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 calesthio/generative-media-skills --skill generated-media-qagit clone --depth 1 https://github.com/calesthio/generative-media-skillsWrote 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/calesthio/generative-media-skills/generated-media-qa)<a href="https://agentmods.dev/skills/calesthio/generative-media-skills/generated-media-qa"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/generated-media-qa/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/calesthio/generative-media-skills/generated-media-qa"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/generated-media-qa.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00071 | $0.06764 |
| Opus 5 | $0.00036 | $0.03382 |
| Sonnet 5 | $0.00014 | $0.01353 |
| Haiku 4.5 | $0.00007 | $0.00676 |
Grade A, and why
generated-media-qa 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 11d 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 — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generated Media QA
Treat QA as a release decision, not a vibe check. Judge the deliverable against the approved brief, platform specifications, legal/safety constraints, and the audience context. Record enough evidence that another agent or producer can reproduce the decision.
Keep three evidence lanes separate
Documented facts are requirements from the brief, platform specs, legal/policy guidance, delivery standards, accessibility standards, or provider documentation. Cite them or name the source and verification date.
Empirical observations are what you directly measured or inspected in the asset: frame size, duration, loudness, sync offset, OCR output, transcript mismatch, visual artifact, metadata, or a timestamped defect.
Production heuristics are professional judgments used when no explicit spec exists: whether a hand artifact is audience-visible, whether a product packshot feels trustworthy, whether an accent is intelligible for the target market, whether a social caption is too fast for mobile. Label these as heuristics and avoid pretending they are universal standards.
Intake before review
Do not start with random artifact hunting. Build the acceptance frame first.
Collect:
- Approved brief, prompt, storyboard, script, shot list, edit decision list, brand rules, product facts, target audience, target platform, duration, aspect ratio, language/locale, and known compromises.
- Delivery spec: file format, codec, resolution, frame rate, color space/HDR, audio channels, loudness target, captions format, thumbnail and metadata requirements.
- Source inventory: generated assets, human-shot assets, licensed stock, user-provided media, logos, fonts, music, SFX, voices, product claims, model releases, likeness/voice consent, and usage rights.
- Generation metadata when available: provider, model, model version/date, prompt, negative prompt, reference images, seed, dimensions, duration, fps, voice ID, language, post tools, upscalers, edit tools, and safety filters.
- Risk context: ads, health/finance/legal claims, political content, public figures, minors, regulated products, synthetic endorsements, realistic news-like scenes, localization, accessibility obligations, and platform disclosure requirements.
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.
- 11d ago First seen · 351 lines · 71 tokens per session scan A cd4d34371223
generated-media-qa is a skill published in the GitHub repository calesthio/generative-media-skills (170 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 6,764 once invoked, about $0.0004 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 2026-08-30.
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