# Clickthrough

Let an Agent interact with your Computer.

`Clickthrough` is a proof-of-concept bridge between a vision-aware agent and a headless controller. The project is split into two halves:

1. A Python server that accepts a static grid overlay (think of a screenshot broken into cells) and exposes lightweight endpoints to ask questions, plan actions, or even run pointer/keyboard events.
2. A **skill** that bundles the HTTP calls/intent construction so we can hardwire the same flow inside OpenClaw later.

## Server surface (FastAPI)

- `POST /grid/init`: Accepts a base64 screenshot plus the requested rows/columns, returns a `grid_id`, cell bounds, and helpful metadata. The grid is stored in-memory so the agent can reference cells by ID in later actions.
- `POST /grid/action`: Takes a plan (`grid_id`, optional target cell, and an action like `click`/`drag`/`type`) and returns a structured `ActionResult` with computed coordinates for tooling to consume.
- `GET /grid/{grid_id}/summary`: Returns both a heuristic description (`GridPlanner`) and a rich descriptor so the skill can summarize what it sees.
- `GET /grid/{grid_id}/history`: Streams back the action history for that grid so an agent or operator can audit what was done.
- `GET /health`: A minimal health check for deployments.

Vision metadata is kept on a per-grid basis, including history, layout dimensions, and any appended memo. Each `VisionGrid` also exposes a short textual summary so the skill layer can turn sensory data into sentences directly.

## Skill layer (OpenClaw integration)

The `skill/` package wraps the server calls and exposes helpers:

- `ClickthroughSkill.describe_grid()` builds a grid session and returns the descriptor.
- `ClickthroughSkill.plan_action()` drives the `/grid/action` endpoint.
- `ClickthroughSkill.grid_summary()` and `.grid_history()` surface the new metadata endpoints.
- `ClickthroughAgentRunner` simulates a tiny agent loop that chooses a cell (optionally by label), submits an action, and fetches the summary/history.

Future work can swap the stub runner for a full OpenClaw skill that keeps reasoning inside the agent and uses these primitives to steer the mouse/keyboard.

## Testing

1. `python3 -m pip install -r requirements.txt`
2. `python3 -m pip install -r requirements-dev.txt`
3. `python3 -m pytest`

The `tests/` suite covers grid construction, the FastAPI surface, and the skill/runner helpers.

## Continuous Integration

`.github/workflows/ci.yml` runs on pushes and PRs:

- Checks out the repo and sets up Python 3.11.
- Installs dependencies (`requirements.txt` + `requirements-dev.txt`).
- Runs `ruff check` over the Python packages.
- Executes `pytest` to keep coverage high.

## Next steps

- Add OCR or UI heuristics so grid cells have meaningful labels before the agent reasons about them.
- Persist grids and histories in a lightweight store so long-running sessions survive restarts.
- Expose a websocket/watch endpoint that streams updated screenshots and invalidates cached `grid_id`s when the scene changes.