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oss-hunter

Automatically hunt for high-impact OSS contribution opportunities in trending repositories.

.agents/skills/oss-hunter Python
PY
TY
BA
4+ layers Tracked stack
Capabilities
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Capabilities
Actionable behaviors documented in the skill body.
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Phases
Operational steps available for guided execution.
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References
Support files available for deeper usage and onboarding.
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Scripts
Runnable or reusable automation artifacts discovered locally.

Architectural Overview

Skill Reading

"This module is grounded in ai engineering patterns and exposes 1 core capabilities across 1 execution phases."

OSS Hunter 🎯

A precision skill for agents to find, analyze, and strategize for high-impact Open Source contributions. This skill helps you become a top-tier contributor by identifying the most "mergeable" and influential issues in trending repositories.

When to Use

  • Use when the user asks to find open source issues to work on.
  • Use when searching for "help wanted" or "good first issue" tasks in specific domains like AI or Web3.
  • Use to generate a "Contribution Dossier" with ready-to-execute strategies for trending projects.

Quick Start

Ask your agent:

  • "Find me some help-wanted issues in trending AI repositories."
  • "Hunt for bug fixes in langchain-ai/langchain that are suitable for a quick PR."
  • "Generate a contribution dossier for the most recent trending projects on GitHub."

Workflow

When hunting for contributions, the agent follows this multi-stage protocol:

Phase 1: Repository Discovery

Use web_search or gh api to find trending repositories. Focus on:

  • Stars > 1000
  • Recent activity (pushed within 24 hours)
  • Relevant topics (AI, Agentic, Web3, Tooling)

Phase 2: Issue Extraction

Search for specific labels:

  • help-wanted
  • good-first-issue
  • bug
  • v1 / roadmap
gh issue list --repo owner/repo --label "help wanted" --limit 10

Phase 3: Feasibility Analysis

Analyze the issue:

  1. Reproducibility: Is there a code snippet to reproduce the bug?
  2. Impact: How many users does this affect?
  3. Mergeability: Check recent PR history. Does the maintainer merge community PRs quickly?
  4. Complexity: Can this be solved by an agent with the current tools?

Phase 4: The Dossier

Generate a structured report for the human:

  • Project Name & Stars
  • Issue Link & Description
  • Root Cause Analysis (based on code inspection)
  • Proposed Fix Strategy
  • Confidence Score (1-10)

Limitations

  • Accuracy depends on the availability of gh CLI or web_search tools.
  • Analysis is limited by context window when reading very large repositories.
  • Cannot guarantee PR acceptance (maintainer discretion).

Contributing to the Matrix

Build a better hunter by adding new heuristics to Phase 3. Submit your improvements to the ClawForge.

Powered by OpenClaw & ClawForge.

Primary Stack

Python

Tooling Surface

Guide only

Workspace Path

.agents/skills/oss-hunter

Operational Ecosystem

The complete hardware and software toolchain required.

This skill is mostly documentation-driven and does not expose extra scripts, references, examples, or templates.

Module Topology

Skill File
Parsed metadata
Skills UI
Launch context
Chat Session
Antigravity Core

Antigravity Core

Principal Engineering Agent

A high-performance agentic architecture developed by Deepmind for autonomous coding tasks.
120 Installs
4.2 Reliability
2 Workspace Files
4.2
Workspace Reliability Avg
5
68%
4
22%
3
10%
2
0%
1
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No explicit validation signals were parsed for this skill yet, but the module remains available for inspection and chat launch.

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