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 anycap-ai/anycap --skill anycap-media-productiongit clone --depth 1 https://github.com/anycap-ai/anycapWrote 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/anycap-ai/anycap/anycap-media-production)<a href="https://agentmods.dev/skills/anycap-ai/anycap/anycap-media-production"><img src="https://agentmods.dev/badge/skills/anycap-ai/anycap/anycap-media-production/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/anycap-ai/anycap/anycap-media-production"><img src="https://agentmods.dev/badge/skills/anycap-ai/anycap/anycap-media-production.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00167 | $0.03129 |
| Opus 5.5 | $0.00067 | $0.01252 |
| Sonnet 5.5 | $0.00033 | $0.00626 |
| Haiku 4.5 | $0.00017 | $0.00313 |
Grade A, and why
anycap-media-production 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 7d 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 — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AnyCap Media Production
Read this entire file before starting. It covers the full production workflow across image, video, music, and audio -- including iterative refinement with human feedback.
Workflow guide for producing media assets with AnyCap. Covers image, video, music, and audio -- from initial generation through iterative refinement to delivery.
This skill is about how to produce media. For CLI command reference and parameters, read the anycap-cli skill.
Prerequisites
AnyCap CLI must be installed and authenticated. Read the anycap-cli skill if setup is needed.
Quick Reference
| Media | Generate | Refine | Typical duration |
|---|---|---|---|
| Image | anycap image generate |
Annotate + image-to-image | 5-30s |
| Video | anycap video generate |
Re-generate with adjusted params | 30-120s |
| Music | anycap music generate |
Re-generate with adjusted prompt | 30-90s |
| Audio | anycap audio generate |
Re-generate with adjusted prompt or references | Model-dependent |
All generation commands follow the same pattern:
1. Discover models anycap {cap} models
2. Check schema anycap {cap} models <model> schema [--mode <mode>]
3. Generate anycap {cap} generate --model <model> --prompt "..." -o output.ext
Always choose model IDs from the live model catalog and inspect the schema for
the selected mode before relying on model-specific parameters. Always use -o
with a descriptive filename.
Image Production
Text-to-Image
Generate an image from a text prompt:
anycap image generate \
--prompt "a cozy home office with a wooden desk, laptop, coffee cup, and plants by the window" \
--model <model-id> \
-o workspace-v1.png
Image-to-Image (Edit / Transform)
Use --mode image-to-image with a reference image to edit or transform an existing image:
anycap image generate \
--prompt "make it a watercolor painting" \
--model <model-id> \
--mode image-to-image \
--param images=./photo.png \
-o photo-watercolor.png
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.
- 7d ago Changed d0960f6a075e
- 28d ago Changed 60220824e900
- 1mo ago Changed 6ed9c9d7c4ed
- 1mo ago Changed f5108dc5c690
- 1mo ago First seen · 357 lines · 167 tokens per session scan A 36c415a1915f
anycap-media-production is a skill published in the GitHub repository anycap-ai/anycap (46 stars, last pushed 7d ago), licensed MIT. It adds 167 tokens to every session and 3,129 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.
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