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.
git clone --depth 1 https://github.com/frankxai/agentic-creator-osWrote 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/commands/frankxai/agentic-creator-os/workflow-automation)<a href="https://agentmods.dev/commands/frankxai/agentic-creator-os/workflow-automation"><img src="https://agentmods.dev/badge/commands/frankxai/agentic-creator-os/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/commands/frankxai/agentic-creator-os/workflow-automation"><img src="https://agentmods.dev/badge/commands/frankxai/agentic-creator-os/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 | $0.00000 | $0.02391 |
| Opus 5.5 | $0.00000 | $0.00956 |
| Sonnet 5.5 | $0.00000 | $0.00478 |
| Haiku 4.5 | $0.00000 | $0.00239 |
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 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 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 — 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
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 · 442 lines · 0 tokens per session scan A 2b3e37886411
workflow-automation is a command published in the GitHub repository frankxai/agentic-creator-os (12 stars, last pushed today), licensed Apache-2.0. 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. It is 100% identical to workflow-automation, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
security-audit-static
Static security audit of AI-built code — map trust boundaries, cross-reference documented intent, self-refute every finding, and report only evidence-backed risks. Scope it to a release's changes with a git range ( .. ) or changes for the uncommitted working tree; audit the whole repository for the first release or…
document-app
Reverse-engineer an AI-built codebase into the system documents reviewers and auditors need — a core set (architecture, flows, permissions, variables) plus conditional docs (emails, cron, SEO, automation) when they apply. check mode writes nothing and reports where the existing docs and the code disagree.
optimize
Analyze this repo and generate per-repo adaptive scripts from learned patterns, git history, and existing configuration.
deploy-pr
Ship a branch to its deploy target — detect or create PR, fix CI, review, merge. Returns structured result for callers.
diagnose
Trace a failed or stalled pipeline run — identify which FORGE annotation is missing or malformed, diagnose the failure point, and suggest specific remediation steps.
fix-ci
Automated CI failure resolution loop — diagnoses failures on a PR, applies targeted fixes, pushes, and loops until green or max attempts reached.