skills-but-for-dev

Search Github Repos

search_github_repos

Search GitHub for healthy, license-compliant repositories to power a specific architecture stack layer before writing code, filtering by language, topics, and commercial viability.

Instructions

Search for fresh, healthy, and license-compliant GitHub repositories for an architecture stack layer.

When to use:

Before writing code for any project layer (e.g., 'speaker diarization', 'meeting bot').
To discover both proven industry leaders and fast-rising breakout repositories.

Input Schema

Name	Required	Description
`limit`	No	Maximum number of ranked candidates to return (default: 8).
`query`	Yes	2-3 English search keywords (e.g., 'speaker diarization', 'audio recorder').
`topics`	No	Optional GitHub topic tags to refine results (e.g., ['diarization', 'webrtc']).
`language`	No	Optional programming language filter (e.g., 'python', 'typescript').
`commercial_use`	No	Set True if the user wants commercial viability (applies GPL/NC penalties).

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Nov 1, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations, so the description carries the burden. It discloses meaningful behavior beyond the schema — results are filtered/ranked for freshness, health, and license compliance — but doesn't explain ranking logic, read-only nature, pagination/limit tradeoffs, or rate limits.

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

Conciseness5/5

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

One-line purpose followed by a bulleted when-to-use block, front-loaded and free of filler. Every sentence contributes to selection.

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

Completeness4/5

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

With an output schema present and full parameter documentation, the description only needs to sell purpose and timing, which it does. Minor gap: no note on what the ranked output emphasizes or how it relates to the comparison sibling compare_candidates.

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

Parameters3/5

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

Schema coverage is 100%, so all five parameters are documented in the schema. The description adds the 'architecture stack layer' framing but no syntax or format guidance beyond what the schema already provides, so baseline 3 applies.

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

Purpose4/5

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

States a specific verb ('Search') and resource ('GitHub repositories') plus quality qualifiers (fresh, healthy, license-compliant). The GitHub resource distinguishes it from search_hf_models and search_hf_spaces, though no sibling is explicitly named.

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?

Has an explicit 'When to use' section with two concrete triggers (before writing code for a project layer; discovering proven and breakout repos). Clear context, but no exclusions or named alternatives, so it stops short of a 5.

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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