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explore-ai-codebase

📁 prakx1/ai-skills 📅 7 days ago
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npx skills add https://github.com/prakx1/ai-skills --skill explore-ai-codebase

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kimi-cli 1
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Skill 文档

AI Codebase Explorer

Systematically explore and explain any modern AI or agentic codebase. Produces structured explanations with ASCII diagrams.

When to Use

  • User asks to “explain this codebase”, “how does this work?”, or “explore the architecture”
  • Codebase involves: agents, MCP servers, LLM tools, RAG pipelines, multi-agent orchestration, AI workflows
  • User mentions: agents, MCP, LangGraph, CrewAI, AutoGen, OpenAI SDK, A2A, tools, chains, graphs, crews, teams, retrieval, embeddings

Phase 1: Identify the Codebase Type

Before exploring, determine what kind of AI codebase this is. Read the dependency file and entry points first.

Fingerprinting Table

Indicator Codebase Type
@modelcontextprotocol/sdk, fastmcp, mcp in deps MCP Server
langgraph, StateGraph, add_node, add_edge LangGraph Agent
crewai, Agent(), Task(), Crew(), yaml agent defs CrewAI System
autogen, AssistantAgent, GroupChat, RoundRobinGroupChat AutoGen Multi-Agent
openai-agents, Agent, Handoff, Guardrail OpenAI Agents SDK
langchain, AgentExecutor, create_react_agent LangChain Agent (legacy)
semantic-kernel Microsoft Semantic Kernel
llamaindex, VectorStoreIndex, QueryEngine LlamaIndex RAG
chromadb, pinecone, qdrant, weaviate, pgvector RAG Pipeline
/.well-known/agent.json, A2AServer, TaskManager A2A Protocol Agent
fastapi + LLM calls + tool definitions Custom Agent Framework
YAML-driven tool/agent configs + HTTP proxy Config-Driven MCP/Agent Server

Action: Once identified, use the matching exploration strategy from FRAMEWORK-PATTERNS.md.

Phase 2: Structural Discovery

Read files in this priority order. Stop early if you already understand the architecture.

Priority 1: Identity Files (read all of these)

pyproject.toml / package.json / go.mod / requirements.txt  → Stack and dependencies
README.md / docs/                                           → Architecture overview
main.py / src/index.* / app.py / __main__.py               → Entry point and startup flow

Priority 2: Configuration Files (read what exists)

*.yaml / *.yml config files     → Declarative definitions (tools, agents, servers, workflows)
.env / .env.example / env.*     → Environment variables (reveal integrations and secrets)
langgraph.json                  → LangGraph deployment config
agents.yaml / tasks.yaml        → CrewAI definitions
Dockerfile* / docker-compose*   → Deployment topology

Priority 3: Core Architecture (explore directories)

Look for these directory patterns and read key files in each:
  tools/          → Tool/function definitions
  agents/         → Agent definitions or classes
  resources/      → MCP resources or data providers
  handlers/       → Request handlers
  middleware/     → Middleware chain (auth, RBAC, validation)
  core/           → Core orchestration logic
  config/         → Configuration loading and schemas
  prompts/        → Prompt templates
  chains/ graphs/ → Workflow definitions
  crews/ teams/   → Multi-agent groupings
  auth/ authz/    → Authentication and authorization
  utils/          → Shared utilities
  integrations/   → External API clients

Priority 4: Supporting Files

tests/           → Test structure reveals architecture assumptions
schemas/         → Data models and validation
models/          → Domain models or LLM model configs

Phase 3: Architecture Mapping

After reading files, map the architecture across these dimensions:

Dimension 1: Control Flow

How do requests/tasks move through the system?

Questions to answer:
- What is the entry point? (HTTP server, CLI, stdio, event listener)
- How are requests routed? (router, graph, state machine, role assignment)
- What is the execution model? (sequential, parallel, graph-based, event-driven)
- Are there loops or cycles? (ReAct loops, retry logic, graph cycles)

Dimension 2: Tool/Capability Layer

What can the system do?

Questions to answer:
- How are tools/functions defined? (code, YAML, JSON schema, decorators)
- How are tools registered? (auto-discovery, manual, config-driven)
- How are tools invoked? (LLM tool calling, direct dispatch, proxy/composition)
- Are tools composed or chained? (pipelines, multi-step, proxied)

Dimension 3: State and Memory

What does the system remember?

Questions to answer:
- Is the system stateless or stateful?
- Where does state live? (in-memory, database, checkpoints, vector store)
- What is the state scope? (per-request, session, global, per-agent)
- Is there conversation memory or long-term memory?

Dimension 4: Security and Access Control

Who can do what?

Questions to answer:
- How do clients authenticate? (API keys, tokens, OAuth, mTLS)
- How is authorization enforced? (RBAC, per-tool ACLs, middleware, fail-closed)
- Is input validated? (schemas, sanitization, injection protection)
- How are secrets managed? (env vars, vaults, config files)

Dimension 5: Integrations and I/O

What does the system connect to?

Questions to answer:
- What external APIs or services does it call?
- What databases or stores does it use?
- What transport protocols? (HTTP, stdio, SSE, WebSocket, gRPC)
- How does it handle errors from external systems? (retries, circuit breakers, fallbacks)

Dimension 6: Observability

How do you know what’s happening?

Questions to answer:
- What logging framework? Structured or unstructured?
- Any tracing? (OpenTelemetry, Langfuse, LangSmith, custom)
- Health check endpoints?
- Metrics or monitoring?

Phase 4: Produce the Explanation

Output Structure

# [Project Name] - Codebase Overview

**Type**: [MCP Server | LangGraph Agent | CrewAI System | RAG Pipeline | Multi-Agent System | Custom Framework]
**Stack**: [Python 3.x + FastMCP + FastAPI | TypeScript + LangGraph | etc.]
**Key Dependencies**: [list 3-7 most important]

## Architecture

[ASCII architecture diagram - see Diagram Guidelines below]

## How It Works (Startup → Request → Response)

[Step-by-step flow of a typical request through the system]
[Include file:function references for each step]

## Key Components

### [Component Name]
**Location**: `path/to/files`
**Purpose**: [1-2 sentences]
**Interactions**: [what it talks to]
[Optional: component-specific diagram]

### [Next Component]
...

## Tool/Capability Inventory

[Table or list of all tools/capabilities with purpose and implementation location]

## Security Model

[Authentication, authorization, input validation, secrets management]

## Configuration

[How the system is configured: env vars, YAML, code, etc.]

## Data Flows

[1-2 sequence diagrams of key flows]

## Observations

**Strengths**: [architectural strengths]
**Potential Issues**: [things to watch out for]
**Suggestions**: [improvements to consider]

Diagram Guidelines

Architecture Diagrams

Show the system boundary, major components, and data flow:

┌─────────────────────────────────────────┐
│              System Boundary            │
│                                         │
│  ┌──────────┐     ┌──────────────────┐  │
│  │ Component│────>│   Component B    │  │
│  │    A     │     │  ┌────┐ ┌────┐   │  │
│  └──────────┘     │  │Sub1│ │Sub2│   │  │
│                   │  └────┘ └────┘   │  │
│                   └────────┬─────────┘  │
│                            │            │
└────────────────────────────┼────────────┘
                             │
                             ▼
                      External Service

Request Flow Diagrams

Show numbered steps with file references:

Client          Middleware      Handler         External
  │                │              │                │
  │──1. request──>│              │                │
  │               │──2. auth────>│                │
  │               │              │──3. API call──>│
  │               │              │<─4. response───│
  │<──5. result───│              │                │

Data Flow Diagrams

Show transformations:

Config YAML ──> Loader ──> Pydantic Models ──> Server Manager
                              │
                              ▼
                        Tool Registration

Decision Flow Diagrams

Show branching logic:

Request arrives
      │
      ├─ auth required? ──yes──> Validate token
      │                              │
      │                         ┌────┴────┐
      │                       valid     invalid
      │                         │         │
      │                         ▼         ▼
      │                      Continue   401 Deny
      │
      └─ no ──> Continue

Tips

  1. One concept per diagram; multiple simple > one complex
  2. Label every arrow and box
  3. Top-to-bottom or left-to-right flow
  4. Use ── for connections, │ for vertical, ▼▲◄► for direction
  5. Group related items with box borders ┌┐└┘

Deep-Dive Techniques

Trace a Request End-to-End

The most effective way to explain a codebase:

  1. Pick a concrete scenario (e.g., “user calls a tool”, “agent processes a task”)
  2. Start at the entry point and follow the code path
  3. Note every file, function, middleware, and decision point
  4. Document the return path
  5. Draw the sequence diagram

Read the Config Schema

In config-driven systems, the schema IS the architecture:

  • Read Pydantic models / Zod schemas / TypeScript interfaces
  • The fields and their types reveal what the system supports
  • Enum values reveal the options/modes
  • Optional vs required fields reveal what’s configurable vs fixed

Read the Middleware Chain

Middleware reveals cross-cutting concerns:

  • Authentication / authorization
  • Rate limiting
  • Input validation / sanitization
  • Logging / tracing
  • Error handling
  • Request transformation

Read middleware in execution order, not alphabetical order.

Read the Tests

Tests reveal:

  • What the system is supposed to do (happy paths)
  • What can go wrong (error cases)
  • How components are mocked (reveals boundaries)
  • Integration vs unit tests (reveals architecture layers)

Check the Deployment

Dockerfiles, CI pipelines, and K8s manifests reveal:

  • How the system is packaged
  • What environment variables it needs
  • What ports/endpoints it exposes
  • Health check mechanisms
  • Multi-service topology

Adaptive Detail Level

Quick Overview (user wants the gist):

  • One architecture diagram
  • Component list with one-line descriptions
  • “How it works” in 5 bullets

Standard Exploration (default):

  • Architecture diagram
  • Component deep-dives with file references
  • 1-2 flow diagrams
  • Security and config summary
  • Observations

Comprehensive Analysis (user wants everything):

  • Multiple diagrams (architecture, flows, decisions, state)
  • Every tool/capability documented
  • Complete request traces
  • Security analysis
  • Config analysis
  • Code quality observations
  • Deployment analysis
  • Suggestions for improvement

If unclear, default to Standard. Ask if the user wants more or less.

Discovery Checklist

Exploration Progress:
- [ ] Codebase type identified (MCP / LangGraph / CrewAI / etc.)
- [ ] Dependencies and stack mapped
- [ ] Entry point and startup flow traced
- [ ] Configuration system understood
- [ ] All tools/capabilities cataloged
- [ ] Request flow traced end-to-end
- [ ] Security model documented
- [ ] State management understood
- [ ] External integrations identified
- [ ] Architecture diagram created
- [ ] Key flow diagrams created
- [ ] Observations and suggestions noted

Pitfalls to Avoid

  1. Don’t assume one framework: Many production systems mix frameworks (e.g., FastMCP + LangGraph + custom middleware)
  2. Don’t skip config files: In declarative systems, YAML IS the architecture
  3. Don’t ignore the middleware chain: Security, auth, and validation live here
  4. Don’t confuse tool definition with implementation: Tools may be proxied to remote services
  5. Don’t assume stateless: Check for caches, sessions, checkpoints, databases
  6. Don’t miss dual-mode systems: Many codebases support both HTTP and stdio transport
  7. Don’t overlook error handling: Fail-open vs fail-closed reveals security posture
  8. Don’t ignore environment management: Different envs (dev/prod) may have wildly different configs

Additional Resources

For framework-specific patterns, see FRAMEWORK-PATTERNS.md. For ASCII diagram templates, see DIAGRAM-TEMPLATES.md.

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