MODScan

Scan a codebase, get everything you need to write plugins and mods for it.

MODScan reads a source-available project, finds where it can be extended — hooks, event systems, dynamic loading, dependency injection, config-driven behavior — and generates modding/plugin documentation grounded in real static analysis. It doesn't just describe the code (Doxygen already does that); it maps the seams a modder actually hooks into, then writes a "how to build a plugin" guide and a working example plugin that it validates by loading it for real.

Status: v0.1.0 (pre-release). The full pipeline works end to end for Python, with experimental TypeScript/JavaScript support. The extension-point ranking is the rough edge — on large codebases it surfaces plausible but low-value seams. See the open issues if you want to help sharpen it.

Why

Great mods and plugins have turned plain games and apps into masterpieces. But getting started modding a project is painful: you have to reverse-engineer the architecture yourself to find where you're even allowed to plug in. MODScan automates that discovery step.

What makes it different

Existing tools generate API docs from source. MODScan focuses on the hard, valuable part everyone skips: extension-point discovery.

• Detects hooks, event/callback systems, dynamic import / plugin discovery, registration decorators, subclassable interfaces (ABCs / Protocols), and config/data-driven behavior.
• Ranks seams by how moddable they are.
• Grounds all generated docs in static analysis — facts come from the parser, prose comes from the LLM, nothing is invented.
• Closes the loop: the example plugin it generates must actually load into the target for the docs to be considered correct.

How it works

flowchart TD
    SRC["Source-available codebase"]
    SRC --> PARSE["1 · Parse — AST into a shared model<br/>deterministic, no LLM"]
    PARSE --> GRAPH["2 · Extension graph — dependencies and public seams"]
    GRAPH --> DETECT["3 · Detect — score and rank seams by moddability"]

    DETECT -->|"modscan detect · no LLM, no API key"| RANK["Ranked extension points<br/>Markdown or JSON"]

    DETECT --> PROBE{"Pre-flight:<br/>does the target import?"}
    PROBE -->|no| FAIL["Stop early — cause plus a pip install remediation<br/>no LLM call spent"]
    PROBE -->|yes| VALIDATE["5 · Validate — load each seam against the target"]
    VALIDATE --> FACTS["FactBlocks — facts from static analysis only"]
    FACTS --> LLM["4 · Doc generator — LLM prose grounded on FactBlocks"]
    LLM --> DOCS["modding-docs/<br/>index.md · plugin-guide.md · examples/ · extension-points.json"]
    DOCS --> SCAFFOLD["modscan scaffold — a plugin skeleton from the manifest"]

    classDef trust stroke-dasharray:5 5;
    class VALIDATE,LLM trust;

Facts come from the parser, prose from the LLM, correctness from the validator. Layers 1–3 are deterministic and verifiable; the LLM (layer 4) only ever sees the structured FactBlocks, never raw source, so it explains what the analysis found rather than inventing it. The Validator (layer 5) is built before the doc generator, so every later stage is measurable against a plugin that really loads.

The dashed stages import and execute target code — that is where a real plugin is loaded to prove a seam. Run only on code you trust; --sandbox contains it in a child process, and --no-validate-examples skips execution entirely (and, with it, the pre-flight probe).

Scope (MVP)

Note on closed / binary apps. Modding a compiled, closed-source application (a typical commercial game) means decompilation and reverse engineering, which carries real legal implications (EULA, DMCA). That is deliberately out of the MVP. MODScan starts with code you are allowed to read and modify.

Languages

Python is the primary, fully-integrated target. TypeScript/JavaScript parsing is experimental (via tree-sitter): it feeds the graph and detector, so extension points and docs work, but example execution-validation is Python-only for now. Install with pip install modscan[typescript]; the front-end registers under typescript and javascript.

LLM providers

The doc generator is provider-agnostic. Pick your model; SDKs are optional deps imported lazily, so you install only what you use. API keys come from env vars, never hardcoded.

Output

Two artifacts, one for humans and one for tools:

• modding-docs/*.md — architecture overview + per-seam plugin guide with a validated example plugin.
• modding-docs/extension-points.json — a versioned, machine-readable manifest of every validated extension point. This is the contract that will power modscan scaffold <id>, editor tooling, and breaking-change diffs.

Every generated example is re-loaded against the target to confirm it works; ones that can't be validated are clearly marked unverified.

Roadmap

1. ✅ AST parser + extension graph (Python)
2. ✅ Extension detector + moddability ranking
3. ✅ Validator — load a real example plugin against a detected seam
4. ✅ Doc generator (LLM, grounded) — Markdown + JSON manifest
5. ✅ modscan ./path CLI wrapper, end to end
6. ✅ modscan scaffold <id> — generate a plugin skeleton from the JSON manifest
7. ✅ TypeScript/JavaScript front-end (experimental), breaking-change diffs, sandboxed validation, spend controls
8. ✅ modscan detect (offline ranking), GitHub Action, and MCP server

See ROADMAP.md for what's next, an honest account of where the ranking works and where it doesn't (measured across six real packages), and how to contribute.

Try it in 30 seconds (no API key)

modscan detect ranks a codebase's extension points using static analysis only — no LLM, no API key, no code execution. It is the fast way to see what MODScan finds before committing to a full documentation run. (Requires modscan ≥ 0.1.1.)

pip install modscan
modscan detect ./path/to/project            # ranked Markdown table
modscan detect ./path/to/project --json     # machine-readable, for tooling/CI
modscan detect ./path/to/project --limit 10 # just the top 10

Point it at an installed package to see it work immediately:

modscan detect "$(python -c 'import os,click;print(os.path.dirname(click.__file__))')" --limit 5

In CI (GitHub Action)

Drop the ranked extension points into every pull request's job summary — safe on untrusted PRs, since detect runs no LLM and executes no target code:

- uses: actions/checkout@v4
- uses: Rinkia/modscan@v0.1.3
  with:
    path: .
    min-score: "0.5"

Breaking-change gate for your plugin API

If your library has plugin/mod authors, guard them from silent breakage: fail a pull request that removes or changes an extension point. It diffs detect on the PR against the base branch — no committed manifest, no LLM, no API key.

# .github/workflows/extension-api.yml
on: pull_request
permissions: { contents: read, pull-requests: write }
jobs:
  gate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with: { fetch-depth: 0 }        # the gate needs the base branch
      - uses: Rinkia/modscan/breaking-change@v0.1.3
        with: { path: your_package }

On a PR it comments the diff and fails the check when an extension point is gone or its category/kind changed. A score/ranking change alone is not breaking. (MODScan uses this on itself — see .github/workflows/extension-api-gate.yml.)

From an AI client (MCP server)

Ask an MCP-capable client (Claude Desktop, Cursor, …) "what are the extension points of this codebase?" without leaving the conversation. The server exposes only the offline detector — no LLM, no code execution — so it is safe on any local checkout.

pip install modscan[mcp]
modscan-mcp        # stdio server; register the `modscan-mcp` command with your client

The one tool, detect_extension_points_tool, takes a path and returns the ranked points. (Requires modscan ≥ 0.1.1.)

Full documentation run (uses an LLM)

pip install modscan[anthropic]     # or [openai], [gemini], [typescript]
export ANTHROPIC_API_KEY=sk-...    # keys come from the environment, never flags

modscan ./path/to/project
# -> writes modding-docs/: index.md, plugin-guide.md, examples/*.py,
#    and extension-points.json

Common flags:

modscan ./proj --provider openai --model gpt-x --base-url http://localhost:11434/v1
modscan ./proj --min-score 0.6 --limit 20 --retries 5
modscan ./proj --language typescript     # scan a TS/JS codebase (static docs)
modscan ./proj --no-validate-examples   # skip importing/executing target code
modscan ./proj --sandbox                 # validate examples in an isolated subprocess
modscan ./proj --cache-dir .modscan-cache  # cache LLM responses for cheap re-runs
modscan ./proj --max-tokens 2048 --max-calls 50   # spend controls: per-call cap + hard run ceiling
modscan ./proj --concurrency 8           # parallel LLM calls (the main speed-up)

Then scaffold a ready-to-edit plugin from any documented extension point (no LLM, reads the JSON manifest):

modscan scaffold "pkg.mod:Symbol" --manifest modding-docs/extension-points.json
# -> writes pkg_mod_Symbol_plugin.py: a concrete subclass with stubbed methods

modscan scaffold --all --out plugins/   # skeletons for every documented point

Diff two manifests to catch breaking changes when the target app updates (exits non-zero on breaking changes — handy as a CI gate):

modscan diff old/extension-points.json new/extension-points.json

To gate pull requests automatically, copy examples/ci/breaking-change.yml into your project: it diffs the committed manifest against the PR's base branch, comments the result on the PR, and fails the check on breaking changes. No API key needed.

Trust note: by default MODScan imports and executes code under the scanned path (and runs generated examples) to validate that plugins really load. Run it only on code you trust, or pass --no-validate-examples.

Contributing

Contributions welcome — MODScan is designed to be easy to extend. Start with CONTRIBUTING.md, then pick up a good first issue.

Each layer is a clean seam you can extend on its own:

• New detection heuristics — add hook/registration name patterns or class role suffixes in detector.py.
• New languages — implement a LanguageParser (see languages/) that emits the shared Codebase model; the graph, detector, and docs come for free.
• New LLM providers — add a thin adapter under providers/.

Tests are framework-free and offline (python tests/test_*.py) — no API key, no network. The golden rule: facts come from the parser, prose from the LLM.

License

Apache License 2.0. Permissive, with an explicit patent grant — the extension-point detection is the core value, so the patent clause is worth the extra verbosity. See also NOTICE.

Planning docs live in .claude/plans/modscan.plan.md.