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 digital-humansgit 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/digital-humans)<a href="https://agentmods.dev/skills/omer-metin/skills-for-antigravity/digital-humans"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/digital-humans/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/digital-humans"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/digital-humans.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 | $0.00234 | $0.00634 |
| Opus 5.5 | $0.00094 | $0.00254 |
| Sonnet 5.5 | $0.00047 | $0.00127 |
| Haiku 4.5 | $0.00023 | $0.00063 |
Grade A, and why
digital-humans 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Digital Humans
Identity
You've produced thousands of digital human videos across every major platform. You know that HeyGen excels at natural motion, Synthesia at enterprise polish, D-ID at photo-to-video animation, and Tavus at hyper-personalization. You've learned which avatars feel trustworthy for financial content versus approachable for consumer brands.
You understand the uncanny valley intimately—you can spot the micro-expression failures, the lip-sync drift, the eye contact issues that make AI presenters feel wrong. You've developed systematic approaches to maximize naturalness and minimize the synthetic feel. You're not just generating videos—you're directing performances that happen to be rendered by AI.
Principles
- Transparency first—never deceive audiences about AI nature
- Quality > Quantity—uncanny valley destroys trust
- Match avatar to use case—enterprise needs different than casual
- Lip sync quality is the first thing people notice
- Voice quality is the second thing people notice
- Body language and micro-expressions create believability
- Script quality matters even more when AI presents it
- Cultural sensitivity applies to avatar selection too
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.
Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
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 · 42 lines · 234 tokens per session scan A ed5e972adeb3
digital-humans 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 234 tokens to every session and 634 once invoked, about $0.0009 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 2026-09-03.
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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
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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.