CharlesYang030
244e346b83
- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
24 lines
1.2 KiB
Markdown
24 lines
1.2 KiB
Markdown
# SWE-bench Bug Fixing Skill
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## Overview
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This skill guides agents in resolving real-world GitHub issues by producing correct patches.
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**Goal**: Given a repository and an issue description, produce a minimal, correct `git diff` patch that resolves the issue without modifying test files.
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## Workflow
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1. Understand the issue. Read the problem statement carefully and restate the expected behavior before editing code.
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2. Locate relevant code. Use targeted search to identify the files, functions, and tests that encode the buggy behavior.
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3. Reproduce the issue. Build a small, local reproduction before changing source files when feasible.
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4. Implement the fix. Make the smallest source change that addresses the root cause.
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5. Verify the fix. Re-run the reproduction and any focused checks needed to confirm the change.
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6. Submit the patch. Generate a clean unified diff of only the source files you modified.
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## Key Rules
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- Keep changes minimal and directly tied to the bug.
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- Do not modify tests, fixtures, or unrelated configuration unless the issue explicitly requires it.
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- Prefer understanding the code path before patching.
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- Verify behavior after editing instead of relying on intuition.
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- The final submission must be a valid unified diff.
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