description |
|---|---|---|
observer.enabled |
false |
Enable the background observer agent |
observer.run_interval_minutes |
5 |
How often the observer analyzes observations |
observer.min_observations_to_analyze |
20 |
Minimum observations before analysis runs |
Other behavior (observation capture, instinct thresholds, project scoping, promotion criteria) is configured via code defaults in instinct-cli.py and observe.sh.
File Structure
~/.claude/homunculus/
+-- identity.json # Your profile, technical level
+-- projects.json # Registry: project hash -> name/path/remote
+-- observations.jsonl # Global observations (fallback)
+-- instincts/
| +-- personal/ # Global auto-learned instincts
| +-- inherited/ # Global imported instincts
+-- evolved/
| +-- agents/ # Global generated agents
| +-- skills/ # Global generated skills
| +-- commands/ # Global generated commands
+-- projects/
+-- a1b2c3d4e5f6/ # Project hash (from git remote URL)
| +-- project.json # Per-project metadata mirror (id/name/root/remote)
| +-- observations.jsonl
| +-- observations.archive/
| +-- instincts/
| | +-- personal/ # Project-specific auto-learned
| | +-- inherited/ # Project-specific imported
| +-- evolved/
| +-- skills/
| +-- commands/
| +-- agents/
+-- f6e5d4c3b2a1/ # Another project
+-- ...
Scope Decision Guide
| Pattern Type | Scope | Examples |
|---|---|---|
| Language/framework conventions | project | "Use React hooks", "Follow Django REST patterns" |
| File structure preferences | project | "Tests in __tests__/", "Components in src/components/" |
| Code style | project | "Use functional style", "Prefer dataclasses" |
| Error handling strategies | project | "Use Result type for errors" |
| Security practices | global | "Validate user input", "Sanitize SQL" |
| General best practices | global | "Write tests first", "Always handle errors" |
| Tool workflow preferences | global | "Grep before Edit", "Read before Write" |
| Git practices | global | "Conventional commits", "Small focused commits" |
Instinct Promotion (Project -> Global)
When the same instinct appears in multiple projects with high confidence, it's a candidate for promotion to global scope.
Auto-promotion criteria:
- Same instinct ID in 2+ projects
- Average confidence >= 0.8
How to promote:
# Promote a specific instinct
python3 instinct-cli.py promote prefer-explicit-errors
# Auto-promote all qualifying instincts
python3 instinct-cli.py promote
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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- LLiam Johnson★★★★★Dec 24, 2024
continuous-learning-v2 has been reliable in day-to-day use. Documentation quality is above average for community skills.
- LLiam Smith★★★★★Dec 16, 2024
continuous-learning-v2 reduced setup friction for our internal harness; good balance of opinion and flexibility.
- SShikha Mishra★★★★★Dec 12, 2024
We added continuous-learning-v2 from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- CChinedu Thomas★★★★★Dec 12, 2024
continuous-learning-v2 fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- SSakshi Patil★★★★★Nov 23, 2024
continuous-learning-v2 fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- CChinedu Sharma★★★★★Nov 7, 2024
We added continuous-learning-v2 from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- YYash Thakker★★★★★Nov 3, 2024
continuous-learning-v2 reduced setup friction for our internal harness; good balance of opinion and flexibility.
- CChinedu Kapoor★★★★★Oct 26, 2024
Keeps context tight: continuous-learning-v2 is the kind of skill you can hand to a new teammate without a long onboarding doc.
- DDhruvi Jain★★★★★Oct 22, 2024
continuous-learning-v2 is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- CChaitanya Patil★★★★★Oct 14, 2024
continuous-learning-v2 has been reliable in day-to-day use. Documentation quality is above average for community skills.
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