{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A multi-step product task fails because the agent loses track of dependencies.",
    "change": "Try the proposed planning step on tasks with checkable intermediate outcomes.",
    "baseline": "The same agent solving the same tasks without the new planning step.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Task completion, unnecessary steps, planning tokens, response time, and total cost.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.12984",
  "title": "Work2Skill: Capturing Human GUI Experience for Reusable Agent Skill Induction",
  "summary": "The method replaces hand-written prompts, scripts, or manual in-context instructions for GUI agents with skills automatically induced from synchronized human screen, mouse, keyboard, and accessibility-tree recordings. It costs infrastructure for capturing and aligning demonstrations (WorkRecorder), an agentic induction pipeline that inspects recorded evidence, and the added context length of injecting skills at execution time; no model training is required, so it fits API-based operation. It can fail when workflows change with software updates, when demonstrations omit edge cases or encode task-specific details as rules, and the gains are validated on only two benchmarks with 350 demos, so transfer to a given production application is unproven.",
  "rationale": "Skill induction from demonstrations requires no training and works with API models, but the reader must validate on their own target software whether the modest point gains justify building a demo-capture and skill-maintenance pipeline.",
  "technique": "Reusable skill induction from human GUI demonstrations",
  "family": "planejamento_e_decomposicao",
  "recommendation": "testar",
  "published": "2026-10-09",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.12984",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 14,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "claim_id": "claim-02",
      "statement": "With four execution models, induced skills improve mean scores over the No Skill baseline by 4.67–10.54 points on MyPCBench Bounded Action and 3.11–7.50 points on a 286-task OSWorld-Verified subset.",
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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      "dimension": "data_and_training",
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
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}