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GitHub リポジトリ

configent

configent には raas-dev から収集した 9 個の skills があり、リポジトリ単位の職業カバレッジとサイト内 skill 詳細ページを表示します。

収集済み skills
9
Stars
19
更新
2026-07-26
Forks
4
職業カバレッジ
2 件の職業カテゴリ · 100% 分類済み
リポジトリエクスプローラー

このリポジトリの skills

token-optimizer
ソフトウェア開発者

Find the ghost tokens. Audit Claude Code or Codex setup, see where context goes, fix it. Use when context feels tight.

2026-07-26
browser-harness
ソフトウェア開発者

Always use browser-harness for any web interaction: automation, scraping, testing, or site/app work.

2026-06-22
intent-layer
ソフトウェア開発者

Set up hierarchical Intent Layer (AGENTS.md files) for codebases. Use when initializing a new project, adding context infrastructure to an existing repo, user asks to set up AGENTS.md, add intent layer, make agents understand the codebase, or scaffolding AI-friendly project documentation.

2026-05-30
open-computer-use
ソフトウェア開発者

Platform-neutral guidance for using Open Computer Use, the open-source Computer Use MCP server and CLI for macOS, Linux, and Windows. Use when an agent needs to install, verify, troubleshoot, configure, or operate Open Computer Use through its native CLI, stdio MCP server, or direct Computer Use tool calls.

2026-05-26
graphify
データサイエンティスト

any input (code, docs, papers, images, video) → knowledge graph → clustered communities → HTML + JSON + GRAPH_REPORT.md. Use when user asks any question about a codebase, project content, architecture, or file relationships — especially if graphify-out/ exists. Provides persistent graph with god nodes, community detection, and BFS/DFS query tools.

2026-05-19
brownfield-onboarding
ソフトウェア開発者

This skill helps users get started with existing (brownfield) projects by scanning the codebase, documenting structure and purpose, analyzing architecture and technical stack, identifying design flaws, suggesting improvements for testing and CI/CD pipelines, and generating AI agent constitution files (AGENTS.md) with project-specific context, coding principles, and UI/UX guidelines.

2026-05-02
agentic-eval
ソフトウェア開発者

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

2026-04-12
skill-conductor
ソフトウェア開発者

Create, edit, evaluate, and package agent skills. Use when building a new skill from scratch, improving an existing skill, running evals to test a skill, benchmarking skill performance, optimizing a skill's description for better triggering, reviewing third-party skills for quality, or packaging skills for distribution. Not for using skills or general coding tasks.

2026-03-28
context7
ソフトウェア開発者

Fetch up-to-date library documentation via Context7 API. Use PROACTIVELY when: (1) Working with ANY external library (React, Next.js, Supabase, etc.) (2) User asks about library APIs, patterns, or best practices (3) Implementing features that rely on third-party packages (4) Debugging library-specific issues (5) Need current documentation beyond training data cutoff (6) AND MOST IMPORTANTLY, when you are installing dependencies, libraries, or frameworks you should ALWAYS check the docs to see what the latest versions are. Do not rely on outdated knowledge. Always prefer this over guessing library APIs or using outdated knowledge.

2026-03-09