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swe-agent-issue-loop

Use when turning GitHub issues or bug reports into reproducible coding-agent repair loops with sandboxed execution, focused tests, patch generation, and regression verification.

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포함된 파일(3)

  • SKILL.md3.3 KB
  • references/agent-loop-checklist.md1.3 KB
  • scripts/issue_repro_matrix.py2.0 KB

SKILL.md(원문)

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SWE Agent Issue Loop

Use this skill to handle issue-driven software engineering tasks the way modern coding-agent benchmarks and production agent harnesses do: isolate the repository, reproduce the failure, edit narrowly, and verify with executable tests.

When To Use

Activate when the user asks to:

  • Fix a GitHub issue, bug report, failing test, or CI failure
  • Build a coding-agent benchmark task
  • Convert an issue into a reproducible repair workflow
  • Compare agent scaffolds on software engineering tasks
  • Add guardrails around autonomous coding agents

Repair Loop

  1. Frame the issue

    • Extract expected behavior, observed behavior, affected files, environment, and acceptance criteria.
    • Identify what evidence is missing before editing.
    • Record any user constraints such as no refactor, no dependency changes, or no UI changes.

  2. Build a reproduction

    • Run the smallest relevant failing command.
    • Prefer a focused test over full-suite execution.
    • If no test exists, write or sketch one before implementation when feasible.
    • Store command, result, and failure reason.

  3. Patch narrowly

    • Read the local code around the failing behavior.
    • Change the smallest coherent surface.
    • Avoid solving benchmark tasks by hardcoding issue text, test names, or environment-specific values.

  4. Verify

    • Re-run the reproducer.
    • Run adjacent tests or a targeted suite.
    • Check for formatting, lint, type, or build regressions when relevant.
    • Summarize command outcomes in the final response.

  5. Harden the harness

    • Use sandboxed execution for autonomous agents.
    • Cap command timeouts and output length.
    • Separate model instructions from tool output.
    • Preserve full trajectories for later review.

Benchmark-Aware Guidance

SWE-bench Verified emphasizes clear issue descriptions, correct tests, and solvable tasks. Mirror that standard for internal benchmarks:

  • Do not include ambiguous reports without expected behavior.
  • Do not count a task as solved unless tests or deterministic checks pass.
  • Track whether the agent used only allowed context.
  • Capture the diff and trajectory, not only pass/fail.

Helper Script

Use issue_repro_matrix.py to convert issue text into a reproducibility matrix:

python scripts/issue_repro_matrix.py issue.md

References

Read agent-loop-checklist.md before designing a coding-agent harness or benchmark task.

External grounding:

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