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Agent Skill · 研究检索

github-workflows-ollama(GitHub workflows Ollama 搜索)

用于围绕 GitHub 仓库、Issue、Pull Request、分支、提交和代码协作流程提供辅助能力。它适合让 Agent 查询项目状态、整理变更、辅助创建或检查协作事项,并把仓库中的信息转成可执行的下一步。使用时需要区分只读查询和写入操作;涉及创建 PR、修改 Issue、推送分支或访问私有仓库时,应确认 token 权限、目标仓库范围和用户授权。

github-workflows-ollama 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 59,579。

总安装 384周安装 16GitHub Stars 公开资料未说明下载 128核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:github-workflows-ollama(GitHub workflows Ollama 搜索)
来源仓库:https://github.com/constructive-io/constructive-skills
仓库路径:skills/github-workflows-ollama
安装命令:
npx skills add https://github.com/constructive-io/constructive-skills --skill github-workflows-ollama
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/constructive-io/constructive-skills --skill github-workflows-ollama

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

结合 Ollama 本地模型增强 GitHub 工作流智能决策能力。在代码审查、异常预测与资源调度等环节引入 AI 辅助判断。需部署本地推理服务,保障敏感数据不出内网环境。模型性能依赖硬件配置,低配设备可能出现响应延迟现象。

github-workflows-ollama 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/github-workflows-ollama

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/constructive-io/constructive-skills --skill gi…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

github-workflows-ollama 怎么安装?

在终端执行 npx skills add https://github.com/constructive-io/constructive-skills --skill github-workf…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

github-workflows-ollama 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

github-workflows-ollama 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

github-workflows-ollama 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

github-workflows-ollama 用的人多吗?

公开数据是累计安装 384 次,近一周安装 16 次,下载 128 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

github-workflows-ollama 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
github-workflows-ollama

GitHub workflows Ollama 搜索 · 本页

384—接触敏感数据未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

GitHub Workflows for Ollama and pgvector

Configure GitHub Actions workflows for testing RAG pipelines, vector embeddings, and Ollama-based AI applications.

When to Apply

Use this skill when:

  • Setting up CI/CD for RAG applications
  • Testing pgvector and embedding functionality in CI
  • Configuring Ollama service containers
  • Running integration tests that need LLM inference
  • Building pipelines for AI-powered applications

Complete Workflow Template

name: CI tests
on:
  pull_request:
    branches:
      - main
  push:
    branches:
      - main
  workflow_dispatch:

concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}-tests
  cancel-in-progress: true

env:
  PGPM_VERSION: '2.7.9'

jobs:
  test:
    runs-on: ubuntu-latest
    continue-on-error: true
    strategy:
      fail-fast: false
      matrix:
        package:
          - my-rag-package

    env:
      PGHOST: localhost
      PGPORT: 5432
      PGUSER: postgres
      PGPASSWORD: password
      OLLAMA_HOST: http://localhost:11434

    services:
      pg_db:
        image: pyramation/postgres:17
        env:
          POSTGRES_USER: postgres
          POSTGRES_PASSWORD: password
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
        ports:
          - 5432:5432

      ollama:
        image: ollama/ollama:latest
        ports:
          - 11434:11434

    steps:
      - name: Configure Git (for tests)
        run: |
          git config --global user.name "CI Test User"
          git config --global user.email "ci@example.com"

      - name: Checkout
        uses: actions/checkout@v4

      - name: Setup pnpm
        uses: pnpm/action-setup@v2
        with:
          version: 10

      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
          node-version: '20'
          cache: 'pnpm'

      - name: Install dependencies
        run: pnpm install

      - name: Cache pgpm CLI
        uses: actions/cache@v4
        with:
          path: ~/.npm
          key: pgpm-${{ runner.os }}-${{ env.PGPM_VERSION }}

      - name: Install pgpm CLI globally
        run: npm install -g pgpm@${{ env.PGPM_VERSION }}

      - name: Build
        run: pnpm -r build

      - name: Seed pg and app_user
        run: |
          pgpm admin-users bootstrap --yes
          pgpm admin-users add --test --yes

      - name: Wait for Ollama and pull models
        run: |
          echo "Waiting for Ollama to be ready..."
          for i in $(seq 1 30); do
            if curl -s http://localhost:11434/api/tags > /dev/null 2>&1; then
              echo "Ollama is ready!"
              break
            fi
            echo "Waiting for Ollama... ($i/30)"
            sleep 2
          done
          echo "Pulling nomic-embed-text model (for embeddings)..."
          curl -s -X POST http://localhost:11434/api/pull -d '{"name": "nomic-embed-text"}'
          echo ""
          echo "Pulling mistral model (for RAG response generation)..."
          curl -s -X POST http://localhost:11434/api/pull -d '{"name": "mistral"}'

      - name: Test ${{ matrix.package }}
        run: cd ./packages/${{ matrix.package }} && pnpm test

Service Containers

PostgreSQL with pgvector

Use the Constructive PostgreSQL image with pgvector and other extensions:

services:
  pg_db:
    image: pyramation/postgres:17
    env:
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: password
    options: >-
      --health-cmd pg_isready
      --health-interval 10s
      --health-timeout 5s
      --health-retries 5
    ports:
      - 5432:5432

For projects requiring additional extensions:

services:
  pg_db:
    image: ghcr.io/constructive-io/docker/postgres-plus:17
    env:
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: password
    options: >-
      --health-cmd pg_isready
      --health-interval 10s
      --health-timeout 5s
      --health-retries 5
    ports:
      - 5432:5432

Available images:

  • pyramation/postgres:17 - PostgreSQL 17 with pgvector (recommended)
  • ghcr.io/constructive-io/docker/postgres-plus:17 - PostgreSQL 17 with pgvector and additional extensions

Ollama Service

services:
  ollama:
    image: ollama/ollama:latest
    ports:
      - 11434:11434

Note: Ollama doesn't have a built-in health check, so we wait for it in a step.

Environment Variables

env:
  # PostgreSQL connection
  PGHOST: localhost
  PGPORT: 5432
  PGUSER: postgres
  PGPASSWORD: password

  # Ollama connection
  OLLAMA_HOST: http://localhost:11434

Waiting for Ollama

Ollama takes time to start. Use this pattern to wait:

- name: Wait for Ollama and pull models
  run: |
    echo "Waiting for Ollama to be ready..."
    for i in $(seq 1 30); do
      if curl -s http://localhost:11434/api/tags > /dev/null 2>&1; then
        echo "Ollama is ready!"
        break
      fi
      echo "Waiting for Ollama... ($i/30)"
      sleep 2
    done

Pulling Models

Models must be pulled before tests run:

- name: Pull embedding model
  run: |
    curl -s -X POST http://localhost:11434/api/pull \
      -d '{"name": "nomic-embed-text"}'

- name: Pull generation model
  run: |
    curl -s -X POST http://localhost:11434/api/pull \
      -d '{"name": "mistral"}'

Model Pull Times

ModelSizeApproximate Pull Time
nomic-embed-text~275MB30-60s
mistral~4GB2-5 min
llama2~4GB2-5 min
all-minilm~45MB10-20s

Consider using smaller models in CI for faster runs.

Test Timeout Configuration

LLM operations can be slow. Configure Jest timeout:

// In test file
jest.setTimeout(300000); // 5 minutes

// Or in jest.config.js
module.exports = {
  testTimeout: 300000,
};

Caching Strategies

Cache pnpm dependencies

- name: Setup pnpm
  uses: pnpm/action-setup@v2
  with:
    version: 10

- name: Setup Node.js
  uses: actions/setup-node@v4
  with:
    node-version: '20'
    cache: 'pnpm'

Cache pgpm CLI

env:
  PGPM_VERSION: '2.7.9'

steps:
  - name: Cache pgpm CLI
    uses: actions/cache@v4
    with:
      path: ~/.npm
      key: pgpm-${{ runner.os }}-${{ env.PGPM_VERSION }}

  - name: Install pgpm CLI globally
    run: npm install -g pgpm@${{ env.PGPM_VERSION }}

Matrix Testing

Test multiple packages in parallel:

strategy:
  fail-fast: false
  matrix:
    package:
      - packages/embeddings
      - packages/rag-service
      - packages/vector-search

steps:
  - name: Test ${{ matrix.package }}
    run: cd ./${{ matrix.package }} && pnpm test

Minimal Workflow (Embeddings Only)

For projects that only need embeddings (no LLM generation):

name: Embedding Tests
on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest

    env:
      PGHOST: localhost
      PGPORT: 5432
      PGUSER: postgres
      PGPASSWORD: password
      OLLAMA_HOST: http://localhost:11434

    services:
      postgres:
        image: pyramation/postgres:17
        env:
          POSTGRES_USER: postgres
          POSTGRES_PASSWORD: password
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
        ports:
          - 5432:5432

    steps:
      - uses: actions/checkout@v4

      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
          node-version: '20'

      - name: Install Ollama
        run: curl -fsSL https://ollama.com/install.sh | sh

      - name: Start Ollama and pull model
        run: |
          ollama serve &
          sleep 5
          ollama pull nomic-embed-text

      - name: Install dependencies
        run: npm install

      - name: Run tests
        run: npm test

Debugging Failed Tests

View Ollama logs

- name: Debug Ollama
  if: failure()
  run: |
    echo "Checking Ollama status..."
    curl -s http://localhost:11434/api/tags || echo "Ollama not responding"

Check PostgreSQL

- name: Debug PostgreSQL
  if: failure()
  run: |
    psql -h localhost -U postgres -c "SELECT version();"
    psql -h localhost -U postgres -c "SELECT * FROM pg_extension WHERE extname = 'vector';"

Best Practices

  1. Use fail-fast: false - Let all tests complete even if some fail
  2. Set generous timeouts - LLM operations are slow
  3. Pull models early - Do it before running tests
  4. Use smaller models in CI - all-minilm instead of nomic-embed-text for speed
  5. Cache dependencies - pnpm and pgpm caching speeds up runs
  6. Health checks - Always use health checks for PostgreSQL
  7. Wait for Ollama - It doesn't have built-in health checks

Troubleshooting

IssueSolution
Ollama not respondingIncrease wait time, check service logs
Model pull timeoutUse smaller model or increase timeout
pgvector not foundEnsure using pgvector-enabled image
Tests timeoutIncrease Jest timeout, use streaming
Out of memoryUse smaller models or reduce parallelism

References

  • Related skill: github-workflows-pgpm for general PGPM CI/CD
  • Related skill: pgpm (references/testing.md) for database testing
  • Related skill: rag-pipeline for RAG implementation
  • Related skill: ollama-integration for Ollama client
  • Ollama Docker documentation

平台分布

Codex

35.49%
按下载量换算45

Claude

27.87%
按下载量换算36

Cursor

17.81%
按下载量换算23

Gemini CLI

9.01%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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