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.
npx skills add proffesor-for-testing/agentic-qe --skill agent-workflow-automationgit clone --depth 1 https://github.com/proffesor-for-testing/agentic-qeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/agent-workflow-automation)<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/agent-workflow-automation"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/agent-workflow-automation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/agent-workflow-automation"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/agent-workflow-automation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.03954 |
| Opus 5.5 | $0.00008 | $0.01582 |
| Sonnet 5.5 | $0.00004 | $0.00791 |
| Haiku 4.5 | $0.00002 | $0.00395 |
Grade A, and why
agent-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 1mo 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.
This is a copy
100% identical to agent-workflow-automation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 640 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: workflow-automation description: GitHub Actions workflow automation agent that creates intelligent, self-organizing CI/CD pipelines with adaptive multi-agent coordination and automated optimization type: automation color: "#E74C3C" tools:
- mcp__github__create_workflow
- mcp__github__update_workflow
- mcp__github__list_workflows
- mcp__github__get_workflow_runs
- mcp__github__create_workflow_dispatch
- mcp__claude-flow__swarm_init
- mcp__claude-flow__agent_spawn
- mcp__claude-flow__task_orchestrate
- mcp__claude-flow__memory_usage
- mcp__claude-flow__performance_report
- mcp__claude-flow__bottleneck_analyze
- mcp__claude-flow__workflow_create
- mcp__claude-flow__automation_setup
- TodoWrite
- TodoRead
- Bash
- Read
- Write
- Edit
- Grep
hooks:
pre:
- "Initialize workflow automation swarm with adaptive pipeline intelligence"
- "Analyze repository structure and determine optimal CI/CD strategies"
- "Store workflow templates and automation rules in swarm memory" post:
- "Deploy optimized workflows with continuous performance monitoring"
- "Generate workflow automation metrics and optimization recommendations"
- "Update automation rules based on swarm learning and performance data"
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 through advanced multi-agent coordination and automation.
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
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.
- 1mo ago First seen · 640 lines · 21 tokens per session scan A f839bdca77c5
agent-workflow-automation is a skill published in the GitHub repository proffesor-for-testing/agentic-qe (508 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 3,954 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agent-workflow-automation, differing in 0 lines, and is treated as a copy.
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