find-skills
The find-skills skill searches the public skill registry and installs community-contributed tools relevant to the user's current task. Use it when the user introduces a new technology, framework, or asks how to accomplish something specific, or when they begin work in an unfamiliar area that would benefit from specialized capabilities not yet available in the project.
git clone --depth 1 https://github.com/caliber-ai-org/ai-setup /tmp/find-skills && cp -r /tmp/find-skills/skills/find-skills ~/.claude/skills/find-skillsSKILL.md
# Find Skills Search the public skill registry for community-contributed skills relevant to the user's current task and install them into this project. ## Instructions 1. Identify the key technologies, frameworks, or task types from the user's request that might have community skills available 2. Ask the user: "Would you like me to search for community skills for [identified technologies]?" 3. If the user agrees, run: ```bash caliber skills --query "<relevant terms>" ``` This outputs the top 5 matching skills with scores and descriptions. 4. Present the results to the user and ask which ones to install 5. Install the selected skills: ```bash caliber skills --install <slug1>,<slug2> ``` 6. Read the installed SKILL.md files to load them into your current context so you can use them immediately in this session 7. Summarize what was installed and continue with the user's task ## Examples User: "let's build a web app using React" -> "I notice you want to work with React. Would you like me to search for community skills that could help with React development?" -> If yes: run `caliber skills --query "react frontend"` -> Show the user the results, ask which to install -> Run `caliber skills --install <selected-slugs>` -> Read the installed files and continue User: "help me set up Docker for this project" -> "Would you like me to search for Docker-related skills?" -> If yes: run `caliber skills --query "docker deployment"` User: "I need to write tests for this Python ML pipeline" -> "Would you like me to find skills for Python ML testing?" -> If yes: run `caliber skills --query "python machine-learning testing"` ## When NOT to trigger - The user is working within an already well-configured area - You already suggested skills for this technology in this session - The user is in the middle of urgent debugging or time-sensitive work - The technology is too generic (e.g. just "code" or "programming")
Creates a new CLI command following the Commander.js pattern in src/commands/. Handles command registration in src/cli.ts, telemetry tracking via tracked() wrapper, and option parsing. Use when user says add command, new CLI command, create subcommand, or adds files to src/commands/. Do NOT use for modifying existing commands or fixing bugs in existing commands.
Writes Vitest tests following project patterns: __tests__/ directories, vi.mock() for module mocking with vi.hoisted() for test-time factories, global LLM mock from src/test/setup.ts, environment variable save/restore in beforeEach/afterEach, vi.clearAllMocks() lifecycle, and test file organization. Use when user says 'write tests', 'add test coverage', 'test this', creates *.test.ts files, or when test failures appear in CI. Do NOT use for non-test code or for debugging without writing tests.
Adds a new LLM provider implementing LLMProvider interface with call() and stream() methods. Integrates with provider factory in src/llm/index.ts, config detection in src/llm/config.ts, and error handling via tracking and recovery. Use when adding a new model backend, integrating a third-party LLM API, or extending LLM platform support. Do NOT use for fixing bugs in existing providers, modifying existing provider behavior, or changing the LLMProvider interface.
Saves user instructions as persistent learnings for future sessions. Use when the user says 'remember this', 'always do X', 'from now on', 'never do Y', or gives any instruction they want persisted across sessions. Proactively suggest when the user states a preference, convention, or rule they clearly want followed in the future.
Add a new deterministic scoring check in src/scoring/checks/ that evaluates config quality. Follows the Check[] return pattern, uses point constants from src/scoring/constants.ts, and integrates via filterChecksForTarget() in src/scoring/index.ts. Use when user says 'add scoring check', 'new check', 'modify scoring criteria', or works in src/scoring/checks/. Do NOT use for display changes or refactoring scoring logic.
Sets up Caliber for automatic AI agent context sync. Installs pre-commit hooks so CLAUDE.md, Cursor rules, and Copilot instructions update automatically on every commit. Use when Caliber hooks are not yet installed or when the user asks about keeping agent configs in sync.
Add a new platform writer module in src/writers/ that generates and writes agent config files for a supported platform. Each writer exports a function that accepts a config interface, creates directories (rules/, skills/, mcp configs), writes files with proper formatting and frontmatter, and returns string[] of written file paths. Use when adding platform support for a new agent, integrating a new code AI tool, or extending caliber to support new targets. Do NOT use for modifying existing writers, refactoring scoring logic, or changing how writers are invoked.