Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/build-with-dhiraj/ai-workflow-framework-portability-kitnpx agentmods add skills/build-with-dhiraj/ai-workflow-framework-portability-kit/github-actionsWrote 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/build-with-dhiraj/ai-workflow-framework-portability-kit/github-actions)<a href="https://agentmods.dev/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/github-actions"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/github-actions/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/build-with-dhiraj/ai-workflow-framework-portability-kit/github-actions"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/github-actions.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 | $0.00038 | $0.01651 |
| Opus 5.5 | $0.00015 | $0.00660 |
| Sonnet 5.5 | $0.00008 | $0.00330 |
| Haiku 4.5 | $0.00004 | $0.00165 |
Grade A, and why
github-actions 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.
How it starts
The opening of the file, as written. The whole thing — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Actions CI/CD
Dispatch to: devops-automator agent.
Reference: https://github.com/actions/starter-workflows
1. Workflow Anatomy
name: CI
on: <trigger> # when to run
jobs:
job-name:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4 # reusable action
- run: npm test # shell command
Key fields: on, jobs.<id>.runs-on, steps[].uses, steps[].run, steps[].with, steps[].env.
2. Key Triggers
on:
push:
branches: [main]
pull_request:
branches: [main]
schedule:
- cron: '0 9 * * 1' # Mondays 9am UTC
workflow_dispatch: # manual "Run workflow" button
3. TypeScript / Node.js CI (primary stack)
.github/workflows/ci.yml
name: CI
on:
push:
branches: [main]
pull_request:
branches: [main]
jobs:
ci:
runs-on: ubuntu-latest
strategy:
matrix:
node-version: [20.x, 22.x]
steps:
- uses: actions/checkout@v4
- name: Setup Node ${{ matrix.node-version }}
uses: actions/setup-node@v4
with:
node-version: ${{ matrix.node-version }}
cache: npm # built-in cache — no extra step needed
- run: npm ci
- run: npm run typecheck --if-present
- run: npm run lint --if-present
- run: npm test --if-present
- run: npm run build --if-present
setup-node@v4 with cache: npm handles node_modules caching automatically. Saves ~60% of install time.
4. Python CI (automation stack)
.github/workflows/python-ci.yml
name: Python CI
on:
push:
branches: [main]
pull_request:
branches: [main]
jobs:
test:
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.11", "3.12"]
steps:
- uses: actions/checkout@v4
- name: Setup Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
cache: pip
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install flake8 pytest
- name: Lint
run: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
- name: Test
run: pytest
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 · 278 lines · 38 tokens per session scan A 2a6cc40562f3
github-actions is a skill published in the GitHub repository build-with-dhiraj/ai-workflow-framework-portability-kit (5 stars, last pushed 21d ago), licensed MIT. It adds 38 tokens to every session and 1,651 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-21.
Other skills, from other repositories
agentic-actions-auditor
Audit GitHub Actions that run AI agents for prompt injection, unsafe interpolation, sandbox gaps, and permissive actor rules. Use for agentic CI workflows, not general application code review.
quality-scan
Run a scoped, read-only quality scan and report exact-candidate evidence, failures and frozen debt. Use for an ad-hoc gate sweep or release preparation; a static scan is not full release acceptance.
vercel
Inspect and manage the user's Vercel websites, web apps, projects, and deployments. Use to read build and runtime logs, investigate production errors and failed deployments, check deployment status, and deploy projects through Vercel's official MCP server.
huggingface-vision-trainer
Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs. Use for vision fine-tuning and evaluation; use huggingface-llm-trainer for language models.
insecure-defaults
Detects fail-open insecure defaults (hardcoded secrets, weak auth, permissive security) that allow apps to run insecurely in production. Use when auditing security, reviewing config management, or analyzing environment variable handling.
ai-slop
Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design. Use as the reference rubric when building or reviewing marketing sites, product interfaces, dashboards, portfolios, or e-commerce pages, especially alongside…