Skip to content

Latest commit

 

History

History
475 lines (330 loc) · 11.7 KB

File metadata and controls

475 lines (330 loc) · 11.7 KB

FAQ: cc-polymath

Quick answers to frequently asked questions about cc-polymath, organized by topic.

Installation & Setup

How do I install cc-polymath?

Open Claude Code and run:

/plugin marketplace add rand/cc-polymath
/plugin install cc-polymath@cc-polymath

That's it. All 410+ skills are immediately available and auto-discover as you work.

For local development, use:

/plugin install /Users/rand/src/cc-polymath

Do I need to configure anything after installation?

No configuration needed. Skills auto-discover based on your project files and conversation keywords.

To verify installation works:

bash ../scripts/verify-install.sh

Can I install manually instead of as a plugin?

Yes. Clone or fork the repository and symlink it to ~/.claude/plugins/cc-polymath:

git clone https://github.com/rand/cc-polymath ~/.claude/plugins/cc-polymath

Then restart Claude Code. You'll have the same functionality.

How do I update to the latest version?

If installed as a plugin via /plugin install https://..., updates happen automatically.

If you cloned the repo:

cd ~/.claude/plugins/cc-polymath && git pull origin main

How do I uninstall?

/plugin uninstall cc-polymath

Or manually remove:

rm -rf ~/.claude/plugins/cc-polymath

Using Skills

How do I know which skills are available?

List all 33+ categories:

/skills list

Search for specific topics:

/skills postgres
/skills react
/skills kubernetes

Get recommendations for your current project:

/skills

This analyzes your files (package.json, requirements.txt, etc.) and recommends relevant skills.

How does auto-discovery work?

Skills activate automatically based on keywords in your messages. Examples:

  • Mention "React" or "Next.js" → discover-frontend skill loads
  • Mention "PostgreSQL" or "query optimization" → discover-database skill loads
  • Mention "REST API" or "GraphQL" → discover-api skill loads

No commands needed. Just work naturally and relevant skills appear.

What if auto-discovery doesn't trigger?

Use manual commands to load what you need:

# Load a specific gateway skill
/discover-api
/discover-ml
/discover-debugging

# Browse a category
/skills database
/skills testing

# Load a specific skill directly
Read ../skills/api/rest-api-design.md

Auto-discovery covers ~80% of common cases. Manual loading handles the rest.

How do I load a specific skill?

Option 1: Use the slash command

/discover-frontend

Option 2: Search by topic

/skills postgres

Option 3: Browse the category

/skills database

Option 4: Load directly with cat (if you know the exact path) Read ../skills/database/postgres-query-optimization.md

Can I load multiple skills at once?

Yes. Use multiple commands or ask Claude to load related skills:

/discover-api
/discover-database
/discover-testing

Or naturally in conversation: "I'm building a REST API with PostgreSQL and need testing patterns" — multiple skills auto-discover together.

How much context do skills use?

Gateway skills: ~1K tokens (~200 lines)
Category indexes: ~2.5K tokens (~500 lines)
Individual skills: ~1.5K tokens (~320 lines)

Loading 3-5 skills uses less context than most project files. This is why the three-tier system works: you get comprehensive knowledge while using minimal tokens.


Skill Organization

What's the difference between gateway, index, and individual skills?

Gateway Skills (~200 lines)

  • Lightweight entry points
  • Auto-discover based on keywords
  • Provide quick reference + links to related skills
  • Example: /discover-frontend covers React, Next.js, TypeScript overview

Category Indexes (~500 lines)

  • Comprehensive listings with descriptions
  • Show all skills in a domain with use cases
  • Loaded when browsing or planning
  • Example: /skills database shows all 11 database skills

Individual Skills (~320 lines)

  • Complete implementation guides
  • Loaded on-demand for deep technical content
  • Self-contained but cross-referenced
  • Example: postgres-query-optimization.md with EXPLAIN patterns

Flow: Gateway → Index → Individual = Progressive discovery

How are skills organized?

By 33+ categories grouped into themes:

Core Development (125 skills)

  • Languages: Python, Rust, Zig, Go, JavaScript
  • Frameworks: React, Next.js, FastAPI, Axum
  • Databases: PostgreSQL, MongoDB, Redis
  • Testing: Unit, integration, e2e

Infrastructure & DevOps (70 skills)

  • Cloud: AWS, GCP, Modal, Vercel
  • Containers: Docker, Kubernetes
  • CI/CD: GitHub Actions, pipelines
  • Observability: Logging, metrics, tracing

Specialized (85 skills)

  • ML/AI: DSPy, HuggingFace, embeddings, RAG
  • Formal Methods: Z3, Lean 4, SAT/SMT solvers
  • Mathematics: Linear algebra, topology, category theory
  • Systems: WebAssembly, eBPF, binary protocols

Meta (7 skills)

  • Workflow: Beads task management
  • Discovery: Analyzing projects and prompts
  • Skill creation: Building custom skills

Full list: /skills list

How many skills are there?

400 total skills across 31+ categories:

  • 283 individual skills
  • 31 gateway skills (auto-discover)
  • 30 category indexes
  • 7 meta/workflow skills

See them all with /skills list.

Can I create custom skills?

Yes. Skills follow Anthropic's agent skills framework with clear structure:

---
title: "My Custom Skill"
description: "What this skill covers"
keywords: [keyword1, keyword2]
---

# Skill Content
[Implementation guide, patterns, examples]

See skills/skill-creation.md for the full template and guidelines.

You can:

  • Add custom skills to your local cc-polymath fork
  • Create standalone skills in your own projects
  • Share skills via pull requests

Common Workflows

Building a web app?

Let skills auto-discover naturally, or load explicitly:

/discover-frontend      # React, Next.js, TypeScript
/discover-api           # REST/GraphQL design
/discover-database      # Database selection & optimization
/discover-testing       # Unit, integration, e2e patterns

Skills compose together seamlessly for full-stack work.

Debugging performance issues?

/discover-debugging      # GDB, LLDB, profiling tools
/discover-observability  # Metrics, tracing, logging
/skills postgres         # If database-related
/discover-cloud          # If cloud infrastructure

Combine skills based on where the bottleneck is.

Working with data?

/discover-database   # SQL optimization, schema design
/discover-data       # ETL, streaming, batch processing
/discover-caching    # Redis, CDN strategies
/discover-ml         # Analytics, embeddings, RAG

Data workflows often need 3-4 skills working together.

Creating documentation?

/discover-diagrams   # Mermaid flowcharts, ER diagrams, sequences
/skills mermaid      # Search for diagram-specific patterns

Diagrams skills help create professional visuals for docs.


Troubleshooting

Commands aren't working (e.g., /discover-frontend returns an error)

Check installation:

bash ../scripts/verify-install.sh

If it fails, reinstall:

/plugin uninstall cc-polymath
/plugin marketplace add rand/cc-polymath
/plugin install cc-polymath@cc-polymath

If the plugin is installed but commands don't work, restart Claude Code.

Skills aren't loading or auto-discovery doesn't trigger

Test auto-discovery: Try a clear prompt like: "I'm building a React app with TypeScript"

You should see discover-frontend activate automatically.

Force load a skill: Read ../skills/discover-frontend/SKILL.md

If that returns content, the installation is working. Auto-discovery may just need clearer keywords.

Run diagnostics:

bash ../scripts/diagnose.sh

This reports what's installed and any configuration issues.

Path errors or file not found

Verify the plugin path is correct:

ls ../skills

You should see skill directories. If empty or missing, reinstall the plugin.

Check that you're not in a directory with conflicting configuration:

cat ~/.claude/config.json | grep cc-polymath

Advanced Usage

Can I use skills with other plugins?

Yes. Skills are model-invoked and work alongside other Claude Code plugins.

If two plugins provide similar functionality, Claude intelligently selects which to use based on context. No conflicts.

How do I find skills for my specific framework?

Search by name:

/skills nextjs
/skills fastapi
/skills axum

Browse a category:

/skills frontend
/skills backend

Use auto-discovery: Just mention your framework naturally in a prompt. Skills auto-discover.

Check the README: ../skills/README.md lists all skills by framework.

Can I contribute skills?

Yes! Pull requests welcome. The repository is open for community contributions.

Process:

  1. Fork the repository
  2. Add your skill to the appropriate category
  3. Follow the template in skills/skill-creation.md
  4. Include keywords for auto-discovery
  5. Submit a PR

Skills are validated for:

  • Code block syntax
  • YAML frontmatter
  • Cross-references
  • Line count (individual skills ~320 lines)

See CONTRIBUTING.md for detailed guidelines.

How do I report issues?

Open an issue on GitHub: cc-polymath issues

Include:

  • What you were trying to do
  • Which skills were involved
  • Error message or unexpected behavior
  • Output from diagnose.sh

Bugs are fixed quickly. Feature requests welcome.


Comparison to Other Approaches

vs. Loading all skills upfront

Approach Context Cost Boot Time Discoverability
cc-polymath 2-5K tokens Instant Auto-discover + manual
All upfront 143K tokens Slow Everything available
Savings 98% reduction Much faster More intelligent

With cc-polymath, you spend context on your actual work, not documentation.

vs. Manual skill files

cc-polymath advantages:

  • Auto-discovery (no need to remember what exists)
  • Organized into categories (easy browsing)
  • Cross-referenced (skills link to related skills)
  • Commands for quick access (/discover-*)
  • Progressive loading (gateway → index → skill)

Manual files require:

  • Remembering the skill exists
  • Manual file loading
  • No automatic activation
  • Less organization

vs. Monolithic documentation

Approach Granularity Reusability Context Efficiency
cc-polymath Atomic (410+ skills) Compose freely 98% savings
Monolithic Large chapters Load all-or-nothing High overhead
Best for Precise knowledge Flexible workflows Efficient context

Atomic skills let you load exactly what you need, no more. This precision is what enables 98% context reduction.


More Resources

Getting Started

Learning by Example

Deep Dives

Troubleshooting

  • TROUBLESHOOTING.md - Common issues and solutions
  • bash ../scripts/diagnose.sh - Automated diagnostics

Full Documentation

Explore Skills

  • /skills list - All 33+ categories
  • /skills [topic] - Search for specific topics
  • Read ../skills/README.md - Full skill catalog

Still have questions? Check TROUBLESHOOTING.md or open an issue on GitHub.