TodoScope

TodoScope finds maintenance comments (TODO, FIXME, ...) in your code and prints a clean report. Optionally, it asks an AI to interpret each comment and estimate its priority — without ever sending your source code anywhere.

todoscope src/

What it does

• Scans Python, JavaScript, TypeScript, JSX/TSX, and Rust files for comments that start with your markers (TODO by default).
• Only real comments count: TODO inside strings, template literals, JSX text, or raw strings is ignored.
• Respects your .gitignore and an optional exclusion list.
• Works fully offline — the AI part is optional.
• When AI is on, it sends only each comment's ID, marker, and text. No file names, no paths, no line numbers, no code.

Install

Requires Python 3.12+.

pipx install todoscope        # recommended
# or
uv tool install todoscope     # if you use uv
# or
python3 -m pip install todoscope

Use

todoscope src/                # scan a folder recursively
todoscope src/main.py         # scan one file
todoscope .                   # scan the whole project
todoscope src/ --no-ai        # normal report, skip AI
todoscope src/ --quiet        # one line per finding, no headings, no AI
todoscope src/ --verbose      # extra details on stderr
todoscope src/ --format json  # machine-readable JSON report on stdout

That's it. Findings are sorted by folder depth, then path, then line.

--format json prints a deterministic JSON document to stdout (scan metadata, findings, skipped counts, and the AI section with a machine- readable status/reason). Verbose details and errors always go to stderr. JSON never contains API keys or environment values.

Configuration

Everything optional lives in a .todoscope.json in your project root:

{
  "markers": ["TODO", "FIXME"],
  "extensions": [".py", ".js", ".jsx", ".ts", ".tsx", ".rs"],
  "exclude": ["tests/fixtures/", "generated/"],
  "model": "your-ai-model-id",
  "max_ai_characters": 20000
}

Invalid configuration stops with a clear error (exit code 3).

AI analysis

To enable it you need both:

1. An API key — from your shell (TODOSCOPE_API_KEY) or a .env file in the project root:

   TODOSCOPE_API_KEY=...
   TODOSCOPE_SECONDARY_API_KEY=...
   

   Shell values win over .env. If a key comes from .env, that file must be ignored by your .gitignore, otherwise AI is refused for safety.

2. A model in .todoscope.json.

When enabled, TodoScope makes one request and then prints one complete report: per finding you get a short interpretation and an estimated priority (High / Medium / Low / Unclear), plus an overall summary. If the request fails and a secondary key is configured, an interactive terminal offers one retry with it — the secondary key is never used silently.

Priorities are estimated from comment text only. No source code was provided to the AI.

Using DeepSeek (or another OpenAI-compatible provider)

The OpenAI SDK reads OPENAI_BASE_URL from your environment. For DeepSeek:

export OPENAI_BASE_URL=https://api.deepseek.com
todoscope .

or as a permanent alias in ~/.zshrc:

alias todoscope="OPENAI_BASE_URL=https://api.deepseek.com /home/$USER/.local/bin/todoscope"

Privacy

The only data from your repository that reaches the AI is each finding's ID, marker, and extracted comment text. Everything else stays local. Comments are treated as untrusted data — instructions written inside a comment can never change TodoScope's behaviour. Never put credentials or secrets in code comments, because comment text may be sent to the AI.

Exit codes

• 0 — scan finished (including local-only results after any AI problem)
• 1 — unexpected failure
• 2 — bad path/usage, or an ignored target refused in non-interactive mode
• 3 — configuration error

Development

uv sync                       # set up the environment
uv run pytest                 # tests
uv run ruff check .           # lint
uv run ruff format --check .  # format check
uv build                      # wheel + sdist

Continuous integration runs these same checks on every push and pull request (Python 3.12 and 3.13).

Releasing

1. Bump version in pyproject.toml (minor for features, patch for fixes).
2. Add a CHANGELOG.md entry for the new version.
3. Commit, then tag and push the tag:

git tag v0.2.0
git push
git push --tags

The publish workflow verifies everything, uploads to PyPI using the PYPI_TOKEN repository secret, and creates a GitHub release automatically.

Changelog

See CHANGELOG.md.