oss-contribute

Contribute

The LLM is your research assistant, not your ghostwriter. This skill digs deep into the issue. traces code, reads history, finds patterns, identifies constraints. then hands everything to you. You think. You decide. You code.

Purpose

The fastest way to NOT learn from open source is to let an AI write the fix. This skill exists to make you a better developer: it gives you the context a senior contributor would have, then makes you do the work a senior contributor would do. The thinking is yours. The code is yours. The learning is yours.

Prerequisites

• Repo forked, cloned, and set up (from oss-prep-to-contribute)
• Working branch created
• Existing tests pass
• User has explained their understanding of the codebase and the issue

Process

1. Deep investigation

Use Agent with subagent_type=Explore to thoroughly investigate the issue. This is where the LLM does heavy lifting. reading code so the user doesn't have to grep blindly.

Investigate:

• The exact code path the issue describes (trace entry → processing → output)
• All files that touch this code path (imports, callers, callees)
• Existing tests for this area (what's covered, what's missing)
• Git history for the relevant files (why was it written this way? any related past fixes?)
• Similar patterns elsewhere in the codebase (how do they handle the same problem?)
• Error handling in the code path
• Edge cases mentioned in comments or tests

# Trace the code path
grep -rn "function_name\|class_name\|relevant_symbol" src/ --include="*.ts" --include="*.py" --include="*.go" --include="*.rs"

# Git history for context
git log --oneline -15 -- "path/to/relevant/files"
git log --all --oneline --grep="keyword_from_issue"

# Find who last touched this code and why
git blame "path/to/file" -L {start},{end}

# Find related tests
grep -rn "describe.*relevant\|test.*relevant\|def test_relevant" tests/ test/ __tests__/ spec/

2. Present the research

Deliver findings in a structured format. Every claim must have a file:line reference.