name: Detect duplicate issues
on:
issues:
types: [opened, reopened]
permissions:
models: read
issues: write
jobs:
continuous-triage-dedup:
if: ${{ github.event.issue.user.type != 'Bot' }}
runs-on: ubuntu-latest
steps:
- uses: pelikhan/action-genai-issue-dedup@v0
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
# Optional tuning:
# labels: "auto" # compare within matching labels, or "bug,api"
# count: "20" # how many recent issues to check
# since: "90d" # look back window, supports d/w/m
name: Issue Completeness Check
on:
issues:
types: [opened]
permissions:
issues: write
models: read
jobs:
check-completeness:
runs-on: ubuntu-latest
steps:
- name: Check issue completeness
uses: actions/ai-inference@v1
id: ai
with:
prompt: |
Analyze this GitHub issue for completeness. If missing reproduction steps, version info, or expected/actual behavior, respond with a friendly request for the missing info. If complete, say so.
Title: ${{ github.event.issue.title }}
Body: ${{ github.event.issue.body }}
system-prompt: You are a helpful assistant that helps analyze GitHub issues for completeness.
model: openai/gpt-4o-mini
temperature: 0.2
- name: Comment on issue
if: steps.ai.outputs.response != ''
uses: actions/github-script@v7
with:
script: |
github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: ${{ github.event.issue.number }},
body: `${{ steps.ai.outputs.response }}`
})
.vscode/mcp.json 配置檔並進行 GitHub OAuth 驗證即可。Source: https://github.blog/ai-and-ml/generative-ai/automate-your-project-with-github-models-in-actions/
permissions 區塊中加入 models: read，並建議遵循最小權限原則，以降低提示詞注入攻擊 (prompt injection) 風險。
permissions:
contents: read
issues: write
models: read
actions/ai-inference@v1 動作分析 Issue 內容，判斷錯誤報告是否包含足夠的重現資訊（例如：重現步驟、預期行為、實際行為、環境細節）。pass 或詳細說明）建立工作流程中的條件邏輯。- name: Analyze Issue For Reproduction
if: contains(join(github.event.issue.labels.*.name, ','), 'bug')
id: analyze-issue
uses: actions/ai-inference@v1
with:
model: mistral-ai/ministral-3b
system-prompt: |
Given a bug report title and text for an application, return 'pass' if there is enough information to reliably reproduce the issue, meaning the report clearly describes the steps to reproduce the problem, specifies the expected and actual behavior, and includes environment details such as browser and operating system; if any of these elements are missing or unclear, return a brief description of what is missing in a friendly response to the author instead of 'pass'. Consider the following title and body:
prompt: |
Title: ${{ steps.issue.outputs.title }}
Body: ${{ steps.issue.outputs.body }}
gh CLI 搭配 gh-models 擴充功能，在 Pull Request 合併時自動摘要其標題、內容、評論及審閱，並將摘要內容追加到指定的發布說明 Issue 中。cat pr.json | gh models run xai/grok-3-mini \
"Given the following pull request information, generate a single, clear, and concise one-line changelog entry that summarizes the main change (feature, fix, or bug) introduced by this PR. Use neutral, user-facing language and avoid technical jargon or internal references. Only write the line, with no additional introduction or explanation text." > summary.md
gh CLI 抓取過去一週新開啟的 Issue，並將其傳遞給 AI 模型進行摘要、歸納主題及優先級排序，最終創建一個新的 Issue 來呈現週報摘要。.prompt.yml 提示文件，提供更複雜的提示邏輯。
Source: https://blog.jetbrains.com/idea/2025/07/intellij-idea-2025-2-beta/
Source: https://www.docker.com/blog/docker-compose-powering-the-full-app-lifecycle/
內容摘要：
本文強調 Docker Compose 及其擴展工具 Compose Bridge 在整個應用程式生命周期中的關鍵作用，將本地開發、測試、部署到生產的流程串聯起來，成為「脊椎骨」般的支撐架構。透過自動化、標準化和環境隔離，提高效率、安全性和可靠性，降低人為錯誤，並支援多階段、多環境的整合。