{
  "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": "2609.29454",
  "title": "Demystifying Agent Skills for Smart Contract Auditing: Design, Effectiveness, Behavioral Impact",
  "summary": "The method replaces ad-hoc prompting of general coding agents with lightweight, reusable skill artifacts that package domain knowledge, audit workflows, and tool-use instructions for smart contract security auditing. Cost is low in infrastructure terms since skills run on existing third-party API agents without training, but requires curating or authoring skills and selecting a capable frontier model, since gains come primarily from the model rather than the agent harness. The principal failure mode is unreliable skill triggering, plus imbalanced vulnerability coverage across skill corpora and behavioral effects that vary across agent-model configurations.",
  "rationale": "Skill artifacts are cheap to adopt on API-based agents and the paper shows meaningful detection gains, but those gains are model-specific and hinge on a triggering bottleneck, so the reader must validate them on their own auditing workload before production reliance.",
  "technique": "Domain skill packs for LLM auditing agents",
  "family": "uso_de_ferramenta",
  "recommendation": "testar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29454",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
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      "source_url": "https://arxiv.org/abs/2609.29454",
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      "claim_id": "claim-02",
      "statement": "Codex/GPT-5.5 with skills improves vulnerability detection score by 22.8% and captured award by 43.2% on EVMBench; gains are model-dependent and require skill triggering.",
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    }
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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    {
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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    }
  ],
  "risks": [],
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  "open_questions": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}