---

name: codebase-context description: Generates a codebase-context.md file that provides pre-built context for AI agents. This skill analyzes your project structure, patterns, and conventions to create a comprehensive context document that reduces token usage and improves agent effectiveness. Use when setting up a new project for AI-assisted development or when the codebase has significantly changed.

Codebase Context Generator

Purpose

Generate context files that help AI agents understand your codebase without blind exploration:

| File | Purpose | Format | |------|---------|--------| | codebase-context.md | Human-readable project overview | Markdown | | code-index.json | Fast lookups for concepts, exports, types | JSON | | deps.mermaid | Dependency graph for tracing imports | Mermaid |

Quick Start

Run the extraction scripts to generate machine-readable context:

# Generate all context files (code-index.json, deps.mermaid, deps.json)
~/.claude/skills/codebase-context/scripts/generate-all.sh .

# Or run individually:
~/.claude/skills/codebase-context/scripts/generate-code-index.sh .
~/.claude/skills/codebase-context/scripts/generate-deps.sh . mermaid

Then use the skill to generate the human-readable context:

/codebase-context

When to Use

• Setting up a new project for AI-assisted development
• After significant architectural changes
• When onboarding new team members who use AI tools
• When agents are spending too many tokens exploring the codebase
• When staleness check indicates context is outdated

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Integration with CLAUDE.md

Recommended: Progressive Disclosure

Add a single reference line in your project's CLAUDE.md:

For full project structure and workflows, see `.claude/codebase-context.md`.

This lets agents discover the context file when they need to explore, without forcing it to load on every request.

Why NOT to use "CRITICAL: Read First" blocks

Earlier versions of this skill recommended adding a mandatory preload block at the top of CLAUDE.md:

## CRITICAL: Read Codebase Context First
BEFORE using Glob, Grep, or exploring the codebase, you MUST read...

This approach has problems:

1. Token waste on targeted tasks — A request like "fix this typo in RecipeDetailScreen" doesn't need 400 lines of project structure loaded into context.
2. Competes with instruction budget — LLMs can follow ~150-200 instructions consistently. Every line of context displaces task-specific instructions.
3. Staleness risk — File paths change constantly. Stale paths in mandatory context actively mislead the agent, which is worse than no context.
4. Defeats progressive disclosure — In monorepos, Claude Code already merges CLAUDE.md files from root down to the working directory. Package-level files give you free progressive disclosure.

For Monorepos

Use package-level CLAUDE.md files instead of cramming everything into root:

CLAUDE.md                  # Project overview, commands, rules (~50 lines)
apps/mobile/CLAUDE.md      # Mobile patterns, design system, workflows
apps/api/CLAUDE.md         # API conventions, DB patterns, auth flow

Each file only loads when working in that directory.

---

Lessons Learned: Keeping Context Lean

Based on real-world usage, here's what works and what doesn't in context files:

What to include

| Content | Why | |---------|-----| | One-line project description | Anchors every agent decision | | Directory tree (top 2-3 levels) | Shows structure without file-path brittleness | | Domain concepts/vocabulary | Stable across refactors, prevents misunderstandings | | Key workflows (1 line each) | Shows how features connect | | Patterns and conventions | Prevents style drift | | Gotchas | Saves debugging time |

What to leave out

| Content | Why | |---------|-----| | Environment variable examples | Sensitive, discoverable from .env.example | | Code snippets/API usage examples | Go stale fast, agent can read the actual code | | Full file listings per screen/feature | Too granular, changes frequently | | App Store / deployment details | Rarely relevant to coding tasks | | Marketing site info | Not relevant to development | | Build/deploy commands beyond basics | Discoverable from package.json |

Target size

Aim for 80-150 lines in codebase-context.md. If it's over 200, you're probably including things that belong in package-level CLAUDE.md files or separate docs.

---

Staleness Detection

Before reading codebase-context.md, check if it's stale by running:

.claude/check-context-freshness.sh

If the script exits non-zero or prints "STALE", regenerate the context.

How Staleness Detection Works

When generating context, also create .claude/codebase-context.snapshot containing:

• Directory tree hash (top 3 levels)
• Key config file checksums (package.json, tsconfig.json, etc.)
• Generation timestamp

The freshness check compares current state against the snapshot.

Generate the Freshness Check Script

After generating codebase-context.md, create .claude/check-context-freshness.sh:

#!/bin/bash
# Check if codebase-context.md needs regeneration

SNAPSHOT_FILE=".claude/codebase-context.snapshot"
CONTEXT_FILE=".claude/codebase-context.md"

if [ ! -f "$CONTEXT_FILE" ]; then
    echo "STALE: No context file found"
    exit 1
fi

if [ ! -f "$SNAPSHOT_FILE" ]; then
    echo "STALE: No snapshot file found"
    exit 1
fi

CURRENT_TREE=$(find . -maxdepth 3 -type d -not -path '*/\.*' -not -path './node_modules*' -not -path './dist*' -not -path './build*' -not -path './.next*' 2>/dev/null | sort | md5sum | cut -d' ' -f1)
STORED_TREE=$(grep "^tree:" "$SNAPSHOT_FILE" 2>/dev/null | cut -d' ' -f2)

if [ "$CURRENT_TREE" != "$STORED_TREE" ]; then
    echo "STALE: Directory structure changed"
    exit 1
fi

for CONFIG in package.json tsconfig.json pyproject.toml Cargo.toml go.mod; do
    if [ -f "$CONFIG" ]; then
        CURRENT_HASH=$(md5sum "$CONFIG" 2>/dev/null | cut -d' ' -f1)
        STORED_HASH=$(grep "^$CONFIG:" "$SNAPSHOT_FILE" 2>/dev/null | cut -d' ' -f2)
        if [ "$CURRENT_HASH" != "$STORED_HASH" ]; then
            echo "STALE: $CONFIG changed"
            exit 1
        fi
    fi
done

GENERATED=$(grep "^generated:" "$SNAPSHOT_FILE" 2>/dev/null | cut -d' ' -f2)
if [ -n "$GENERATED" ]; then
    NOW=$(date +%s)
    AGE=$((NOW - GENERATED))
    DAYS=$((AGE / 86400))
    if [ $DAYS -gt 7 ]; then
        echo "STALE: Context is $DAYS days old (recommend regenerating weekly)"
        exit 1
    fi
fi

echo "FRESH: Context is up to date"
exit 0

Generate the Snapshot

After generating codebase-context.md, create .claude/codebase-context.snapshot:

#!/bin/bash
SNAPSHOT_FILE=".claude/codebase-context.snapshot"

TREE_HASH=$(find . -maxdepth 3 -type d -not -path '*/\.*' -not -path './node_modules*' -not -path './dist*' -not -path './build*' -not -path './.next*' 2>/dev/null | sort | md5sum | cut -d' ' -f1)

echo "tree: $TREE_HASH" > "$SNAPSHOT_FILE"
echo "generated: $(date +%s)" >> "$SNAPSHOT_FILE"

for CONFIG in package.json tsconfig.json pyproject.toml Cargo.toml go.mod; do
    if [ -f "$CONFIG" ]; then
        HASH=$(md5sum "$CONFIG" | cut -d' ' -f1)
        echo "$CONFIG: $HASH" >> "$SNAPSHOT_FILE"
    fi
done

echo "Snapshot saved to $SNAPSHOT_FILE"

---

Generation Process

Step 1: Analyze Project Structure

Explore the codebase to understand:

1. Project type and stack — Framework, language, package manager
2. Directory structure — Key folders and their purposes (top 2-3 levels)
3. Domain concepts — Business vocabulary and key workflows
4. Patterns & conventions — Naming, code style, state management

Step 2: Write a Lean Context File

Create .claude/codebase-context.md with these sections (target: 80-150 lines):

# Codebase Context

> Optional reference for AI agents. Read when you need to understand project structure.

## Project Overview
- One-line description, type, package manager

## Tech Stack
- Table of layer → technology

## Directory Structure
- Tree view, top 2-3 levels with purpose annotations

## Domain Concepts
- Table of term → meaning

## Key Workflows
- One line per workflow showing the flow

## Patterns
- Bullet list of conventions (styling, naming, auth, etc.)

## Gotchas
- Non-obvious behaviors that waste debugging time

Omit: env vars, code snippets, deployment details, marketing content, full file listings.

Step 3: Save Files

1. .claude/codebase-context.md — The context document
2. .claude/codebase-context.snapshot — Freshness snapshot
3. .claude/check-context-freshness.sh — Freshness check script (make executable)

chmod +x .claude/check-context-freshness.sh

Step 4: Add Reference to CLAUDE.md

Add one line to the project's CLAUDE.md (do NOT add a mandatory preload block):

For full project structure and workflows, see `.claude/codebase-context.md`.

---

Code Index (Lightweight Search)

In addition to the human-readable context file, generate a machine-optimized code index for fast lookups.

Purpose

The code index provides O(1) lookups for common agent queries:

• "Where is authentication handled?" → Jump directly to relevant files
• "What exports does this module have?" → Instant answer without reading files
• "What depends on this file?" → Trace dependencies without exploration

Generate code-index.json

After generating codebase-context.md, also create .claude/code-index.json:

{
  "version": "1.0",
  "generated": 1706140800,
  "concepts": {
    "authentication": ["src/auth/login.ts:15", "src/middleware/jwt.ts:1"],
    "error handling": ["src/utils/errors.ts:1", "src/api/middleware.ts:42"],
    "database": ["src/db/client.ts:1", "src/db/queries.ts:1"]
  },
  "entry_points": {
    "POST /api/login": "src/app/api/auth/login/route.ts:15",
    "GET /api/users": "src/app/api/users/route.ts:8"
  },
  "exports": {
    "src/lib/db/client.ts": ["prisma", "PrismaClient"],
    "src/lib/utils/index.ts": ["cn", "formatDate", "debounce"]
  },
  "types": {
    "User": "src/types/user.ts:5",
    "Task": "src/types/task.ts:3"
  }
}

Index Sections

concepts: Maps domain concepts to file locations entry_points: Maps API routes/CLI commands to handlers exports: Maps files to their public exports types: Maps type/interface names to definitions

---

Dependency Graph

Generate a lightweight dependency graph to answer "what depends on what?" questions.

Generate deps.mermaid

Create .claude/deps.mermaid with a Mermaid flowchart:

graph LR
  subgraph API
    api/users.ts --> db/queries.ts
    api/auth.ts --> lib/jwt.ts
  end

  subgraph Database
    db/queries.ts --> db/client.ts
  end

How to Generate

For JavaScript/TypeScript projects:

npx madge --json src > .claude/deps.json
npx madge --dot src > .claude/deps.dot

Manual extraction (any language):

• Grep for import statements
• Build adjacency list: file → [imported files]
• Output as Mermaid or DOT

Graph Scope

Keep the graph focused:

• Include: source files that define core logic
• Exclude: node_modules, vendor, test files, type-only imports
• Limit depth: top 2-3 levels of the import tree

---

Output Structure

After generation, the .claude/ directory contains:

.claude/
├── codebase-context.md           # Human-readable context (80-150 lines)
├── codebase-context.snapshot     # Freshness snapshot
├── code-index.json               # Machine-optimized lookups
├── deps.mermaid                  # Dependency graph
└── check-context-freshness.sh    # Freshness check script

---

Best Practices

1. Progressive disclosure — Reference context from CLAUDE.md, don't mandate preloading it
2. Keep it lean — Target 80-150 lines; if over 200, split into package-level files
3. Concepts over file paths — Domain vocabulary is stable; file paths change constantly
4. Check freshness — Run the check script before trusting the context
5. Monorepo structure — Use package-level CLAUDE.md files for package-specific guidance
6. Commit the files — Version control context, snapshot, and check script

Maintenance

The staleness check detects when regeneration is needed:

• Directory structure changes (new folders, reorganization)
• Config file changes (package.json, tsconfig.json, etc.)
• Age > 7 days

Manual regeneration triggers:

• Adding new major features
• Changing tech stack components
• Modifying conventions or patterns

---

Companion Tools

grepai — Semantic Code Search

grepai enables natural language code search using vector embeddings. 100% local (uses Ollama), supports MCP server for Claude Code integration.

grepai search "user authentication flow"
grepai trace callers myFunction

Recommended Workflow

| Step | Tool | Purpose | |------|------|---------| | 1 | codebase-context.md | Understand project structure | | 2 | code-index.json | Fast lookups (concepts, exports, types) | | 3 | deps.mermaid | Trace dependencies | | 4 | grepai | Find code by semantic meaning (if installed) | | 5 | Glob/Grep | Exact pattern matching |