STEP 1 - Discover relevant skills. Call this FIRST at the start of any task to check whether the registry contains a curated skill that matches. Performs semantic vector search and returns ranked results with similarity scores.

Workflow after this call: • score > 0.6 → strong match - call skills_get_body with that skill_id • score 0.4–0.6 → possible match - inspect description before proceeding • score < 0.4 → no relevant skill - proceed without one

Query tips: be task-specific, not generic. 'write pytest unit tests for a Flask REST API' outperforms 'testing'. Describe what you are trying to accomplish, not what you want to find.

STEP 2 - Load full skill instructions. Call after skills_find_relevant once you have identified the best-matching skill_id.

Returns three fields: • instructions - expert step-by-step guidance; read and follow these • system_prompt_addition - optional context to add to your persona (may be empty) • tier3_manifest - lists available references, scripts, and assets by filename

After loading: apply the instructions. If tier3_manifest lists files that the instructions explicitly reference, fetch them with skills_get_reference, skills_run_script, or skills_get_asset. Most tasks are fully served by the instructions alone - do not load Tier 3 speculatively.

Version pinning: pass version='1.2' to pin to a specific skill version, or use the inline form skill_id='stripe-integration@1.2'. If the requested version is not found, the latest version is returned with a version_note explaining the fallback. Deprecated skills include a deprecation_notice field naming the replacement.

OPTIONAL STEP 2b - Load config schema, variants, and constraints for a skill. Call only when: (a) the user asks to customise skill behaviour, or (b) skills_get_body instructions mention configurable options.

Returns: config_schema (JSON Schema for parameters), variants (alternative skill modes), dependencies (required tools/packages), limitations (known constraints).

Do NOT call this by default - most tasks complete with skills_get_body alone.

STEP 3a - Fetch a reference document bundled with a skill (markdown files: checklists, policies, API specs, examples).

Two-phase use:

1. Call with filename='list' (default) to see the full reference manifest

2. Call again with the specific filename to fetch its content

Only call when: tier3_manifest from skills_get_body lists reference files AND the skill instructions explicitly name one. Do not load references speculatively.

STEP 3b - Execute a helper script bundled with a skill. Script source is NEVER returned - only stdout, stderr, and exit_code.

Two-phase use:

1. Call with filename='list' to see available scripts and their descriptions

2. Call with the specific filename (and optional input_data) to execute

input_data: key-value pairs passed to the script as environment variables. Scripts run sandboxed in an isolated temp directory with a 30-second hard timeout.

Only call when skill instructions direct you to run a specific script.

STEP 3c - Fetch a template or static resource bundled with a skill (markdown templates, config starters, example data files).

Two-phase use:

1. Call with filename='list' (default) to see the full asset manifest

2. Call again with the specific filename to fetch its content

Use the returned content as a starting template - adapt it to the specific task. Only call when skill instructions reference a specific asset file.

BROWSING - Browse all 100+ available skills in the registry without semantic search.

Use this when you want to see what skills are available, understand the full breadth of the registry, or look for skills by browsing rather than searching.

Returns lightweight frontmatter for each skill (skill_id, name, tags, complexity_level, has_tier3) to keep token usage reasonable.

Supports pagination: use offset to skip results, limit to control batch size.