Open library of portable skills for AI agents. Procedural knowledge any agent can discover and load on demand — via MCP, CLI, or plain markdown.
Two tools with clear, actionable descriptions and well-formed schemas. However, both tools are READ_ONLY discovery/retrieval operations with minimal parameter validation. Naming is verb-forward (find_, load_) and follows conventions. Schemas include required fields and type declarations. Critical gaps: no error handling guidance (what if skill not found?), no pagination for find_skill results, no output schema documentation, and parameter descriptions lack format/constraint details. The tools are simple and focused, but lack production-grade robustness.
Search the agentskills library for skills relevant to the task at hand. Returns names and descriptions only, not the skill contents. Call this first, then load_skill for whichever result fits.
Load the full instructions for one skill by name. Use the name returned by find_skill. Follow the loaded instructions exactly.
No output schema documented for either tool. LLMs cannot plan downstream calls or extract structured data without knowing what fields to expect.
find_skill lacks pagination parameters (limit defaults to 10, but no offset/cursor or total_count in response). Large result sets will be truncated silently.
No error handling guidance. What happens if load_skill('nonexistent-skill') is called? No recovery hint or available-skills fallback offered.
Parameter 'reference' in load_skill is optional but lacks constraint documentation. Description says 'Only use a filename the skill body told you to load', this is vague and requires the LLM to parse skill body text to validate.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 55 | 2026-07-28+ | v2 |
find_skill 'limit' parameter is type 'number' but lacks min/max bounds. LLMs could pass 0, negative, or absurdly large values.