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ultracold-molecule

GitHub Semantic Search

by Ultracold-molecule · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install github-semantic
Description
AI-Native GitHub Assistant powered by Embedder+Qdrant+LLM architecture. Index repos, semantic search across issues/PRs/code, proactive monitoring with Feishu...
README (SKILL.md)

🦞 Super GitHub — AI-Native GitHub Assistant

Powered by the same Embedder + Qdrant + LLM architecture as elite memory systems. Index repos, search semantically, monitor proactively — all with natural language.

Architecture

Query → [LLM: understand intent] → [Embedder: vectorize] → [Qdrant: semantic search] → [gh CLI: act]

Three-layer system (same as production memory pipelines):

Layer Component Role
Embedder Ollama nomic-embed-text Converts text → 768-dim vectors
Vector Store Qdrant (local) Stores & searches vectors by similarity
Action Layer gh CLI Executes GitHub operations

Prerequisites

  • gh CLI authenticated (gh auth status)
  • Ollama running with nomic-embed-text:latest
  • Qdrant running at localhost:6333

Quick Start

# 1. Initialize Qdrant collection
python scripts/github_indexer.py init

# 2. Index a repo
python scripts/github_indexer.py add owner/repo --all

# 3. Search with natural language
python scripts/github_search.py "memory search failing in agent" --limit 10

# 4. Monitor for keywords
python scripts/github_monitor.py watch owner/repo --events issues,ci --keywords bug,broken,urgent

Scripts

Script Purpose
github_indexer.py Index repos (issues, PRs, metadata) into Qdrant
github_search.py Natural language semantic search
github_monitor.py Proactive monitoring with keyword alerts

Detailed Commands

Index (github_indexer.py)

python github_indexer.py init                    # Create Qdrant collection
python github_indexer.py add owner/repo --all     # Index everything
python github_indexer.py add owner/repo --issues # Issues only
python github_indexer.py add owner/repo --prs    # PRs only
python github_indexer.py add owner/repo --repo   # Repo metadata
python github_indexer.py status                   # Show indexed data
python github_indexer.py rm owner/repo           # Remove from index

Search (github_search.py)

python github_search.py "query"                            # Search all
python github_search.py "query" --repo owner/repo         # Filter by repo
python github_search.py "query" --type issue              # Filter by type
python github_search.py "query" --limit 20               # More results
python github_search.py "query" --repo owner/repo --ci    # Show CI runs

Monitor (github_monitor.py)

python github_monitor.py watch owner/repo                  # Start watching
python github_monitor.py watch owner/repo --events issues,ci
python github_monitor.py status                            # Show watches
python github_monitor.py check                            # Run checks
python github_monitor.py unwatch owner/repo               # Stop watching

Memory System Analogy

Component GitHub Skill Memory System
Data Issues, PRs, code Conversations
Embedder nomic-embed-text nomic-embed-text
Vector Store Qdrant Qdrant
Add github_indexer.py mem0 add
Search github_search.py mem0 search

Why Vector Search vs Keyword?

Approach "memory problems" query
Keyword Exact match only
Vector (this) "memory leak", "OOM", "out of memory"

Setup Checklist

  • gh auth login — authenticate GitHub CLI
  • ollama pull nomic-embed-text:latest — download embedder
  • Start Qdrant: qdrant --storage-path ./qdrant-data
  • python github_indexer.py init — create collection
Usage Guidance
This skill performs GitHub indexing, semantic search, and proactive monitoring and will use your local GitHub CLI auth and local Ollama/Qdrant services. Before installing or running it: (1) review and if needed change hard-coded paths (GH_EXE, OLLAMA_MODELS, STATE_FILE) so it matches your OS and does not write to unexpected locations; (2) understand that the monitor sends alerts via 'openclaw message send' to a hard-coded Feishu user id — check/replace that recipient and ensure you consent to sending repo content to Feishu; (3) confirm you want the skill to access your gh-authenticated account (it will read issues/PRs and CI data via the gh CLI, including private repo content if your gh session allows it); (4) run the scripts in an isolated environment or on a test repo first; and (5) ask the publisher to declare required binaries and any credentials explicitly (gh, Ollama running locally, Qdrant, openclaw) and to remove hard-coded IDs/paths for safer, cross-platform usage.
Capability Assessment
ℹ Purpose & Capability
Functionality (indexing, semantic search, monitoring) matches the name/description and SKILL.md. However the package metadata declares no required binaries/env but the code requires the GitHub CLI, a local Ollama embeddings endpoint, Qdrant, and the openclaw CLI. Also the scripts hard-code Windows paths (GH_EXE and D:\ChatAI paths), which is an implementation detail that wasn't declared.
ℹ Instruction Scope
SKILL.md instructions cover index/search/monitor flows and list high-level prerequisites (gh, Ollama, Qdrant). The runtime scripts, however, also: write state to a specific local file (D:\ChatAI\OpenClaw\github_monitor_state.json), call a local embedding HTTP endpoint (http://localhost:11434), and invoke 'openclaw message send' to post Feishu alerts to a hard-coded user id. Those behaviors are consistent with monitoring but are not fully documented in the metadata and grant the skill the ability to transmit indexed content to an external chat channel.
✓ Install Mechanism
Instruction-only with included scripts; there is no install spec that downloads or extracts remote archives. Risk from install mechanism is low. The runtime requires external services (Ollama, Qdrant) and system CLIs which are not installed by the skill.
⚠ Credentials
No required env vars are declared, but the code will use the user's GitHub credentials via the gh CLI, the user's Ollama/Qdrant instances, and the openclaw CLI to send Feishu messages. The monitor will transmit alert content (issue/PR text, potentially private data) to a hard-coded Feishu user via openclaw. The lack of declared credentials/permissions and the hard-coded external recipient make privilege/credential access proportionality unclear.
✓ Persistence & Privilege
always:false and user-invocable are appropriate. The skill does create/modify a local state file under a hard-coded path, but it does not attempt to change other skills or global agent configuration.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install github-semantic
  3. After installation, invoke the skill by name or use /github-semantic
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release: AI-native GitHub with Embedder+Qdrant+LLM architecture. Index repos, semantic search, proactive monitoring.
Metadata
Slug github-semantic
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is GitHub Semantic Search?

AI-Native GitHub Assistant powered by Embedder+Qdrant+LLM architecture. Index repos, semantic search across issues/PRs/code, proactive monitoring with Feishu... It is an AI Agent Skill for Claude Code / OpenClaw, with 74 downloads so far.

How do I install GitHub Semantic Search?

Run "/install github-semantic" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is GitHub Semantic Search free?

Yes, GitHub Semantic Search is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does GitHub Semantic Search support?

GitHub Semantic Search is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created GitHub Semantic Search?

It is built and maintained by Ultracold-molecule (@ultracold-molecule); the current version is v1.0.0.

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