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 ffmpeg-media-finishinggit 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/ffmpeg-media-finishing)<a href="https://agentmods.dev/skills/calesthio/generative-media-skills/ffmpeg-media-finishing"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/ffmpeg-media-finishing/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/ffmpeg-media-finishing"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/ffmpeg-media-finishing.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 | $0.00106 | $0.08753 |
| Opus 5 | $0.00053 | $0.04376 |
| Sonnet 5 | $0.00021 | $0.01751 |
| Haiku 4.5 | $0.00011 | $0.00875 |
Grade A, and why
ffmpeg-media-finishing 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 9d 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 — 724 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FFmpeg media finishing
Use this skill at the finishing stage: after creative generation or editing has produced source media and before the user receives deliverables. Your job is to turn fragile, inconsistent media outputs into reproducible, spec-aware files with an audit trail.
This skill is provider-independent. It assumes local ffmpeg and ffprobe are available, but not that every codec, filter, hardware encoder, or subtitle renderer is compiled into the installed build. FFmpeg build capabilities, encoder names, platform upload rules, and social specs are volatile; re-check them at production time with the commands below and with the current delivery spec.
Operating rule
Do not guess the state of media. Probe first, decide, run deterministic commands, then probe outputs.
Every finishing handoff should include:
- input audit summary from
ffprobe; - chosen delivery spec and any unresolved assumptions;
- exact commands run, including FFmpeg version/build;
- output audit summary;
- checksums and, when useful, frame hashes;
- known risks such as re-encoded captions, color metadata ambiguity, clipping, lossy transcodes, or platform-specific uncertainty.
Bundled deterministic assistance
This skill includes a deterministic Python 3.11+ standard-library helper at scripts/media_probe.py. Use it when you need a repeatable technical audit or a quick delivery-spec gate before or after finishing. It is assistance, not a replacement for perceptual review, color-managed approval, audio listening, caption/accessibility review, rights review, or platform/client signoff.
Documented fact, verified 2026-07-11: ffprobe is designed to gather multimedia stream information and can print JSON with -show_format, -show_streams, and -print_format json; if an input cannot be opened or recognized it returns a positive exit code. The helper invokes ffprobe with a subprocess argv list, never through a shell, and never runs ffmpeg or mutates media.
Invocation:
python skills/production/runtime-assembly/ffmpeg-media-finishing/scripts/media_probe.py input.mp4 --pretty
python skills/production/runtime-assembly/ffmpeg-media-finishing/scripts/media_probe.py input.mp4 --expect expected.json --pretty
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.
- 9d ago First seen · 724 lines · 106 tokens per session scan A 65891126d9a8
ffmpeg-media-finishing is a skill published in the GitHub repository calesthio/generative-media-skills (170 stars, last pushed 2mo ago), licensed MIT. It adds 106 tokens to every session and 8,753 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
cliptalk-cover-director
Produces evidence-backed cover candidates and reviewable cover variants for a ClipTalk video. Use when the user asks for a cover, poster frame, thumbnail, or multiple cover directions; do not use for timeline editing or social-video reframing.
cliptalk-smart-reframe
Creates a subject-aware, time-varying crop track and a review-only social-format preview from an accepted ClipTalk cut. Use for automatic vertical, square, or portrait reframing; do not use for a fixed manual crop or before content editing is accepted.
cliptalk-content-extractor
Locates and assembles source passages matching a semantic request. Use for extracting explanations, topics, quotes, demonstrations, or other specifically described content.
cliptalk-interview-editor
Produces a coherent interview edit by combining speaker discovery, topic selection, dialogue context, cleanup, subtitles, and preview. Use for interviews, podcasts, testimonials, or question-and-answer recordings.
cliptalk-shortform-hook-director
Finds and assembles a reviewable short-form cut with a strong opening hook. Use for Shorts, Reels, social clips, talking-head cutdowns, or requests for a punchier opening.
cliptalk-social-reframe-exporter
Creates a review-only 9:16, 4:5, 1:1, or 16:9 version from an existing accepted ClipTalk cut, then checks the rendered preview. Use only when a cut already exists and the user asks to adapt it for Shorts, Reels, Douyin, Xiaohongshu, WeChat Channels, or square feeds; do not use when the user still needs content found…