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_ids when the scene changes.