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MCP server · Security

AI Devops Actions

The CI/CD layer for AI-native development — 5 GitHub Actions for PR quality, cost tracking, MCP testing, supply chain security, and agent skill validation

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Scored 6 months ago · breakdown

About AI Devops Actions

AI Devops Actions is an MCP server in the Security category: the CI/CD layer for AI-native development — 5 GitHub Actions for PR quality, cost tracking, MCP testing, supply chain security, and agent skill validation.

Install

From source
git clone https://github.com/ollieb89/ai-devops-actions

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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README

AI DevOps Actions

AI DevOps Actions Suite Setup time No LLM required No external API

🔴 Live example: Open PR #1 (canonical demo) or any recent PR to see the full suite running on itself — context summary, quality score, and root cause hints, all from GITHUB_TOKEN.

The full CI/CD layer for AI-native development — 8 GitHub Actions covering PR quality, safety, cost, infra, and behavioral testing.

AI-native repos have problems that standard CI/CD doesn't solve. PRs flooded with AI slop. Unchecked LLM spend. Sensitive data leaking through AI outputs. MCP servers shipped without validation. Action tags silently compromised. Agent skills published without schema checks. Behavioral regressions invisible until production.

This suite covers the full stack — eight GitHub Actions that work independently or as a pipeline.


🧠 The AI Reliability Loop

Most CI tells you something broke. This system tells you why — and what to do next.

Three things this loop does that standard CI/CD can't:

Stage What it does
🔍 Detect Catch regressions in AI behavior — not just code
🧠 Explain Identify root cause: prompt change, model drift, data shift, cost spike
🔧 Fix Turn failures into actionable feedback, automatically

Example output when your AI pipeline breaks:

❌ Eval failed

Root Cause Analysis:
→ Knowledge drift (HIGH confidence)
→ RAG corpus changed 2 commits ago
→ Eval suite: 3/12 assertions failed

Suggested fix:
→ Re-run evals with updated embeddings
→ Check: ai-workflow-evals + llm-cost-tracker

AI systems don't fail like code — they degrade silently, drift with data, and pass tests while producing worse outputs. This is the CI layer that catches it.


⚡️ 1-minute setup

  1. Copy this into .github/workflows/ai-hygiene.yml in any repo:
name: AI PR Hygiene
on:
  pull_request:
    types: [opened, synchronize, reopened]
jobs:
  hygiene:
    runs-on: ubuntu-latest
    permissions:
      pull-requests: write
      contents: read
    steps:
      - uses: actions/checkout@v4
      - uses: ollieb89/pr-context-enricher@v1.0.0
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
      - uses: ollieb89/ai-pr-guardian@v1.0.0
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
          threshold: 60
      - uses: ollieb89/ai-root-cause-hints@v1.0.0
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
  1. Open a PR.

  2. You'll see:

🔍 PR Context Summary
Author: @you | Base: main ← feature/my-change
5 files changed (+120/-30) | Risk: low | Complexity: 3/10
Related issues: #42

✅ PR Quality Score: 78/100

🟢 No correlated failure patterns detected.

No API keys. No external services. Just GITHUB_TOKEN.


Start here

New to the suite? Pick your entry point:

You want to... Start with
Gate AI-generated PR slop ai-pr-guardian
Give AI reviewers full PR context pr-context-enricher
Stop secrets leaking from AI outputs ai-output-redacter
Lock down your supply chain actions-lockfile-generator
Catch agent behavioral regressions ai-workflow-evals
Validate your MCP server in CI mcp-server-tester
Publish agent skills safely agent-skill-validator
Understand why your AI pipeline broke ai-root-cause-hints
Track LLM spend before it hits your card llm-cost-tracker

Each action works standalone. The full pipeline shows how they compose.

The Suite

🔍 PR Quality & Context

Action What it solves
ai-pr-guardian Scores PR quality 0–100, detects AI-generated slop, gates merges
pr-context-enricher Auto-generates rich context summaries: files, risk level, commit history, ready-to-paste AI reviewer prompt

🛡️ Safety & Security

Action What it solves
ai-output-redacter Scans and redacts API keys, tokens, PII, and secrets from AI-generated outputs before they leave CI
actions-lockfile-generator Pins all uses: to full commit SHAs — prevents supply chain attacks

🧪 Testing & Behavioral Validation

Action What it solves
ai-workflow-evals Runs eval suites for prompts, agents, and workflows — catches behavioral regressions before merge
mcp-server-tester Validates MCP servers: health, protocol compliance, tool/resource discovery
agent-skill-validator Lints and validates agent skill repos (OpenClaw, Claude Code, Codex, Gemini)

💰 Infrastructure & Cost

Action What it solves
llm-cost-tracker Tracks OpenAI/Anthropic/Gemini spend in CI, alerts on budget overruns


Usage paths — start with your problem

Don't know which action to use? Pick your problem:

My problem Recommended stack
AI outputs regressed, don't know why AI Debugging Stack — evals + cost tracker + root cause hints
PRs are noisy and AI-sloppy PR Hygiene Stack — context enricher + guardian + lockfile
Worried about secrets leaking AI Safety Stack — output redacter + lockfile + root cause hints
Shipping agent skills or MCP servers agent-skill-validator + mcp-server-tester
Want everything Full pipeline

→ Detailed "when to use which action" guide

Full Pipeline

jobs:
  ai-devops:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      # Enrich PR with context for AI reviewers
      - id: context
        uses: ollieb89/pr-context-enricher@v1.0.0
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}

      # Gate AI-generated / low-quality PRs before review
      - uses: ollieb89/ai-pr-guardian@v1.0.0
        with:
          threshold: 60
          on-low-quality: comment

      # Scan AI outputs for secrets and PII before they ship
      - uses: ollieb89/ai-output-redacter@v1.0.0
        with:
          path: ./outputs
          mode: enforce

      # Enforce SHA pinning on all workflow action references
      - uses: ollieb89/actions-lockfile-generator@v1.0.0
        with:
          mode: enforce
          github-token: ${{ secrets.GITHUB_TOKEN }}

      # Catch behavioral regressions in prompts and agents
      - uses: ollieb89/ai-workflow-evals@v1.0.0
        with:
          eval-suite: ./evals
          fail-on: regression

      # Track what this run cost in LLM calls
      - uses: ollieb89/llm-cost-tracker@v1.0.0
        with:
          provider: anthropic
          budget-limit: '1.00'

      # Validate MCP server didn't regress
      - uses: ollieb89/mcp-server-tester@v1.0.0
        with:
          transport: stdio
          server-command: "node dist/server.js"

      # Validate agent skills before publish
      - uses: ollieb89/agent-skill-validator@v1.0.0
        with:
          ecosystem: auto
          fail-on: errors

Install any action independently

# PR Quality & Context
uses: ollieb89/ai-pr-guardian@v1.0.0
uses: ollieb89/pr-context-enricher@v1.0.0

# Safety & Security
uses: ollieb89/ai-output-redacter@v1.0.0
uses: ollieb89/actions-lockfile-generator@v1.0.0

# Testing & Behavioral Validation
uses: ollieb89/ai-workflow-evals@v1.0.0
uses: ollieb89/mcp-server-tester@v1.0.0
uses: ollieb89/agent-skill-validator@v1.0.0

# Infrastructure & Cost
uses: ollieb89/llm-cost-tracker@v1.0.0

All actions are MIT licensed, independently versioned, and production-ready.


Suite stats

Layer Actions Total tests
PR Quality & Context 2 86
Safety & Security 2 127
Testing & Behavioral Validation 4 180
Infrastructure & Cost 1 48
Total 9 441

Related tools

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About AI Devops Actions

How do I install AI Devops Actions?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is AI Devops Actions safe to use with an AI agent?

Its trust score is 34 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is AI Devops Actions still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.