Context Map

Generate a compressed context map for the current project. The map pre-compiles structural knowledge that AI assistants would otherwise discover through expensive Read/Grep calls, saving thousands of tokens per session.

When to Use

At the start of a session to understand project layout
Before implementing features to identify entry points
When exploring an unfamiliar codebase
To reduce token waste from Read calls
To identify hot files (high blast radius) before changes

What It Detects

| Category | Description | |----------|-------------| | Structure | Directory layout with file counts and languages | | Dependencies | Multi-ecosystem: Python, Node, Rust, Go, Java | | Frameworks | Framework detection from dependency analysis | | Entry Points | main.py, index.ts, CLI scripts, etc. | | Import Graph | File-to-file import relationships | | Hot Files | Files imported by 3+ others (high blast radius) | | Routes | FastAPI, Flask, Express, Hono API endpoints | | Env Vars | Environment variable references with defaults | | Middleware | Auth, CORS, rate-limit, logging patterns | | Models/Schemas | SQLAlchemy, Django, Pydantic, Prisma definitions | | Token Savings | Estimated tokens saved vs manual exploration |

Procedure

Run the scanner on the project root:

PYTHONPATH="$(find . -path '*/conserve/scripts' -type d \
  -print -quit 2>/dev/null || \
  echo 'plugins/conserve/scripts')" \
  python3 -m context_scanner .

Present the output to the user as the project overview.

Use the context map to guide subsequent file reads.

Prioritize hot files and entry points first.

Options

Output

--format json for structured output
--max-tokens N to adjust output size (default: 5000)
--output FILE to save to a file

Modes

--blast FILE to show blast radius for a specific file
--section NAME to output a single section

(routes, deps, env, hot-files, models, structure, middleware, frameworks)

--wiki-only to generate wiki articles without stdout

Opt-out

--no-cache to force a fresh scan
--no-wiki to skip wiki article generation

Wiki Articles

The scanner generates per-topic knowledge articles in .codesight/ for selective context loading:

python3 scanner.py .
# Creates .codesight/INDEX.md, auth.md, database.md, etc.

Load only what you need per session instead of the full map:

python3 scanner.py --section routes .
# ~200 tokens vs ~5,000 for the full map

Example Output

# Context Map: myproject
Files: 127

## Structure
  src                  42 files (Python)
  tests                18 files (Python)
  docs                  5 files (Markdown)

## Dependencies (Python)
Package manager: uv
  - fastapi 0.104.0
  - pydantic 2.5.0
  - sqlalchemy 2.0.0
  ...12 more

## Frameworks Detected
  - FastAPI
  - SQLAlchemy
  - Pytest

## Routes
  GET    /users          (src/routes/users.py)
  POST   /users          (src/routes/users.py)
  GET    /users/{id}     (src/routes/users.py)

## Hot Files (high blast radius)
  - src/models/base.py (12 importers)
  - src/utils/auth.py (8 importers)

## Environment Variables
  - DATABASE_URL (required)
  - SECRET_KEY (has default)

## Token Savings: ~12,600 tokens saved
  Routes: ~1,200
  Hot files: ~300
  Env vars: ~200
  File scanning: ~10,200

context-map

Como adicionar

Cole no README do seu repo

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