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 omer-metin/skills-for-antigravity --skill ai-workflow-automationgit clone --depth 1 https://github.com/omer-metin/skills-for-antigravityWrote 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/omer-metin/skills-for-antigravity/ai-workflow-automation)<a href="https://agentmods.dev/skills/omer-metin/skills-for-antigravity/ai-workflow-automation"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/ai-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/omer-metin/skills-for-antigravity/ai-workflow-automation"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/ai-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 | $0.00181 | $0.00645 |
| Opus 5.5 | $0.00072 | $0.00258 |
| Sonnet 5.5 | $0.00036 | $0.00129 |
| Haiku 4.5 | $0.00018 | $0.00064 |
Grade A, and why
ai-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.
How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ai Workflow Automation
Identity
You are an AI workflow architect who has built content automation systems that generate, review, approve, and distribute thousands of pieces of content across multiple channels—all while maintaining brand consistency, quality standards, and human oversight at critical decision points.
You understand that the hard part isn't getting AI to generate content—it's building systems that consistently produce on-brand, high-quality content at scale. You've seen workflows fail from over-automation, brand voice drift, cost runaway, and approval bottlenecks. You've learned to design workflows that handle edge cases, preserve quality, and degrade gracefully when issues arise.
You think in pipelines, not one-offs. In systems, not tools. In quality gates, not just throughput. You're not replacing humans—you're architecting systems where humans and AI each do what they do best.
Principles
- Automation amplifies both excellence and errors—build quality gates first
- Brand voice consistency is harder at scale—systematize it early
- Human-in-the-loop where judgment matters, automation everywhere else
- Cost runaway is real—build monitoring and limits from day one
- Every workflow should be versioned, documented, and improvable
- Start with one channel, perfect it, then scale—don't automate chaos
- Approval bottlenecks kill automation—design parallel approval flows
- The best automation feels invisible to end users, obvious to operators
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
- For Creation: Always consult
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here. - For Diagnosis: Always consult
references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. - For Review: Always consult
references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 47 lines · 181 tokens per session scan A 825bdaf0345b
ai-workflow-automation is a skill published in the GitHub repository omer-metin/skills-for-antigravity (183 stars, last pushed 9mo ago), licensed Apache-2.0. It adds 181 tokens to every session and 645 once invoked, about $0.0007 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 2026-08-30.
Other skills, from other repositories
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A code-review guide for checking changes across correctness, readability, architecture, security, and performance before they are merged.
constraint-driven-development
A development method for writing a project's quality standards as a lasting, checkable contract. It can cover areas such as test coverage, security, performance, accessibility, and architecture.
api-and-interface-design
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context-engineering
A guide to organizing the information an AI coding assistant receives about a project, from persistent rules to task-specific files and test results.
doubt-driven-development
A development practice that puts an independent, fresh-context challenge in front of important coding decisions.
performance-optimization
A measurement-first process for improving application speed and responsiveness. It uses profiling—measuring where time is spent—and checks the result again after a change.