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find_skill

Discover bundled ecosystem skills, plugins, and integration recipes by task, category, or ID. Use layered search to get top matches, install commands, and full docs in AI Team OS.

Instructions

Find ecosystem skills/plugins using a 3-layer progressive loading system.

Searches a small curated catalog of third-party skills, plugins and integration recipes bundled with the OS. It does not list what is installed in the current session and does not query a live marketplace.

Layer 1 (quick recommend): Describe your task and get the top 5 catalog entries with one-line descriptions, install commands and match_score; entries with match_score 0 did not match the description. Layer 2 (category browse): Browse all skills grouped by category (memory / code-quality / frontend / security / dev-workflow / integration / etc.). Layer 3 (full detail): Get complete documentation for a single skill including features, OS complement relationship, and variants.

The integration category holds the ecosystem integration recipes (GitHub / Slack / Linear / fullstack team); each one says which external MCP server to install and which OS tools it pairs with.

Input Schema

TableJSON Schema

Name	Required	Description
`level`	No	Discovery depth — 1=quick (default), 2=category, 3=full detail.
`category`	No	Category filter for level=2 (e.g., "frontend", "security", "integration"). Empty string returns all categories.
`skill_id`	No	Skill identifier for level=3 detail lookup (e.g., "vibesec", "superpowers", "claude-mem", "github-integration").
`task_description`	No	What you want to accomplish (used for level=1 matching). Examples: "frontend ui design", "security audit web app", "data science jupyter", "code review PR".

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.11.2Aug 3, 2026

changedInput schema / properties / category / description

Previous value: -"Category filter for level=2 (e.g., \"frontend\", \"security\").\n      Empty string returns all categories."New value: +"Category filter for level=2 (e.g., \"frontend\", \"security\",\n      \"integration\"). Empty string returns all categories."

changedInput schema / properties / skill_id / description

Previous value: -"Skill identifier for level=3 detail lookup\n      (e.g., \"vibesec\", \"superpowers\", \"claude-mem\")."New value: +"Skill identifier for level=3 detail lookup\n      (e.g., \"vibesec\", \"superpowers\", \"claude-mem\",\n      \"github-integration\")."

First observedv1.9.0Jul 14, 2026

TDQS

A4.6/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden and does so well. It discloses that the catalog is curated and bundled, not installed-session state or a live marketplace, and explains match_score=0 semantics and what each layer returns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The definition is longer than average, but the complexity of a three-layer discovery tool justifies it. It is front-loaded with the core purpose and then structured cleanly by layer, with minimal waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, four optional parameters, and an output schema, the description provides enough context for an agent to select the right layer and understand the catalog's boundaries. Return-value detail is appropriately left to the output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents the four parameters. The description still adds useful meaning by mapping level values to distinct behaviors and explaining the category and skill_id use cases beyond the schema text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: find ecosystem skills/plugins in a small curated catalog. It immediately distinguishes this from installed-session skills and live-marketplace search, which prevents the most likely confusion with sibling ecosystem tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly explains when to use each of the three layers: quick recommend, category browse, and full detail. It does not explicitly name sibling tools as alternatives, but the level-based guidance makes invocation choice clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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