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knowledge-retriever
Retrieves relevant knowledge from project documentation, code comments, and README files
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Retrieves relevant knowledge from project documentation, code comments, and README files
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | knowledge-retriever |
| description | Retrieves relevant knowledge from project documentation, code comments, and README files |
| version | 1.0.0 |
| author | go-on-team |
| tags | ["knowledge","retrieval","documentation","search","context"] |
| min_go_on_version | 1.0.0 |
Scans project documentation, comments, READMEs, and design docs to surface the most relevant information for a given query. Useful for onboarding, answering architecture questions, or gathering context before making changes.
README.md, CONTRIBUTING.md, ARCHITECTURE.md, CHANGELOG.md, doc-comments in source, and any docs/ directory) and builds a lightweight search index.max_results.| Parameter | Type | Description |
|---|---|---|
query | string | Natural-language question or keywords to search for |
max_results | integer | Maximum number of results to return (default: 5, max: 20) |
{
"query": "How does the authentication middleware work?",
"max_results": 3
}
{
"results": [
{
"file": "docs/auth/overview.md",
"snippet": "The JWT middleware is registered in src/main.rs on line 42. It extracts the Bearer token from the Authorization header...",
"score": 0.94
},
{
"file": "src/auth/middleware.rs",
"snippet": "// validate_token decodes the JWT, checks expiry, and injects the Claims into the request context.",
"score": 0.87
},
{
"file": "README.md",
"snippet": "Authentication: set AUTH_SECRET env var and include an Authorization: Bearer <token> header on protected routes.",
"score": 0.72
}
]
}
Analyzes project structure, module dependencies, imports, and entry points to generate architecture diagrams in Mermaid format
Analyzes ETL and data pipeline code for optimization opportunities across Python (Pandas, PySpark), Rust (polars, datafusion), SQL, and general pipeline descriptions
Validates environment variable configurations and config files (YAML, TOML, JSON, .env) for missing required variables, type mismatches, deprecated keys, naming convention violations, secret exposure risks, and invalid value ranges
Analyzes code for performance bottlenecks including N+1 queries, O(n^2) or worse algorithms, unnecessary allocations, sync I/O in async contexts, excessive cloning, missing caching opportunities, and large payload transfers. Supports Rust, Python, TypeScript, and Go.
Analyzes, improves, and restructures LLM prompts for clarity, efficiency, and reliability
Analyzes source code for common security vulnerabilities including SQL injection, XSS, command injection, hardcoded secrets, insecure deserialization, path traversal, and SSRF