Code Intelligence — Structural Understanding for AI Agents

Stop scanning. Start querying. Skeleton Index (<4s, zero deps) + AST graph + architecture diagrams = instant code understanding. Inspired by CodeGraph + GitDiagram. TRIZ-optimized: 10 inventive principles applied.

When to Use

ALWAYS for medium-to-large projects. This is infrastructure, not an action skill.

• Auto-triggered by:

  cm-start

  Step 0.7 (project init) — ALWAYS runs Layer 0
• Manually triggered for: "understand this codebase", "what calls X?", "what breaks if I change Y?"
• Skip when: NEVER — Layer 0 (Skeleton) works on any project size

Detection Thresholds (Auto-Trigger)

TRIGGER if ANY of these are true:
  → Project has >50 source files
  → User wants to refactor or re-code an existing project
  → User says "understand the codebase" / "what does this do?"
  → cm-execution encounters >3 grep/glob calls for one task
  → cm-debugging needs callers/callees to trace a bug

Architecture: 4 Layers

┌──────────────────────────────────────────────────────────────────────────┐
│                           cm-codeintell                                 │
├──────────────────┬──────────────────┬──────────────────┬────────────────┤
│  LAYER 0         │  LAYER 1         │  LAYER 2         │  LAYER 3       │
│  Skeleton Index  │  Code Graph      │  Architecture    │  Smart Context │
│  (Instant)       │  (Structure)     │  Diagram (Visual)│  (Synthesis)   │
├──────────────────┼──────────────────┼──────────────────┼────────────────┤
│ grep/find/awk    │ tree-sitter AST  │ File tree + LLM  │ All layers +   │
│ → skeleton.md    │ → SQLite graph   │ → Mermaid.js     │ qmd → focused  │
│ (~5K tokens)     │ → MCP server     │ → .cm/ storage   │ context packet │
├──────────────────┼──────────────────┼──────────────────┼────────────────┤
│ ZERO deps        │ codegraph_*      │ Auto-generated   │ Feeds: exec,   │
│ <4 seconds       │ MCP tools        │ at project init  │ plan, debug    │
│ ANY project size │ 50+ files        │ 20+ files        │ All consumers  │
└──────────────────┴──────────────────┴──────────────────┴────────────────┘

TRIZ Principles Applied

Layer 0: Skeleton Index (Instant — Zero Dependencies)

Purpose: Lightning-fast grep-based extraction of function signatures, class definitions, exports, and module boundaries. Produces a compact

.cm/skeleton.md

that gives the agent instant understanding of any codebase.

How It Works

1. SCAN     → find all source files (14 languages supported)
2. EXTRACT  → grep for function/class/export signatures only
3. GROUP    → organize by directory (module boundaries)
4. CAP      → limit per-dir (15 files) + total (600 lines)
5. OUTPUT   → .cm/skeleton.md (~5K tokens for 600-file project)

Usage

# Run from project root
bash scripts/index-codebase.sh

# Custom paths
bash scripts/index-codebase.sh /path/to/project /path/to/output.md

What It Extracts (Per Language)

export

function

class

interface

type

enum

const =

def

async def

class

@app.route

from...import

func

type...struct

type...interface

package

pub fn

struct

enum

impl

trait

mod

class

interface

fun

data class

package

function

class

interface

trait

namespace

def

class

module

struct

class

typedef

#define

func

class

struct

protocol

extension

Output Format

# 🦴 Skeleton Index: my-project

| Meta | Value |
|------|-------|
| Source Files | 127 |
| Languages | typescript(89) python(38) |
| Framework | next.js+cloudflare |

## Entry Points
- `src/index.ts`
- `app/layout.tsx`

## Directory Structure
(compact tree, depth 2)

## Code Skeleton
### `src/auth/`
**AuthService.ts**
‍‍‍
3:export class AuthService
5:export async function login(email, password)
12:export function validateToken(token)
‍‍‍

### `src/api/`
**routes.ts**
‍‍‍
8:export const router
15:router.get('/users'
22:router.post('/auth'
‍‍‍

Compression Stats

┌──────────────────┬────────────┬────────────────┬──────────────┐
│ Project Size     │ Raw Tokens │ Skeleton Tokens│ Compression  │
├──────────────────┼────────────┼────────────────┼──────────────┤
│ 50 files (small) │ ~20,000    │ ~1,500         │ 92.5%        │
│ 200 files (med)  │ ~80,000    │ ~3,000         │ 96.3%        │
│ 600 files (large)│ ~240,000   │ ~5,000         │ 97.9%        │
└──────────────────┴────────────┴────────────────┴──────────────┘

Agent Protocol

AT SESSION START:
  1. Check if .cm/skeleton.md exists
  2. IF exists → read it (~5K tokens) → instant codebase understanding
  3. IF not exists → run: bash scripts/index-codebase.sh
  4. Use skeleton to:
     → Know what functions exist and where
     → Understand module boundaries
     → Navigate to the right file for any task
     → Skip grep/list_dir when exploring

WHEN TO RE-GENERATE:
  → After major refactoring (>20 files changed)
  → After branch switch
  → When skeleton is >24h old
  → User requests: "re-index the codebase"

Layer 1: Code Graph (Structure)

Purpose: Pre-indexed AST-based knowledge graph. Functions, classes, imports, call relationships — all queryable instantly.

Setup

# Install CodeGraph (one-time)
npx @colbymchenry/codegraph

# Initialize for current project
codegraph init .

# Index the codebase (tree-sitter AST extraction)
codegraph index .

MCP Server Setup

Add to your MCP config (

.mcp.json

claude_desktop_config.json

{
  "mcpServers": {
    "codegraph": {
      "command": "codegraph",
      "args": ["serve"]
    }
  }
}

Key MCP Tools

codegraph_context(task)

codegraph_search(query)

grep -r "pattern"

codegraph_callers(symbol)

codegraph_callees(symbol)

codegraph_impact(symbol)

codegraph_files(path)

list_dir

view_file

codegraph_node(symbol)

view_file

When Agents Use These Tools

INSTEAD OF:                          USE:
─────────────────────────────────    ─────────────────────────
grep -r "UserService" src/           codegraph_search("UserService")
list_dir + view_file × 10           codegraph_context("implement auth")
"What calls validatePayment?"       codegraph_callers("validatePayment")
"What if I change this class?"      codegraph_impact("UserService", depth=2)
list_dir src/ --recursive            codegraph_files("src/", format="tree")

Keeping Index Fresh

AUTO-SYNC (built-in):
  → CodeGraph hooks auto-sync when files change (if hooks installed)

MANUAL SYNC (if hooks not installed):
  → codegraph sync .

WHEN TO RE-INDEX:
  → After major refactoring (>20 files changed)
  → After branch switch
  → When codegraph_status reports stale index

AI RULE: Before starting any task, check:
  → codegraph status .
  → If stale → codegraph sync . → then proceed

Layer 2: Architecture Diagram (Visual)

Purpose: Auto-generated Mermaid.js architecture diagram from project structure. See the big picture at a glance.

Generation Process

1. EXTRACT  → Read file tree structure (codegraph_files or list_dir)
2. ANALYZE  → Identify key directories, patterns, entry points
3. GENERATE → Produce Mermaid.js diagram showing:
              - Major modules/directories
              - Key relationships (imports, API boundaries)
              - Entry points (main, routes, handlers)
              - Data flow direction
4. STORE    → Save to .cm/architecture.mmd
5. RENDER   → Display inline or via Pencil MCP

Diagram Template

When generating the architecture diagram, use this Mermaid structure:

## Architecture Diagram

​```mermaid
graph TD
    subgraph "Frontend"
        A[pages/] --> B[components/]
        B --> C[hooks/]
        C --> D[utils/]
    end

    subgraph "Backend"
        E[routes/] --> F[controllers/]
        F --> G[services/]
        G --> H[models/]
    end

    subgraph "Infrastructure"
        I[config/]
        J[middleware/]
        K[database/]
    end

    A -->|API calls| E
    G --> K
    J --> E
​```

When to Generate

AUTO-GENERATE at:
  → cm-start Step 0.5 (project init)
  → cm-brainstorm-idea Phase 1a (codebase scan)
  → First time running cm-codeintell on a project

RE-GENERATE when:
  → Major architectural change (new module, new service)
  → User requests: "update the architecture diagram"
  → >30 files added/removed since last generation

STORE at:
  → .cm/architecture.mmd (Mermaid source)
  → Include in proposal.md when relevant

Integration with Pencil MCP

If Pencil MCP is available, render the diagram visually:

1. Generate Mermaid code → .cm/architecture.mmd
2. If pencil MCP available → render as visual node
3. If not → display Mermaid code inline (agents can parse it)

Layer 3: Smart Context Builder (Synthesis)

Purpose: Combine graph data + diagram + text search into a focused context packet for any task.

Context Building Protocol

When any CM skill needs to understand the codebase for a specific task:

1. QUERY GRAPH     → codegraph_context(task, maxNodes=20)
                     Returns: entry points, related symbols, code snippets

2. CHECK DIAGRAM   → Read .cm/architecture.mmd
                     Identify which module/layer the task affects

3. SEARCH DOCS     → IF qmd available: qmd query "task description"
                     Returns: relevant documentation, past decisions

4. COMPOSE PACKET  → Merge results into a structured context:
                     {
                       "task": "...",
                       "affected_modules": ["..."],
                       "entry_points": ["..."],
                       "related_symbols": ["..."],
                       "impact_radius": ["..."],
                       "relevant_docs": ["..."],
                       "architecture_context": "..."
                     }

5. FEED DOWNSTREAM → Pass context packet to requesting skill

Adaptive Intelligence Levels

┌──────────────┬────────────┬─────────────────────────────────────────────────┐
│ Project Size │ Level      │ What Activates                                  │
├──────────────┼────────────┼─────────────────────────────────────────────────┤
│ ANY size     │ SKELETON   │ Skeleton Index always runs (Layer 0)             │
│ <20 files    │ MINIMAL    │ Skeleton only (no graph, no diagram)             │
│ 20-50 files  │ LITE       │ Skeleton + architecture diagram                  │
│ 50-200 files │ STANDARD   │ Skeleton + CodeGraph + diagram                   │
│ >200 files   │ FULL       │ Skeleton + CodeGraph + diagram + qmd             │
└──────────────┴────────────┴─────────────────────────────────────────────────┘

Skeleton Index ALWAYS runs — it's the foundation for all levels.
Detection is automatic at cm-start Step 0.7.
User can override: "Use FULL intelligence mode"

Integration with CodyMaster Skills

cm-start (Step 0.5 — enhanced)

EXISTING Step 0.5: Skill Coverage Check
NEW addition:

  0.5b. Code Intelligence Setup:
    1. Count source files → determine intelligence level
    2. IF level >= LITE:
       → Auto-generate architecture diagram → .cm/architecture.mmd
    3. IF level >= STANDARD:
       → Check if CodeGraph installed: codegraph status
       → IF not installed → suggest: "npx @colbymchenry/codegraph"
       → IF installed but not indexed → codegraph init . && codegraph index .
       → IF indexed → codegraph sync . (ensure fresh)
    4. IF level >= FULL:
       → Also check qmd (cm-deep-search detection)
    5. Log intelligence level to CONTINUITY.md

cm-execution (Pre-flight — enhanced)

EXISTING Pre-flight: Skill Coverage Audit
NEW addition:

  Pre-flight Step 2: Code Context Loading
    IF codegraph available:
      → For each task in current batch:
        → context = codegraph_context(task.description, maxNodes=15)
        → Inject context into agent prompt
      → For tasks modifying shared code:
        → impact = codegraph_impact(symbol, depth=2)
        → If impact.affected > 10 files → WARN: "High impact change"

    Result: Agents start with pre-loaded context instead of exploring

cm-planning (Impact Analysis — new)

NEW addition to Phase A:

  Before writing implementation plan:
    1. For each proposed change:
       → codegraph_impact(affected_symbol) → list affected files
    2. If total impact > 20 files:
       → Flag as HIGH RISK in plan
       → Recommend cm-tdd coverage for all impacted callers
    3. Include impact summary in OpenSpec `design.md`

cm-debugging (Trace Analysis — enhanced)

EXISTING Phase 2: Hypothesis Formation
NEW enhancement:

  IF codegraph available:
    1. From error stack trace → extract function name
    2. codegraph_callers(function) → who calls this?
    3. codegraph_callees(function) → what does it call?
    4. codegraph_impact(function) → what else is affected?
    5. Use call chain to narrow hypotheses

  Result: Root cause found in 1-2 queries instead of 5-10 grep calls

cm-brainstorm-idea (Phase 1a — enhanced)

EXISTING Phase 1a: Codebase Scan
NEW enhancement:

  1. Read .cm/architecture.mmd for instant overview
  2. IF codegraph available:
     → codegraph_files(".", format="tree", includeMetadata=true)
     → Summary: X symbols, Y edges, Z files
  3. Present architecture diagram to user in Discovery output
  4. Use graph to identify:
     → Most connected modules (highest coupling)
     → Isolated modules (candidates for parallel work)
     → Dead code (unreferenced symbols)

File Storage

.cm/
├── skeleton.md               # Skeleton Index output (Layer 0)
├── architecture.mmd          # Mermaid architecture diagram
├── codegraph-meta.json       # Graph metadata (last indexed, stats)
├── CONTINUITY.md             # (existing) — updated with intelligence level
├── learnings.json            # (existing)
└── decisions.json            # (existing)

.codegraph/                   # CodeGraph's own directory (auto-created)
├── codegraph.db              # SQLite graph database
└── config.json               # CodeGraph configuration

codegraph-meta.json Format

{
  "intelligenceLevel": "STANDARD",
  "lastIndexed": "2026-03-25T22:25:00+07:00",
  "stats": {
    "sourceFiles": 127,
    "symbols": 387,
    "edges": 1204,
    "languages": ["typescript", "javascript"]
  },
  "diagramGenerated": "2026-03-25T22:25:30+07:00",
  "codegraphVersion": "1.0.0"
}

Lifecycle Position

cm-project-bootstrap → cm-codeintell (auto) → cm-brainstorm-idea → cm-planning → cm-execution
      (create)          (index + diagram)         (analyze)           (plan)        (implement)
                              ↑                                         ↓
                         cm-debugging ←──── cm-quality-gate ←──── cm-tdd
                        (trace callers)     (verify)            (test first)

Memory System (Updated)

Tier 1: SENSORY        → Temporary session variables
Tier 2: WORKING        → CONTINUITY.md (~500 words)
Tier 3: LONG-TERM      → learnings.json, decisions.json
Tier 4: SEMANTIC TEXT   → qmd (BM25 + vector over docs/text)
Tier 5: STRUCTURAL     → CodeGraph (AST symbols + call graph)  ← NEW

Integration Table

cm-start

cm-execution

cm-planning

cm-debugging

cm-brainstorm-idea

cm-deep-search

cm-continuity

cm-tdd

cm-safe-deploy

cm-dockit

Rules

✅ DO:
- Auto-detect project size and select appropriate intelligence level
- Keep graph index fresh (sync before major tasks)
- Use codegraph_context INSTEAD of grep/glob for code exploration
- Generate architecture diagram at project init
- Store metadata in .cm/codegraph-meta.json
- Feed context to downstream skills (execution, planning, debugging)

❌ DON'T:
- Force CodeGraph on tiny projects (<20 files)
- Skip freshness checks (stale index worse than no index)
- Use codegraph as REPLACEMENT for qmd (they complement each other)
- Assume codegraph is installed — always check first
- Generate diagrams without validating Mermaid syntax
- Store sensitive code in architecture diagrams

Requirements

Layer 0 (Skeleton Index):
  - ZERO dependencies (grep, find, awk — standard POSIX)
  - Works on any OS (macOS, Linux, WSL)
  - <4 seconds for 600-file projects

Layer 1 (CodeGraph):
  - Node.js 18+ (for tree-sitter binaries)
  - npx @colbymchenry/codegraph (one-time install)
  - ~50MB disk for SQLite + embeddings per project

Layer 2 (Diagrams):
  - No additional dependencies (uses agent's LLM)
  - Mermaid.js knowledge (built into agent)

Layer 3 (Smart Context):
  - Layer 0 required (always available)
  - Layers 1 + 2 optional upgrades
  - Optional: qmd for text search complement

The Bottom Line

Skeleton Index = instant understanding. Code graph = deep meaning. Architecture diagrams = big picture. Together = AI that truly understands your code.