ai-coding-agent-setup
3
总安装量
3
周安装量
#55345
全站排名
安装命令
npx skills add https://github.com/markpitt/claude-skills --skill ai-coding-agent-setup
Agent 安装分布
opencode
3
gemini-cli
3
github-copilot
3
codex
3
kimi-cli
3
amp
3
Skill 文档
AI Coding Agent Setup
Configure AI agents to deeply understand a codebase and continuously improve as the project grows. This skill covers the three-layer architecture: AGENTS.md for project identity, agent skills for packaged workflows, and MCP servers for live tool access â plus self-improvement strategies to prevent context rot.
Quick Reference Table
| Goal | Load Resource | Key Concepts |
|---|---|---|
| Create or improve AGENTS.md | resources/agents-md-guide.md |
project identity, hierarchical discovery, conventions |
| Package project workflows as skills | resources/agent-skills-architecture.md |
progressive disclosure, SKILL.md, trigger phrases |
| Add IDE/codebase tools via MCP | resources/mcp-codebase-tools.md |
Bifrost, vscode-mcp-server, semantic search |
| Reduce context rot, build feedback loops | resources/self-improvement-patterns.md |
living docs, meta-skills, context compression |
Orchestration Protocol
Phase 1 â Classify the Request
Determine which layer the user needs help with:
- “Set up AI for my project” â start with
agents-md-guide.md, then assess if skills and MCP are needed - “AI keeps making the same mistake” â
self-improvement-patterns.md(feedback loops section) - “AI can’t navigate my code / doesn’t understand the structure” â
mcp-codebase-tools.md - “How do I package this workflow for AI reuse?” â
agent-skills-architecture.md - “Agent ignores my conventions” â
agents-md-guide.md(conventions and enforcement section)
Phase 2 â Select Resource
Load the relevant resource file from the table above. Most setups require agents-md-guide.md as the foundation, with other resources added on top.
Phase 3 â Execute
Follow the specific guidance in the loaded resource. Output actionable file content (AGENTS.md, SKILL.md, mcp.json) â not just advice.
Common Task Workflows
Workflow 1: New Project AI Setup (15 minutes)
- Load
resources/agents-md-guide.mdâ createAGENTS.mdat repo root using the template - Add project overview, build commands, test instructions, code conventions
- Configure
.vscode/mcp.jsonwith at minimum the Bifrost server (seemcp-codebase-tools.md) - If the project has complex, repeatable workflows â package them as skills (see
agent-skills-architecture.md) - Commit all AI configuration files to version control alongside the codebase
Workflow 2: AGENTS.md for an Existing Repo
- Load
resources/agents-md-guide.md - Audit the existing
README.mdfor technical content that belongs in AGENTS.md - Extract: build commands, test scripts, folder structure map, conventions â move to AGENTS.md
- For monorepos: create root AGENTS.md + sub-directory AGENTS.md files for each package
- Add a “pointer” to
CLAUDE.mdorcopilot-instructions.mdreferencing AGENTS.md
Workflow 3: Preventing Agent Mistakes from Recurring
- Identify the pattern: did the agent use a wrong pattern/file/command?
- Load
resources/self-improvement-patterns.md - Update the relevant
AGENTS.mdsection with an explicit correction - If the mistake is workflow-specific â update or create a skill (
agent-skills-architecture.md) - Test: ask the agent to perform the same task; verify it now follows the corrected instruction
Workflow 4: Giving Agents Deep Code Navigation
- Load
resources/mcp-codebase-tools.md - Install Bifrost VS Code extension (provides call hierarchy, find usages, go-to-definition)
- Add server config to
.vscode/mcp.json - For semantic search â configure a vector-store MCP server (Qdrant or Azure AI Search)
- Document MCP tool names in AGENTS.md so the agent knows when to use them
Workflow 5: Self-Improving Agent Configuration
- After each significant debugging session, ask the agent: “What should we add to AGENTS.md to prevent this?”
- Review proposed update â approve and commit
- Monthly: use a “review” prompt against
self-improvement-patterns.mdto audit all config files - Over time: stale instructions become “context rot” â prune or update them proactively
Resource Summaries
| Resource | Purpose | Line Count |
|---|---|---|
resources/agents-md-guide.md |
AGENTS.md template, recommended sections, hierarchical discovery, comparison with CLAUDE.md/copilot-instructions.md | ~350 |
resources/agent-skills-architecture.md |
How to package project workflows as reusable SKILL.md-based skills with progressive disclosure | ~300 |
resources/mcp-codebase-tools.md |
MCP servers for live code navigation: Bifrost, vscode-mcp-server, semantic search, GitHub MCP | ~280 |
resources/self-improvement-patterns.md |
Context rot prevention, feedback loops, living documentation, meta-skills, memory architecture | ~300 |
Best Practices
- Identity vs. Capability: Use AGENTS.md for static project rules (identity); use skills for dynamic, executable workflows (capability). Do not put workflow logic in AGENTS.md.
- Commit AI config to version control: AGENTS.md, skills, and mcp.json are first-class project files â track them in git alongside code.
- Hierarchical AGENTS.md for monorepos: Root file holds global rules; sub-directory files override for local packages. Closest file wins.
- Treat agent mistakes as documentation bugs: Every repeated agent error is a missing or incorrect instruction in AGENTS.md or a skill. Fix the docs, not just the output.
- Pointer pattern: In
CLAUDE.mdwriteRead @AGENTS.mdâ avoids duplication across tool-specific instruction files. - Progressive disclosure in skills: Keep SKILL.md under 5,000 words; move heavy reference content to
resources/files loaded only when needed. - Name MCP tool names explicitly: Include exact MCP tool names in AGENTS.md so agents know which tools to invoke for code navigation tasks.