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
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.
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.
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.
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.
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:
1---2name: swe-agent-issue-loop3description: Use when turning GitHub issues or bug reports into reproducible coding-agent repair loops with sandboxed execution, focused tests, patch generation, and regression verification.4---56# SWE Agent Issue Loop78Use 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.910## When To Use1112Activate when the user asks to:1314- Fix a GitHub issue, bug report, failing test, or CI failure15- Build a coding-agent benchmark task16- Convert an issue into a reproducible repair workflow17- Compare agent scaffolds on software engineering tasks18- Add guardrails around autonomous coding agents1920## Repair Loop21221. **Frame the issue**23 - Extract expected behavior, observed behavior, affected files, environment, and acceptance criteria.24 - Identify what evidence is missing before editing.25 - Record any user constraints such as no refactor, no dependency changes, or no UI changes.26272. **Build a reproduction**28 - Run the smallest relevant failing command.29 - Prefer a focused test over full-suite execution.30 - If no test exists, write or sketch one before implementation when feasible.31 - Store command, result, and failure reason.32333. **Patch narrowly**34 - Read the local code around the failing behavior.35 - Change the smallest coherent surface.36 - Avoid solving benchmark tasks by hardcoding issue text, test names, or environment-specific values.37384. **Verify**39 - Re-run the reproducer.40 - Run adjacent tests or a targeted suite.41 - Check for formatting, lint, type, or build regressions when relevant.42 - Summarize command outcomes in the final response.43445. **Harden the harness**45 - Use sandboxed execution for autonomous agents.46 - Cap command timeouts and output length.47 - Separate model instructions from tool output.48 - Preserve full trajectories for later review.4950## Benchmark-Aware Guidance5152SWE-bench Verified emphasizes clear issue descriptions, correct tests, and solvable tasks. Mirror that standard for internal benchmarks:5354- Do not include ambiguous reports without expected behavior.55- Do not count a task as solved unless tests or deterministic checks pass.56- Track whether the agent used only allowed context.57- Capture the diff and trajectory, not only pass/fail.5859## Helper Script6061Use [issue_repro_matrix.py](./scripts/issue_repro_matrix.py) to convert issue text into a reproducibility matrix:6263```bash64python scripts/issue_repro_matrix.py issue.md65```6667## References6869Read [agent-loop-checklist.md](./references/agent-loop-checklist.md) before designing a coding-agent harness or benchmark task.7071External grounding:7273- [SWE-bench Verified](https://www.swebench.com/verified.html)74- [mini-SWE-agent SWE-bench documentation](https://mini-swe-agent.com/latest/usage/swebench/)75- [SWE-ReX GitHub repository](https://github.com/SWE-agent/swe-rex)76- [OpenHands Software Agent SDK HuggingFace paper page](https://huggingface.co/papers/2511.03690)77- [OpenHands GitHub repository](https://github.com/All-Hands-AI/OpenHands)78