workflow-automation

A GitHub Actions setup that uses groups of AI agents to inspect code changes and run development checks. GitHub Actions is GitHub's system for automatically running jobs when code is pushed or a pull request is opened.

In plain words
What is it for?
Use it to analyze commits, suggest tests, generate workflow files, choose affected tests, and run safe tests in parallel.
Why use it?
It helps tailor automated checks to the codebase and avoid running every test when only a smaller set may be affected.

Command for Claude Code

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/ruvnet/ruview/workflow-automation
Clone the repo
git clone --depth 1 https://github.com/ruvnet/RuView

Made for: Claude Code.

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,391 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.02391
Opus 5 $0.00000 $0.01196
Sonnet 5 $0.00000 $0.00478
Haiku 4.5 $0.00000 $0.00239

Measured 3d ago against content hash 2b3e37886411, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

workflow-automation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

8 near-identical copies found in the catalogue:

.claude/commands/github/workflow-automation.md · 442 lines

How it starts

The opening of the file, as written. The whole thing — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Workflow Automation - GitHub Actions Integration

Overview

Integrate AI swarms with GitHub Actions to create intelligent, self-organizing CI/CD pipelines that adapt to your codebase.

Core Features

1. Swarm-Powered Actions

# .github/workflows/swarm-ci.yml
name: Intelligent CI with Swarms
on: [push, pull_request]

jobs:
  swarm-analysis:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Initialize Swarm
        uses: ruvnet/swarm-action@v1
        with:
          topology: mesh
          max-agents: 6
          
      - name: Analyze Changes
        run: |
          npx ruv-swarm actions analyze \
            --commit ${{ github.sha }} \
            --suggest-tests \
            --optimize-pipeline

2. Dynamic Workflow Generation

# Generate workflows based on code analysis
npx ruv-swarm actions generate-workflow \
  --analyze-codebase \
  --detect-languages \
  --create-optimal-pipeline

3. Intelligent Test Selection

# Smart test runner
- name: Swarm Test Selection
  run: |
    npx ruv-swarm actions smart-test \
      --changed-files ${{ steps.files.outputs.all }} \
      --impact-analysis \
      --parallel-safe

Workflow Templates

Multi-Language Detection

# .github/workflows/polyglot-swarm.yml
name: Polyglot Project Handler
on: push

jobs:
  detect-and-build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Detect Languages
        id: detect
        run: |
          npx ruv-swarm actions detect-stack \
            --output json > stack.json
            
      - name: Dynamic Build Matrix
        run: |
          npx ruv-swarm actions create-matrix \
            --from stack.json \
            --parallel-builds

Adaptive Security Scanning

# .github/workflows/security-swarm.yml
name: Intelligent Security Scan
on:
  schedule:
    - cron: '0 0 * * *'
  workflow_dispatch:

jobs:
  security-swarm:
    runs-on: ubuntu-latest
    steps:
      - name: Security Analysis Swarm
        run: |
          # Use gh CLI for issue creation
          SECURITY_ISSUES=$(npx ruv-swarm actions security \
            --deep-scan \
            --format json)
          
          # Create issues for complex security problems
          echo "$SECURITY_ISSUES" | jq -r '.issues[]? | @base64' | while read -r issue; do
            _jq() {
              echo ${issue} | base64 --decode | jq -r ${1}
            }
            gh issue create \
              --title "$(_jq '.title')" \
              --body "$(_jq '.body')" \
              --label "security,critical"
          done

Read the full file on GitHub · 442 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 442 lines · 0 tokens per session scan A 2b3e37886411

Subscribe to this mod's changes

workflow-automation is a command published in the GitHub repository ruvnet/RuView (92,289 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,391 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.