Agent skill

AI Media

by ericrisco in ericrisco/rsc-harness

A skill your agent uses when a creative goal must become a finished media file: pick and order generative-media models per modality — AI voiceover, image-to-video clips, score — then glue them with…

MITAuto-check passedMedia & Creative

Install AI Media

skills CLI
$ npx skills add ericrisco/rsc-harness --skill ai-media -a claude-code

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

GitHub CLI
$ gh skill install ericrisco/rsc-harness ai-media --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/ericrisco/rsc-harness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-media .claude/skills/ai-media && 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
ai-media
GitHub stars
174
Token cost
~3.3k tokens
SKILL.md length
1,476 words
Files
6 (incl. scripts, references)
Skills in repo
233
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when a creative goal must become a finished media file: pick and order generative-media models per modality — AI voiceover, image-to-video clips, score — then glue them with…

  • Works in 11 steps: Lock the plan — scene list, aspect (e.g.… → Stills per scene →… → Clip per scene (img→video, ~5–15 s each,… → …
  • A creative goal must become a finished media file: pick and order generative-media models per modality — AI voiceover
  • SKILL.md covers Pipeline shape — decide what…, Modality 1 — Voice (TTS), Modality 2 — Image-to-video and Modality 3 — Music / score, plus 4 more sections
  • Runs Shell scripts from its folder; calls ffmpeg; needs ELEVENLABS_API_KEY

What it does

AI Media is an agent skill from ericrisco/rsc-harness. Use when a creative goal must become a finished media file: pick and order generative-media models per modality — AI voiceover, image-to-video clips, score — then glue them with ffmpeg (mux, duck, loudnorm, concat). NOT still-image generation/editing (that is replicate-images); NOT code-rendered React compositing (that is remotion-video).

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `evals/README.md`, `evals/cases.yaml` and `references/ffmpeg-assembly.md`).

It sits in Media & Creative, covering Video production, Text to speech and voice and AI video generation. It works with FFmpeg, Remotion and React. The repository describes itself as: Your agent invents things because it has no memory, and can't touch your database because it has no arms. rsc is the meta-harness that gives it both, plus the trade to know the… The licence is MIT.

When your agent uses it

  • A creative goal must become a finished media file: pick and order generative-media models per modality — AI voiceover
  • Image-to-video clips
  • Score — then glue them with ffmpeg (mux

Example prompts

  • “/ai-media”

Requirements

  • Python 3
  • A Bash shell
  • A credential in ELEVENLABS_API_KEY

Workflow steps

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

  1. Lock the plan — scene list, aspect (e.g. 16:9 1080p 30fps), target -14 LUFS, models chosen.
  2. Stills per scene → ../replicate-images/SKILL.md (one prompt per scene).
  3. Clip per scene (img→video, ~5–15 s each, model cap) via your chosen model on fal/replicate.
  4. VO → ElevenLabs convert(...) at the master sample rate.
  5. Music → ElevenLabs Music v2, length = total runtime, confirm rights.
  6. Conform every clip to 1920x1080/30fps/SAR 1.
  7. Concat the conformed clips → body.mp4.
  8. Loudnorm VO and music tracks (two-pass) to consistent levels.
  9. Mix + duck music under VO → mix.m4a.
  10. Final loudnorm the mix to -14 LUFS → master.m4a.
  11. Mux master.m4a onto body.mp4 with -shortest → final.mp4.

What it can do on your machine

Read from SKILL.md and the folder at commit e3d5b33. 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/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • ffmpeg

    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 these keys or tokens, usually read from environment variables:

    • ELEVENLABS_API_KEY

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

Context cost

AI Media loads about 3.3k tokens when it runs, and up to ~6.4k if it reads all its reference files. Until then it costs about 88 tokens; SKILL.md has 1,476 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.4k

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 ericrisco/rsc-harness at commit e3d5b33, republished under its MIT licence (© ericrisco). 1,476 words, ~3,290 tokens.

Download SKILL.mdSave it as .claude/skills/ai-media/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
ai-media
description
Use when a creative goal must become a finished media file: pick and order generative-media models per modality — AI voiceover, image-to-video clips, score — then glue them with ffmpeg (mux, duck, loudnorm, concat). NOT still-image generation/editing (that is `replicate-images`); NOT code-rendered React compositing (that is `remotion-video`).
tags
ai-media, text-to-speech, image-to-video, voiceover, music-generation, ffmpeg, media-pipeline, elevenlabs
recommends
replicate-images, fal, replicate, remotion-video, video-shorts
origin
risco

ai-media

You are the cross-modal director. You decide which generative-media model to call per modality, in what order, with what params, then assemble the pieces with ffmpeg into one finished file. You do not own a single provider's API surface and you do not prompt still images — you orchestrate and glue.

Pipeline shape — decide what the goal needs

Map the goal to modalities and an ordered step list, and lock that plan before you generate a single asset — media generation is slow and metered, so a re-roll of a 10 s Veo clip or a 90 s music track costs real money and minutes. Fixing the scene list, aspect ratio, target loudness and model per modality first is cheaper than discovering at mux time that your clips are 9:16 and your VO is the wrong sample rate. The "delegate to" column is where the actual call mechanics live — you pick the model and params, those skills run the call.

GoalNeedsOrdered stepsDelegate calls to
Narrated explainerstills + img→video + VO + musicscript → per-scene stills → clip per scene → VO → music → conform → concat → mix+duck → loudnorm → MP4replicate-images, fal/replicate
Product teaser (1 hero)1 still + img→video + musicstill → clip → music → mix → loudnorm → MP4replicate-images, fal/replicate
Faceless shortstills + img→video + VO + music + captions(explainer pipeline) + burn captionsreplicate-images; ../video-shorts/SKILL.md for the script
Just a voiceoverVO onlyscript → TTS → loudnorm—
Just a clip from a stillimg→video onlystill (input) → clipfal/replicate
Code-rendered explainernone of the aboverender from React/TSstop — route to remotion-video

If the video is rendered from data/code (charts, timelines, JSON-driven scenes), this is not your job → ../remotion-video/SKILL.md. You handle model-generated + ffmpeg-glued.

Modality 1 — Voice (TTS)

ElevenLabs Python SDK. The call is convert(text, voice_id, model_id, output_format); auth via ELEVENLABS_API_KEY.

python
from elevenlabs.client import ElevenLabs

client = ElevenLabs()  # reads ELEVENLABS_API_KEY
audio = client.text_to_speech.convert(
    text="Your narration script here.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_multilingual_v2",     # final-quality VO
    output_format="mp3_44100_128",          # codec_samplerate_bitrate
)
with open("vo.mp3", "wb") as f:
    for chunk in audio:
        f.write(chunk)

Pick the model tier by what the job needs:

ModelWhenLatencyCost lever
eleven_v3most expressive, hero final VO — verify availability first (see caveat)highermost credits/char
eleven_multilingual_v2high-quality multilingual VO (default for finals)mediummedium
eleven_flash_v2_5real-time / batch / scale / drafts~75 mscheapest

Do not hardcode eleven_v3 blind. It shipped to the API in alpha (Aug 2025) and the docs model list now carries it, but the text_to_speech.convert API reference still documents the default as eleven_multilingual_v2 and does not enumerate eleven_v3 as a guaranteed value. Before you build a final pass on it, confirm it returns from GET /v1/models for your key (or just call once and check) — otherwise default to eleven_multilingual_v2, which is the safe, always-available hero tier.

output_format is codec_samplerate_bitrate — e.g. mp3_44100_128, mp3_22050_32. Match the VO sample rate to your assembly target, do not master the VO loud and hope.

Bad → Good:

  • Bad: generate VO at mp3_22050_32, then mux onto a 48 kHz video — ffmpeg silently resamples, you get artifacts and a level mismatch.
  • Good: generate VO at the rate you will master at (e.g. mp3_44100_128), and set final loudness with loudnorm in assembly, not by cranking the TTS.

TTS is billed per character/token (~0.5–1 credit/char on the Flash/Turbo lines). ElevenLabs cut TTS API pricing up to 55% on 2026-05-07 (e.g. Flash on Creator $0.11→$0.05 / 1k tokens) — that figure is TTS-specific, not the Music cut. Pricing staling fast: these are point-in-time numbers from elevenlabs.io/pricing/api as of 2026-06-02 — re-check the page before quoting a budget. Shorter scripts and Flash on drafts are the cost levers.

Modality 2 — Image-to-video

The still is an input, not your output. Generate or edit the source image in ../replicate-images/SKILL.md, then animate it here. Reality check: every serious 2026 model does 1080p or native 4K — resolution is no longer the differentiating axis. The hard limit is per-generation duration (~5–15 s, model-dependent). Long pieces are one clip per scene, then concat — never one long take.

Durations below are from each vendor's own pages (as of 2026-06; see references/models-and-params.md for the citations) — they move with releases, so verify on the catalog before a final run:

ModelDurationAspect / max resControl surfaceNative audioOpen-source
Google Veo 3.18 s / generation16:9 / 9:16, up to 4Khighyes — synced 48 kHz dialogue/SFXno
Kling 3.0up to 15 sflexible, 4Kstrong identity/temporal, lip-syncnono
Runway Gen-4.52–10 sflexiblebest — motion brushes, camera control, reference imagenono
MiniMax Hailuo 026 s or 10 s (1080p caps at 6 s)up to 1080pmediumnono
Wan 2.6up to 15 sup to 1080pfirst/last-frame control, A/V syncno (sync)yes (Apache)

Choose by the binding constraint: need synced dialogue → Veo 3.1; need precise camera/motion control → Runway Gen-4.5; need identity consistency across scenes or the longest single take → Kling 3.0 / Wan 2.6; need open-source/self-host → Wan 2.6; cost-sensitive 1080p → Hailuo 02. Endpoint ids and per-call mechanics live in ../fal/SKILL.md / ../replicate/SKILL.md (both rails carry these models). See references/models-and-params.md for endpoint ids and current limits.

Modality 3 — Music / score

Costs are per-minute and plan-dependent — treat them as approximate and verify on the vendor pricing page (figures as of 2026-06; sources in references/models-and-params.md):

ModelCost (approx, verify)Licensing storyControl
ElevenLabs Music v2per-minute, ~$0.15–0.50/min depending on plan (Music API pricing cut up to 50% at v2 launch — separate from the 55% TTS cut)cleanest — vendor states trained only on licensed data, cleared for commercial use (Believe collaboration named at launch)genre-switch mid-track
Suno v5plan-basedusage rights on paid plans post Nov-2025 label settlements (rights, not ownership)vendor blind-test benchmark ELO ~1293
Udio$30/mo Pro plan (commercial rights); no official public API — third-party gateways onlyUMG-licensed platform announced for 2026—

Confirm commercial rights before you ship. Licensing differs per model and per plan; "I generated it" is not "I may sell the ad with it." For a clean commercial story with an official API, ElevenLabs Music v2 is the safe default — Udio has no first-party API, so do not plan a programmatic pipeline around it. The rest is the same fal/replicate call mechanics.

Show full SKILL.md (507 more words)Show less

Assembly with ffmpeg

Four operations. Each is a copy-paste recipe; full filter graphs, caption burning and pitfalls are in references/ffmpeg-assembly.md.

(a) Mux VO onto video — map both streams, copy video, take the shorter duration:

bash
ffmpeg -i scene.mp4 -i vo.mp3 \
  -map 0:v -map 1:a -c:v copy -shortest out.mp4

(b) Duck music under the VO — sidechaincompress keys the music off the voice so it drops when narration plays (pro DAW ducking, no manual keyframes):

bash
ffmpeg -i vo.mp3 -i music.mp3 -filter_complex \
  "[1:a][0:a]sidechaincompress=threshold=0.03:ratio=8:attack=20:release=300[duck]; \
   [0:a][duck]amix=inputs=2:duration=longest[aout]" \
  -map "[aout]" -c:a aac mix.m4a

Cheaper static fallback when sidechain is overkill — fix the music low under a full VO:

bash
ffmpeg -i vo.mp3 -i music.mp3 -filter_complex \
  "[1:a]volume=0.3[m];[0:a][m]amix=inputs=2:duration=longest[aout]" \
  -map "[aout]" mix.m4a

(c) Loudnorm to a target LUFS (two-pass) — measure, then apply. Target -14 LUFS for social/streaming, -16 for podcast-style VO. Normalize per track before mixing.

bash
# pass 1: measure (read the JSON it prints)
ffmpeg -i mix.m4a -af loudnorm=I=-14:TP=-1.5:LRA=11:print_format=json -f null -
# pass 2: apply with the measured values
ffmpeg -i mix.m4a -af \
  loudnorm=I=-14:TP=-1.5:LRA=11:measured_I=-20.1:measured_TP=-4.2:measured_LRA=6.0:measured_thresh=-30.8:offset=0.5:linear=true \
  master.m4a

(d) Concat scenes — conform first. Same-codec/res/fps clips → fast demuxer with -c copy. Mismatched clips → re-encode and scale first, then concat. Never -c copy-concat mismatched clips — you get desync or a corrupt stream.

bash
# all clips identical codec/res/fps:
printf "file '%s'\n" scene1.mp4 scene2.mp4 scene3.mp4 > list.txt
ffmpeg -f concat -safe 0 -i list.txt -c copy joined.mp4

# mismatched: conform each, then concat filter
ffmpeg -i s1.mp4 -i s2.mp4 -filter_complex \
  "[0:v]scale=1920:1080,fps=30,setsar=1[v0];[1:v]scale=1920:1080,fps=30,setsar=1[v1]; \
   [v0][1:a?][v1][1:a?]concat=n=2:v=1:a=0[v]" -map "[v]" joined.mp4

End-to-end worked pipeline (narrated explainer)

Ordered command list — generate once, assemble deterministically:

  1. Lock the plan — scene list, aspect (e.g. 16:9 1080p 30fps), target -14 LUFS, models chosen.
  2. Stills per scene → ../replicate-images/SKILL.md (one prompt per scene).
  3. Clip per scene (img→video, ~5–15 s each, model cap) via your chosen model on fal/replicate.
  4. VO → ElevenLabs convert(...) at the master sample rate.
  5. Music → ElevenLabs Music v2, length = total runtime, confirm rights.
  6. Conform every clip to 1920x1080/30fps/SAR 1.
  7. Concat the conformed clips → body.mp4.
  8. Loudnorm VO and music tracks (two-pass) to consistent levels.
  9. Mix + duck music under VO → mix.m4a.
  10. Final loudnorm the mix to -14 LUFS → master.m4a.
  11. Mux master.m4a onto body.mp4 with -shortest → final.mp4.

Emit this as a runnable script. scripts/verify.sh lints it (loudnorm present, conform-before-concat, final MP4 target).

Cost & regen discipline

  • Draft small, then final. Generate clips short/low-res and VO on Flash to lock timing and the cut; only the final pass spends on hero quality. Re-rolling locked scenes is the biggest waste.
  • Per-modality levers: shorter scripts (TTS per-char), fewer scene re-rolls (img→video), fewer music minutes (music is billed per minute — verify the current rate on the vendor pricing page).
  • Spend tracking as a discipline → ../fal/SKILL.md / ../replicate/SKILL.md for per-call cost; treat budget as a constraint you set before generating.

Anti-patterns

Anti-patternWhy it bitesDo instead
Generating assets before locking the pipelineaspect/sample-rate/duration mismatches surface at mux time, forcing paid re-rollslock scene list, aspect, LUFS, models first
One long video-gen call for the whole piecemodels cap at ~5–15 s; you fight the limit and waste rollsone clip per scene, then concat
Mixing tracks without per-track loudnormVO buried or blasting over music; inconsistent levelstwo-pass loudnorm each track before mix
-c copy-concat of mismatched clipsdesync, corrupt stream, wrong frame timingconform res/fps/SAR, then concat
Music low set by ear / static only when VO needs spacenarration gets masked under the bedsidechaincompress keyed off the VO
Shipping generated music without checking rights"generated" ≠ "licensed to sell" — legal exposureconfirm commercial rights per model/plan
Prompting/editing the still inside this skillduplicates replicate-images' job, worse promptsdelegate the still, consume it here
Mastering loudness by cranking the TTSclipping, no true-peak controlset level with loudnorm, not the generator

© ericrisco, 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 5 other files (scripts, references) in skills/ai-media of ericrisco/rsc-harness.

  • SKILL.md
  • evals/README.md
  • evals/cases.yaml
  • references/ffmpeg-assembly.md
  • references/models-and-params.md
  • scripts/verify.sh

Open the folder on GitHubat commit e3d5b33

Compare with similar skills

AI Media 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.

AI Media compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Media this skillericrisco/rsc-harness174—~3.3kAutomated safety check: PassMIT
Anything2explainerVincentwei1021/anything2explainer2.4k—~2.7kAutomated safety check: PassCustom licence
Super Video MakerBomx/super-video-maker-skill309—~11kAutomated safety check: NotesNone
Beatdesign WorkspaceBeatAPI/BeatDesign1321 repos~632Automated safety check: PassApache-2.0
Remotion Video Factorywwwzhouhui/skills_collection283—~990Automated safety check: PassNone
Remotion Best Practiceslyonjs/shortvid.io14733 repos~1kAutomated safety check: PassMIT

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Questions about AI Media

What does AI Media do?

A skill your agent uses when a creative goal must become a finished media file: pick and order generative-media models per modality — AI voiceover, image-to-video clips, score — then glue them with…. AI Media is an agent skill from ericrisco/rsc-harness. Use when a creative goal must become a finished media file: pick and order generative-media models per modality — AI voiceover, image-to-video clips, score — then glue them with ffmpeg (mux, duck, loudnorm, concat).

When should I use AI Media?

AI Media fits situations like: A creative goal must become a finished media file: pick and order generative-media models per modality — AI voiceover; image-to-video clips; score — then glue them with ffmpeg (mux.

How do I install AI Media in Claude Code?

Run `npx skills add ericrisco/rsc-harness --skill ai-media -a claude-code`. Or copy the skill folder (skills/ai-media in ericrisco/rsc-harness) into .claude/skills/ai-media in your project. Claude Code loads it when a task matches its description.

How do I install AI Media in Codex?

Run `npx skills add ericrisco/rsc-harness --skill ai-media -a codex`. Or copy the skill folder (skills/ai-media in ericrisco/rsc-harness) into .agents/skills/ai-media in your project. Codex loads it when a task matches its description.

Can I use AI Media 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 ericrisco/rsc-harness --skill ai-media -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-media, .gemini/skills/ai-media, .github/skills/ai-media and .opencode/skills/ai-media in your project.

What does AI Media need to run?

Going by SKILL.md and its folder, AI Media needs a shell for the scripts in its folder, the command-line tools its instructions call (ffmpeg) and credentials named ELEVENLABS_API_KEY. Our summary lists: Python 3; A Bash shell; A credential in ELEVENLABS_API_KEY.

Does AI Media 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 AI Media 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 AI Media use?

AI Media is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Media use?

About 3.3k tokens (SKILL.md is roughly 13k 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 3.1k tokens, read only when the agent opens those files.

What are the alternatives to AI Media?

Skills that share tags, products or a category with AI Media: Anything2explainer (Vincentwei1021/anything2explainer, 2.4k stars), Super Video Maker (Bomx/super-video-maker-skill, 309 stars), Beatdesign Workspace (BeatAPI/BeatDesign, 132 stars) and Remotion Video Factory (wwwzhouhui/skills_collection, 283 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Media?

ericrisco (a GitHub user) maintains it in ericrisco/rsc-harness, which has 174 GitHub stars. The repository holds 233 skills in this directory. The repository was last updated on October 7, 2026.

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