Agent skill

Ffmpeg

by magnus919 in magnus919/agent-skills

A skill your agent uses for local FFmpeg/FFprobe media inspection, remuxing, transcoding, filtering, evidence-bounded video review, transcript-assisted editorial plans, edit decision lists…

MITAuto-check passedMedia & Creative

Install Ffmpeg

skills CLI
$ npx skills add magnus919/agent-skills --skill ffmpeg -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install magnus919/agent-skills ffmpeg --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ffmpeg .claude/skills/ffmpeg && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ffmpeg
GitHub stars
113
Token cost
~3k tokens
SKILL.md length
1,299 words
Files
48 (incl. scripts, references)
Skills in repo
115
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses for local FFmpeg/FFprobe media inspection, remuxing, transcoding, filtering, evidence-bounded video review, transcript-assisted editorial plans, edit decision lists…

  • Works in 7 steps: Intake. Confirm authorization and… → Inspect. Probe streams and format. Check… → Collect bounded evidence. Extract only… → …
  • Local FFmpeg/FFprobe media inspection
  • SKILL.md covers When Not to Use, Boundaries and Routing, Evidence Classes and Media Editing Loop, plus 4 more sections
  • Calls ffprobe

What it does

Ffmpeg is an agent skill from magnus919/agent-skills. Use this skill for local FFmpeg/FFprobe media inspection, remuxing, transcoding, filtering, evidence-bounded video review, transcript-assisted editorial plans, edit decision lists, podcast/audio cleanup, rendering, and output acceptance. It emphasizes explicit stream selection, source preservation, build-aware commands, bounded evidence, and verified new outputs. Do not use it for libav API programming, opaque whole-video understanding, automatic publishing, rights clearance, DRM circumvention, professional…

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 49 other files, including scripts and reference files (for example `README.md`, `evals/evals.json` and `references/advanced-operations-and-safety.md`). Compatibility notes: Requires ffmpeg and ffprobe for execution; exact filters, codecs, protocols, and hardware backends vary by build and version.

It sits in Media & Creative, covering Video production, Motion graphics and Computer vision. It works with FFmpeg and HeyGen. The repository describes itself as: Curated collection of AI agent skills for Hermes and other agent frameworks. The licence is MIT.

When your agent uses it

  • Local FFmpeg/FFprobe media inspection
  • Evidence-bounded video review
  • Transcript-assisted editorial plans
  • Edit decision lists

Example prompts

  • “/ffmpeg”

Requirements

  • Compatibility (from SKILL.md): Requires ffmpeg and ffprobe for execution; exact filters, codecs, protocols, and hardware backends vary by build and version.

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Intake. Confirm authorization and privacy boundaries; identify every source; use a private workspace copy of templates/media-intake.json…
  2. Inspect. Probe streams and format. Check required local capabilities with inventories or scripts/ffmpeg-preflight; never assume a filter…
  3. Collect bounded evidence. Extract only the frames, clips, waveform/signal measurements, or transcript spans needed for the decision…
  4. Plan before rendering. For editorial changes, write a reviewable EDL or podcast edit plan. Every consequential cut needs a source range…
  5. Render safely. Make stream mapping explicit; prefer -n and a new output path; avoid untrusted shell concatenation. Distinguish…
  6. Verify in layers. Check exit status, decodeability, output probe, stream/timing contract, bounded frame/audio evidence, editorial review…
  7. Accept or stop. Use a private workspace copy of templates/media-acceptance-report.md to record pass/fail/blocked per criterion. A valid…

What it can do on your machine

Read from SKILL.md and the folder at commit 96fbe07. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    Shell commands in SKILL.md call:

    • ffprobe

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires ffmpeg and ffprobe for execution; exact filters, codecs, protocols, and hardware backends vary by build and version.

    From compatibility in the SKILL.md frontmatter.

Context cost

Ffmpeg loads about 3k tokens when it runs, and up to ~25k if it reads all its reference files. Until then it costs about 159 tokens; SKILL.md has 1,299 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~159
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~25k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from magnus919/agent-skills at commit 96fbe07, republished under its MIT licence (© magnus919). 1,299 words, ~2,998 tokens.

Download SKILL.mdSave it as .claude/skills/ffmpeg/SKILL.md (or your agent's skills folder). This skill also uses 47 other files; get the full folder from GitHub.
name
ffmpeg
description
Use this skill for local FFmpeg/FFprobe media inspection, remuxing, transcoding, filtering, evidence-bounded video review, transcript-assisted editorial plans, edit decision lists, podcast/audio cleanup, rendering, and output acceptance. It emphasizes explicit stream selection, source preservation, build-aware commands, bounded evidence, and verified new outputs. Do not use it for libav API programming, opaque whole-video understanding, automatic publishing, rights clearance, DRM circumvention, professional broadcast/color certification, or HyperFrames-authored compositions; route those tasks to their owning capabilities.
compatibility
Requires ffmpeg and ffprobe for execution; exact filters, codecs, protocols, and hardware backends vary by build and version.
license
MIT

FFmpeg Expert

Treat FFmpeg as a typed media pipeline and media editing as an evidence-driven workflow. Inspect the actual source, separate measurements from interpretations, make decisions reviewable, render to a new path, and verify at the intended boundary.

When Not to Use

  • Do not use this skill for libav API programming, opaque whole-video semantic understanding, automatic publishing, rights clearance, DRM circumvention, professional broadcast/color certification, or HyperFrames-authored compositions.
  • Route online media or transcript acquisition to the owning source skill, semantic frame interpretation to a vision-capable reviewer, and platform upload/API work to the platform skill.

Boundaries and Routing

  • Use a YouTube/transcript capability to acquire online video or transcripts; return here only for local supplied media and transcript artifacts.
  • Use HyperFrames for HTML-authored motion graphics or composition; use this skill to inspect and preprocess its media inputs or verify rendered outputs.
  • Use the named platform skill for upload, publishing, account, or API operations.
  • FFmpeg can extract bounded frames and audio segments but does not interpret their semantic content. Route visual interpretation to a vision-capable reviewer and preserve its observations as attributed evidence.
  • Do not infer rights, consent, identity, intent, or whole-program meaning from technical metadata, sparse frames, silence intervals, or an unaligned transcript.

Evidence Classes

Label consequential claims so unlike evidence is not blended:

  • Technical contract — behavior documented by an official FFmpeg or standards source.
  • Observed artifact — probe output, measured signal result, extracted frame, listened segment, or downstream test from this source/output.
  • Reproducible experiment — exact version, input identity/generator, command, result, and limits.
  • Editorial heuristic — a reversible judgment that requires human review, not a fact established by FFmpeg.
  • User requirement — the requested output contract, preservation policy, and acceptance threshold.

Media Editing Loop

  1. Intake. Confirm authorization and privacy boundaries; identify every source; use a private workspace copy of templates/media-intake.json to record probe evidence, timing, the output contract, preservation policy, and unresolved assumptions. Keep raw probe output and media in the restricted task workspace, and minimize any shareable derivative.
  2. Inspect. Probe streams and format. Check required local capabilities with inventories or scripts/ffmpeg-preflight; never assume a filter, encoder, or hardware backend exists.
  3. Collect bounded evidence. Extract only the frames, clips, waveform/signal measurements, or transcript spans needed for the decision. Record sample timestamps, count, byte/size limits, and the statement that samples cover sampled times only.
  4. Plan before rendering. For editorial changes, write a reviewable EDL or podcast edit plan. Every consequential cut needs a source range, reason, evidence, confidence, treatment, mapping, and verification state. Leave ambiguous decisions unresolved rather than improvising.
  5. Render safely. Make stream mapping explicit; prefer -n and a new output path; avoid untrusted shell concatenation. Distinguish keyframe-limited stream copy from decoded/re-encoded precise cuts.
  6. Verify in layers. Check exit status, decodeability, output probe, stream/timing contract, bounded frame/audio evidence, editorial review, and the actual downstream consumer as applicable. Treat probe/decode success and destination acceptance as separate gates; a local file can pass the former and fail the latter.
  7. Accept or stop. Use a private workspace copy of templates/media-acceptance-report.md to record pass/fail/blocked per criterion. A valid container or successful command alone is not acceptance.

Start technical inspection with:

sh
ffprobe -v error -show_format -show_streams -of json INPUT

For shipped helper workflows, run the helper from the skill root with explicit output paths and limits; treat its JSON result as a report to verify, not as acceptance by itself. When using extract-review-frames, pass timestamps as separate values (--timestamps 0 5 9), not one comma-separated value.

Route to the Focused Reference

Evidence-driven media work
  • Read references/media-intake-and-manifest.md before handling supplied/generated media, sensitive material, multiple sources, or a defined delivery contract.
  • Read references/video-inspection-and-visual-evidence.md when extracting or reviewing frames/clips, choosing samples, or making visual claims.
  • Read references/editorial-video-editing.md for transcript-assisted selection, sequencing, pacing, transitions, overlays, and reviewable editorial decisions.
  • Read references/audio-and-podcast-editing.md for podcast cuts, signal cleanup, silence/noise analysis, loudness measurement, and listening gates.
  • Read references/ffmpeg-edit-decision-lists.md before creating, validating, or turning an EDL into a command plan.
  • Read references/media-verification-and-acceptance.md before declaring an output complete or compatible.
  • Read references/media-failure-modes.md when evidence is contradictory, a cut drifts, a filter is missing, review samples are sparse, or a workflow repeatedly fails.
  • Read references/media-research-source-index.md when supporting claims, refreshing version-sensitive guidance, or recording a technical experiment.
  • Read references/editorial-workflow-example.md when proving that intake, evidence, EDL, rendering, and acceptance artifacts compose end to end on a synthetic fixture.
  • Read references/synthetic-media-fixtures.md when a change needs bounded real-media fixtures for cuts, cadence, concat, audio, subtitles, or visual-boundary sampling.
Core FFmpeg work
  • Read references/core-model-and-command-anatomy.md for containers, streams, codecs, option scope, mapping, copy/transcode, and timestamps.
  • Read references/filters-and-transformations.md for filtergraphs, labels, audio/video processing, and incremental graph debugging.
  • Read references/intermediate-workflows.md for seeking, trimming, concat, metadata, subtitles, batching, pipes, and streaming.
  • Read references/advanced-operations-and-safety.md for hardware acceleration, synchronization, reproducibility, network safety, and failure boundaries.
  • Read references/command-cookbook.md only after inspection and capability checks; every recipe is conditional.
  • Read references/learning-summary.md for the newcomer-first mental model.
  • Read references/source-inventory.md for the original FFmpeg source survey and references/local-verification.md only for its explicitly host-specific FFmpeg 8.1.2 observations.
Show full SKILL.md (490 more words)Show less

Templates

  • templates/media-intake.json — source identities, probes, contract, privacy, preservation, assumptions
  • templates/edit-decision-list.json — reviewable source ranges and treatments
  • templates/video-inspection-report.md — technical inspection and bounded evidence ledger
  • templates/visual-review-packet.md — attributed frame/clip observations and coverage limits
  • templates/vision-review-observations.json — parseable attributed observation block for a prepared packet
  • templates/podcast-edit-plan.md — mechanical, signal, and editorial audio decisions
  • templates/media-acceptance-report.md — layered verification and criterion verdicts
  • templates/media-acceptance-contract.json — machine-readable stream, format, evidence, loudness, and downstream requirements
  • templates/target-compatibility-manifest.json — one named target, sourced requirements, technical constraints, and downstream lane
  • templates/research-experiment-record.md — reproducible version/command/result record

Run scripts/editorial-workflow-example in a new or empty task-local directory when a reproducible synthetic integration proof is required. Its PASS_WITH_UNVERIFIED_BOUNDARIES result is deliberately narrower than editorial or destination acceptance.

Run scripts/generate-media-fixtures when tests need deterministic non-personal media. Keep its generated binaries and manifest in the task workspace; commit the generator and assertions, not the outputs.

Use scripts/render-edl for a non-executing single- or multi-source plan. Default to decoded concat-filter assembly; select concat-demuxer stream copy only with matching probe-derived signatures and verified packet/keyframe boundaries. Unsupported transitions must remain explicit errors.

Use scripts/audio-inspect for bounded silence, EBU R128, peak/clipping, and transcript-alignment evidence. Request each measurement explicitly, preserve unavailable filters as UNAVAILABLE, and treat every interval or transcript range as a listening-review candidate. Its optional report output refuses overwrite.

Use scripts/media-verify with a declared acceptance contract, output FFprobe JSON, and optional evidence JSON. It reports every criterion independently as PASS, FAIL, BLOCKED, UNVERIFIED, or NOT_APPLICABLE; only a report with no failed or missing required evidence is an overall pass.

Use scripts/target-compatibility when acceptance names a real player, editor, host, archive, or service. Keep sourced technical requirements and local probe results separate from evidence produced by that exact consumer; a pass applies only to the named target/version.

Use scripts/vision-review-handoff to prepare bounded, privacy-safe frame packets for an authorized human or vision reviewer. Import only attributed reviewed observations with scripts/import-vision-review; treat proposed editorial consequences as evidence for review, never automatic decisions.

Copy a template into the task workspace and replace its placeholder/example values. Do not put private paths, media, transcripts, or review evidence in the public skill repository.

Non-Negotiable Checks

  • Make stream selection explicit whenever multiple inputs/tracks or a complex graph are involved.
  • Treat option order as significant: options generally apply to the next input or output.
  • Do not call silence useless; silencedetect reports threshold crossings, not editorial value.
  • Claim loudness, clipping, timing, or keyframe status only from an available measurement method and retain its output.
  • A transcript is evidence only for its text and supplied timing quality; spot-check alignment against media before frame-accurate edits.
  • Stop for review when evidence is sparse, ambiguity could remove meaningful content, an optional tool/filter is absent, privacy/authorization is unclear, or two materially different approaches fail.

Completion

Finish only when the requested artifact exists at a new path, required probes and bounded reviews are recorded, the output has been exercised at the relevant downstream boundary, and every acceptance criterion is passed or explicitly blocked. Report untested claims and remaining assumptions instead of filling gaps with plausible output.

© magnus919, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 47 other files (scripts, references) in ffmpeg of magnus919/agent-skills.

  • SKILL.md
  • README.md
  • evals/evals.json
  • references/advanced-operations-and-safety.md
  • references/audio-and-podcast-editing.md
  • references/command-cookbook.md
  • references/core-model-and-command-anatomy.md
  • references/editorial-video-editing.md
  • references/editorial-workflow-example.md
  • references/ffmpeg-edit-decision-lists.md
  • references/filters-and-transformations.md
  • references/intermediate-workflows.md
  • references/learning-summary.md
  • references/local-verification.md
  • references/media-failure-modes.md
  • references/media-intake-and-manifest.md
  • references/media-research-source-index.md
  • references/media-verification-and-acceptance.md
  • references/source-inventory.md
  • … and 29 more

Open the folder on GitHubat commit 96fbe07

Compare with similar skills

Ffmpeg next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Ffmpeg compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ffmpeg this skillmagnus919/agent-skills113—~3kAutomated safety check: PassMIT
HyperFrames Video Entry Pointheygen-com/hyperframes59k3 repos~5.2kAutomated safety check: PassApache-2.0
Black White Text OpenerPluviobyte/video-production-skills667—~1kAutomated safety check: PassNone
Super Video MakerBomx/super-video-maker-skill308—~11kAutomated safety check: NotesNone
Content To Videoarchitectds/modeldock117—~2.4kAutomated safety check: PassApache-2.0
KinocutKyaniteLabs/kinocut192—~5.7kAutomated safety check: PassApache-2.0

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Works with

Questions about Ffmpeg

What does Ffmpeg do?

A skill your agent uses for local FFmpeg/FFprobe media inspection, remuxing, transcoding, filtering, evidence-bounded video review, transcript-assisted editorial plans, edit decision lists…. Ffmpeg is an agent skill from magnus919/agent-skills. Use this skill for local FFmpeg/FFprobe media inspection, remuxing, transcoding, filtering, evidence-bounded video review, transcript-assisted editorial plans, edit decision lists, podcast/audio cleanup, rendering, and output acceptance.

When should I use Ffmpeg?

Ffmpeg fits situations like: local FFmpeg/FFprobe media inspection; evidence-bounded video review; transcript-assisted editorial plans; edit decision lists.

How do I install Ffmpeg in Claude Code?

Run `npx skills add magnus919/agent-skills --skill ffmpeg -a claude-code`. Or copy the skill folder (ffmpeg in magnus919/agent-skills) into .claude/skills/ffmpeg in your project. Claude Code loads it when a task matches its description.

How do I install Ffmpeg in Codex?

Run `npx skills add magnus919/agent-skills --skill ffmpeg -a codex`. Or copy the skill folder (ffmpeg in magnus919/agent-skills) into .agents/skills/ffmpeg in your project. Codex loads it when a task matches its description.

Can I use Ffmpeg in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add magnus919/agent-skills --skill ffmpeg -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ffmpeg, .gemini/skills/ffmpeg, .github/skills/ffmpeg and .opencode/skills/ffmpeg in your project.

What does Ffmpeg need to run?

Going by SKILL.md and its folder, Ffmpeg needs the command-line tools its instructions call (ffprobe). Compatibility (from SKILL.md): Requires ffmpeg and ffprobe for execution; exact filters, codecs, protocols, and hardware backends vary by build and version..

Does Ffmpeg access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Ffmpeg safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Ffmpeg use?

Ffmpeg is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ffmpeg use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 22k tokens, read only when the agent opens those files.

What are the alternatives to Ffmpeg?

Skills that share tags, products or a category with Ffmpeg: HyperFrames Video Entry Point (heygen-com/hyperframes, 59k stars), Black White Text Opener (Pluviobyte/video-production-skills, 667 stars), Super Video Maker (Bomx/super-video-maker-skill, 308 stars) and Content To Video (architectds/modeldock, 117 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ffmpeg?

magnus919 (a GitHub user) maintains it in magnus919/agent-skills, which has 113 GitHub stars. The repository holds 115 skills in this directory. The repository was last updated on October 6, 2026.

Source: magnus919/agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.