{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An assistant chooses the wrong API or sends arguments your product cannot use.",
    "change": "Apply the proposed tool-selection or argument-generation change in a sandbox.",
    "baseline": "Your current agent with the same tool schemas and task budget.",
    "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": "Correct completed actions, invalid calls, retries, latency, and cost per completed task.",
    "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": "2609.38024",
  "title": "Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation",
  "summary": "RASO replaces rollout-only iterative skill refinement (e.g., hand-written or purely trial-and-error-optimized agent skills) with retrieval and cross-harness adaptation of existing public skills, initializing skills without agent rollouts and updating them using execution-feedback-guided retrieval. Its cost is maintaining and querying an external skill corpus plus the adaptation step, which adds pipeline complexity and modest retrieval/LLM call overhead; it runs on API models, so no extra GPU infrastructure is required. It can fail when the retrieved skills mismatch the target domain or harness, when the public corpus lacks relevant skills for the task, or when adaptation propagates errors from low-quality retrieved artifacts, so gains must be validated on the reader's specific agent workloads.",
  "rationale": "The method is compatible with API-based models and small teams since it avoids training and reduces rollout cost, but the abstract offers no quantified improvement and the benefit depends on corpus relevance to the reader's tasks, warranting small-scale validation before adoption.",
  "technique": "Retrieval-Augmented Skill Optimization (RASO)",
  "family": "uso_de_ferramenta",
  "recommendation": "testar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.38024",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 121,
  "citations": null,
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  "validation_tier": "unknown",
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