Ruflo is an execution and coordination layer for Claude Code and Codex that equips AI coding agents with tools, memory, control loops, sandboxes, and collaboration mechanisms. Developers use it to organize specialized agents into swarms, coordinate workflows, retain knowledge across sessions, and communicate across machines. The catalogue entries are Ruflo’s skills, commands, agents, hooks, and plugin components.
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 agentmods add skills/ruvnet/ruflo/agent-workflow-automationnpx skills add ruvnet/ruflo --skill agent-workflow-automationgit clone --depth 1 https://github.com/ruvnet/rufloWrote 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/ruvnet/ruflo/agent-workflow-automation)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-workflow-automation"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-workflow-automation.svg" alt="Measured on agentmods" 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 | $0.00010 | $0.01977 |
| Sonnet 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 2d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- agent-workflow-automation — 100% identical, 0 lines differ
- agent-workflow-automation — 100% identical, 0 lines differ
- agent-workflow-automation — 100% identical, 0 lines differ
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.
- 2d ago First seen · 640 lines · 21 tokens per session scan A f839bdca77c5
agent-workflow-automation is a skill published in the GitHub repository ruvnet/ruflo (70,498 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 3,954 once invoked, about $0.0001 per session on Opus 5. 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 2026-09-03.
Other skills, from other repositories
nw-devops
Designs CI/CD pipelines, infrastructure, observability, and deployment strategy. Use when preparing platform readiness for a feature.
nw-cicd-and-deployment
CI/CD pipeline design methodology, deployment strategies, GitHub Actions patterns, and branch/release strategies. Load when designing pipelines or deployment workflows.
nw-infrastructure-and-observability
Infrastructure as Code patterns (Terraform, Kubernetes), observability design (SLOs, metrics, alerting, dashboards), and pipeline security stages. Load when designing infrastructure, observability, or security scanning.
nw-platform-engineering-foundations
Foundational platform engineering knowledge from key references -- Continuous Delivery, SRE, Accelerate, Team Topologies, Chaos Engineering, and Secure Delivery. Load when contextual grounding in platform engineering theory is needed.
nw-par-critique-dimensions
Platform design review critique dimensions and severity levels. Load when reviewing CI/CD pipelines, infrastructure, deployment strategies, observability, or security designs.
nw-par-review-criteria
Quality dimensions and review checklist for devop reviews.