GitHub Semantic Search

该 Skill 通过 Python 脚本把 GitHub 仓库的 issues、PR、元数据索引到本地 Qdrant，使用 Ollama 的 nomic-embed-text 生成向量，支持自然语言语义搜索，并可监控仓库关键词事件并发送告警，底层通过 gh CLI 执行 GitHub 操作。

ultracold-molecule

热度 34.79

最近更新 2026-05-18 11:41:52

分类：搜索依赖：代码执行 / 第三方 API / 内部调用大模型应用场景：研究 / 代码审查 / 调试排错 / 自动化脚本 ...

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add github-semantic

Metadata

name: super-github
description: AI-Native GitHub Assistant powered by Embedder+Qdrant+LLM architecture. Index repos, semantic search across issues/PRs/code, proactive monitoring with Feishu alerts. Use when: (1) natural language GitHub queries, (2) tracking issues/PRs/CI across repos, (3) monitoring repos for bugs/keywords, (4) finding related issues without keyword matching.

🦞 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