{
  "name": "autobrowse",
  "version": "1.0.0",
  "description": "Autonomous browser skill generator: runs tasks, reads execution logs, optimizes strategy, and graduates a durable SKILL.md + JS script.",
  "backend": "pi-subprocess",
  "defaults": {
    "agentScope": "both"
  },
  "steps": [
    {
      "id": "init",
      "agent": "planner",
      "output": "strategy.md",
      "task": "Create the initial web automation strategy for this task:\n\n{task}\n\nIdentify target URL, required login/cookies state, likely selectors, and extraction steps. Write the first version of strategy.md. Do not write code or run browser yet."
    },
    {
      "loop": {
        "id": "optimize",
        "maxIterations": 3,
        "gateStep": "evaluate",
        "passStatuses": ["converged"],
        "retryStatuses": ["improving"],
        "stopStatuses": ["blocked"],
        "onExhausted": "stop",
        "body": [
          {
            "id": "generate-script",
            "agent": "worker",
            "output": "script-{loop.iteration}.js",
            "task": "Write or optimize a PandaScript (vanilla JS for Lightpanda) based on strategy:\n\n{outputs.init}\n\nLast iteration evaluation/notes, if any:\n{outputs.evaluate.output}\n\nUse Lightpanda primitives (goto, click, fill, extract). Keep it minimal. Do not use LLM in the output script. Focus on extracting the requested data cleanly."
          },
          {
            "id": "run-trace",
            "agent": "worker",
            "output": "run-{loop.iteration}.log",
            "task": "Execute the script using Lightpanda and capture the full stdout/stderr/network trace.\n\nCommand to run: `lightpanda agent script-{loop.iteration}.js`.\n\nReturn the exact log output and exit code."
          },
          {
            "id": "evaluate",
            "agent": "reviewer",
            "output": "evaluation-{loop.iteration}.json",
            "gate": {
              "type": "json-status",
              "passStatuses": ["converged"],
              "retryStatuses": ["improving"],
              "stopStatuses": ["blocked"]
            },
            "task": "Analyze the run log: {outputs.run-trace.output}.\n\nDid it succeed? Are there redundant clicks, slow waits, or missing elements? Update strategy.md with optimizations.\n\nReturn ONLY valid JSON matching this shape:\n{\n  \"status\": \"converged\" | \"improving\" | \"blocked\",\n  \"unnecessary_steps_removed\": [\"step\"],\n  \"updated_selectors\": [\"selector\"],\n  \"reasoning\": \"short verdict\"\n}"
          }
        ]
      }
    },
    {
      "id": "graduate",
      "agent": "worker",
      "output": "SKILL.md",
      "task": "Autobrowse converged successfully in {loop.iteration} iterations!\n\nRead final strategy: {outputs.evaluate.output}\nAnd final script: {outputs.generate-script.output}\n\nWrite a clean, reusable SKILL.md file. Explain the target site, prerequisites, exact command to run (lightpanda agent <script>), output schema, and expected errors."
    }
  ]
}