open-source-contributor（开源贡献者）

open-source-contributor

Autonomous GitHub contribution agent using subagent orchestration for real code fixes.

Description

Scouts open-source projects for contribution opportunities, analyzes issues, implements fixes using AI subagents, and submits PRs under your identity. Supports three difficulty levels from beginner-friendly to advanced.

Quality Standards

• Every contribution must be meaningful — no typo fixes, no placeholder PRs
• Must pass CI/tests where available
• Must include proper description and tests if applicable
• Must reference the issue it fixes
• Must disclose AI assistance

⚠️ GitHub Token

The GitHub token is at ~/.openclaw/.github_token. Read it with the token resolver at ~/.openclaw/lib/token_resolver.py. Never ask the user for the token — it's already on disk. Never log or print token values.

Excluded Repos

Do not contribute to repos we've already contributed to:

• MemPalace/mempalace
• simpler-grants-gov
• code-charity/youtube
• osslab-pku/gfi-bot
• puppeteer/puppeteer (already have PR #14916)
• TwiN/gatus (already have PR #1644)
• dundee/gdu (already have PR #558)
• FilipePS/Traduzir-paginas-web (already have PR #1007)

Difficulty Levels

Level 1: Easy — Warm-up Contributions

Target: Small, well-scoped fixes in familiar territory. Good for building reputation.

Typical fixes:

• UI text / label corrections
• Missing error handling (try/except, null checks)
• Simple configuration fixes
• Small accessibility improvements
• Missing validation on user inputs

Research query:

label:"good first issue" OR label:"help wanted" OR label:"bug"
language:python|javascript|typescript|go|rust
state:open sort:updated

Timeout: runTimeoutSeconds: 600 for Worker (simpler fixes, faster)

Level 2: Intermediate — Real Feature Work

Target: Multi-file changes requiring understanding of codebase architecture. This is the default level.

Typical fixes:

• Dependency version bumps with regression tests
• Adding missing API parameters or options
• Fixing edge cases in data processing
• Implementing missing methods or handlers
• Performance improvements (caching, lazy loading)

Research query:

label:"bug" OR label:"feature" OR label:"performance" OR label:"enhancement"
-language:"good first issue" -language:"help wanted"
language:python|javascript|typescript|go|rust stars:>1000
state:open sort:updated

Timeout: runTimeoutSeconds: 900 for Worker (needs more time for multi-file changes)

Level 3: Advanced — Architecture & Deep Fixes

Target: Complex changes requiring deep codebase understanding, cross-module impact, or algorithmic thinking.

Typical fixes:

• Race condition / concurrency bug fixes
• Memory leak detection and remediation
• API redesign or new endpoint implementation
• Database query optimization
• Plugin/extension system development
• Cross-browser/cross-platform compatibility fixes

Research query:

label:"bug" OR label:"feature" OR label:"performance" OR label:"enhancement" OR label:"design"
language:python|javascript|typescript|go|rust stars:>2000
state:open sort:updated

Timeout: runTimeoutSeconds: 1200 for Worker (complex changes need more time)

Optional: Spawn a Council of L3ms to evaluate approach before implementing. Use when the fix involves:

• Security-sensitive code paths
• Breaking API changes
• Database schema changes
• Performance-critical hot paths

Pipeline (Subagent-Based)

This skill uses the subagent-orchestration pattern with research → implement → report phases:

Phase 1: Research (spawn Researcher agent)

Spawn a Researcher agent with toolsAllow: ["ollama_web_fetch", "ollama_web_search"] to:

1. Search GitHub API using the query for the configured difficulty level
2. Filter results by:

- Open for the right age range (varies by level) - Comment count within threshold - No existing PRs already addressing the issue - Not in the excluded repos list

1. For each candidate, fetch the issue body, repo structure, and recent PRs
2. Return: top 3 issues ranked by impact-to-effort ratio, with full context

Phase 2: Evaluate & Implement (spawn Worker agent)

For the best issue from Phase 1, spawn a Worker agent to:

1. Fork the repo to wahajahmed010 using GitHub API (token at ~/.openclaw/.github_token)
2. Clone the fork, create branch: fix/<issue-number>-<short-description>
3. Read CONTRIBUTING.md or similar docs first — follow their style
4. Implement the fix — write real code, not placeholder comments
5. Run existing tests. If they pass, proceed. If no tests exist, manually verify.
6. If the fix touches >50 lines or changes core logic, add or update a test
7. Push and create a PR via GitHub API with:

- Clear description referencing the issue - Explanation of the approach - Difficulty level tag - Note that this was AI-assisted

Phase 2.5: Council Review (Level 3 only — optional)

For Level 3 fixes that are security-sensitive or architecturally complex, spawn a Council of LLMs before implementing:

1. Spawn 3 parallel subagents: Strategos (kimi-k2.6), Analyticos (deepseek-v4-pro), Creativos (gemma4:31b)
2. Pass the issue context and proposed approach to each
3. Synthesize their verdicts
4. If consensus is "don't implement" or high risk → skip this issue, move to next candidate

Phase 3: Report

Return a final summary with:

• Issue title + link
• PR link
• Lines changed
• Difficulty level (1/2/3)
• Brief approach description

If no suitable issues found, report that. Do NOT force low-quality contributions.

Configuration

Set the difficulty level in ~/.openclaw/workspace/contrib-scout/config.json:

{
  "difficulty_level": 2,
  "github_token_path": "~/.openclaw/.github_token",
  "max_contributions_per_night": 1,
  "languages": ["python", "javascript", "typescript", "go", "rust"],
  "excluded_repos": ["MemPalace/mempalace", "puppeteer/puppeteer", "TwiN/gatus", "dundee/gdu", "FilipePS/Traduzir-paginas-web"]
}

Default: Level 2 (Intermediate). Change to 1 for warm-up, 3 for deep work.

Critical Rules

• Do NOT use sessions_yield for intermediate status — only return the final report
• Do NOT send partial progress messages — the first message you return is what gets delivered
• Use GitHub API (urllib + token) for fork/PR operations, git CLI for clone/branch/push
• Write .py files for any scripts — never use python3 -c inline
• Pre-fetch web content yourself for Worker agents — they can't browse
• Keep task descriptions under 2000 words — longer = context overflow
• Use lightContext: true on all subagent spawns
• Scale timeouts with difficulty: L1=600s, L2=900s, L3=1200s
• Never force a contribution — if no good fit, report "no suitable issues found"

Cron Configuration

Runs daily at midnight (Europe/Berlin). Uses the subagent-orchestration pattern.

Storage

~/.openclaw/workspace/contrib-scout/
├── repos/              # Cloned repositories (cleaned up after each run)
├── logs/               # Activity + audit trail (JSONL)
├── config.json         # Difficulty level + settings
└── nightly-report.json # Daily summary

Companion Skills

• subagent-orchestration — Required. Provides spawn patterns, timeout config, sandbox constraints.
• council-of-llms — Optional. For Level 3 complex decisions before implementing.