{ "schema_version": 1, "builder_plan": { "scenario": "Your coding assistant makes changes that still need substantial repair before merging.", "change": "Try the technique on held-out issues from a repository with reliable tests.", "baseline": "The same coding agent, repository snapshot, tools, and token 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 patches, regression failures, human repair time, and total cost per accepted change.", "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": "2609.30725", "title": "Analyzing and Mitigating Cost-Inefficient Behaviors in Coding Agents", "summary": "The method replaces unguided agent exploration — which exhibits subsumed retrieval, similar script generation, and test re-execution in 79–98% of tasks, consuming up to 22.75% of task cost — with developer-authored, high-level, trace-agnostic skill prompts injected into the agent. It costs manual effort to write and maintain the skills and adds a small prompt-token overhead per session, with no infrastructure or fine-tuning requirement. 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