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Creative media

Skill Skryx-L-A/project-kit/skills/creative-media

Build/set up a creative-media project — AI image/video/audio generation, a YouTube or ads channel, thumbnails, short-form, or brand visuals — from the user's grilled answers. Project-kit sub-skill loaded by new-project routing whenever someone wants to make videos, generate images, run a content channel, design thumbnails, produce ads, or build brand visuals. Chains higgsfield-generate (+ soul-id, product-photoshoot), design-harvest, pptx, and the virality predictor, and bakes in a repeatable produce→review→publish pipeline.From its SKILL.md

Install
npx -y skills add Skryx-L-A/project-kit --skill creative-media

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 1 command, including `ffmpeg`.

SKILL.md

7.5 KB, ~1.8k tokens by cl100k_base, as published. Nobody here has run it

creative-media — a repeatable produce→review→publish pipeline

What this sub-skill is for

Standing up a creative-media project: a repeatable pipeline that turns a concept into published assets — AI image/video/audio generation, a YouTube or short-form channel, an ads operation, thumbnails, or brand visuals. Loaded by new-project for any "make videos / generate images / run a channel / produce ads / design thumbnails / brand visuals" request. The whole skill is organized around one idea: build a pipeline (concept → generate → review → publish) that can run again, not just one lucky asset.

Mandatory grill-questions (fold into the Definition of Ready)

Lock these before generating anything:

  • Medium — image, video, audio, or mixed? Decides the generation model + the whole pipeline.
  • Platform + format/aspect — YouTube long-form (16:9), Shorts/Reels/TikTok (9:16), feed posts (1:1/4:5), display ads (set sizes)? Pin the exact aspect ratio(s) and duration now.
  • Brand / style — palette, typography, tone, references. Harvest tokens from references with design-harvest. Consistency across assets matters more than any single hero piece.
  • Volume / cadence — how many assets per week, and the publishing rhythm? The pipeline must sustain that cadence, not just produce one piece.
  • Identity consistency — recurring faces (use higgsfield-soul-id to train a Soul) or recurring products (use higgsfield-product-photoshoot)? Lock identity before batch-generating.
  • Edit / assembly flow — where raw generations get cut, captioned, scored, and exported (an NLE, a script, higgsfield's tools). No emojis in any on-screen text/overlays.
  • Publish + rights — who posts, where, and is every input (faces, music, product, stock) cleared for the intended use? AI-disclosure required on the target platform?
  • Success metric — what "this works" means: a published asset, a tested hook, a virality_predictor score over a bar, or a filled content calendar.

Project sub-agents to generate (into <project>/.claude/agents/)

  • concept-writer (delegate-by-default) — turns the brief into concepts, hooks, scripts, and generation prompts on-brand and on-format; keeps a backlog feeding the pipeline's cadence.
  • asset-generator (delegate-by-default) — runs the higgsfield generation: generate_image, generate_video, generate_audio, applying the trained Soul (--soul-id) or product-photoshoot mode for consistency, and organizing outputs by campaign/episode.
  • thumbnail-designer — designs thumbnails/covers/key art that read at small sizes, on-brand, emoji-free; chains design-harvest for reference patterns and the higgsfield image tools.
  • virality-checker (delegate-by-default for video) — runs virality_predictor (brain_activity) on cut videos for hook strength, attention, retention, and distraction risk; reports the score honestly and flags weak hooks before publish.
  • Plus the kit default reviewer (brand/format/rights review before publish).

Tools / CLIs / MCP / skills needed

Check in environment-readiness; offer install, never auto-install:

  • CHAIN the higgsfield MCP + skills as the generation core: higgsfield-generate (generate_image / generate_video / generate_audio, image-to-image, image-to-video, Marketing Studio for ads with avatars/products/hooks), higgsfield-soul-id (train a face for identity-consistent output), higgsfield-product-photoshoot (brand/product visuals), higgsfield-marketplace-cards (listing/product image sets), and virality_predictor for video. Use media_upload_widget / media_import_url for the user's own input media.
  • CHAIN design-harvest — pull palette/typography/spacing/component tokens from reference sites for the brand system and thumbnails. CHAIN framer-inspiration if the project ships a site.
  • CHAIN the pptx skill for pitch/brand decks and the magic MCP for any web UI/overlay templates. docx for scripts/treatments if wanted.
  • Editing/encoding — ffmpeg (cut/caption/encode/aspect-convert), an NLE if hand-edited. Verify ffmpeg is present. CHAIN global skills: deep-research (platform/format/trend facts), verify, code-review (if the pipeline is scripted), cli-anything (wrap a GUI editor like Shotcut/Inkscape headlessly).

File / asset nudges (on top of the base set)

Beyond CLAUDE.md, PROJEKT_<NAME>.md, TASKS.md, DONE.md, README, .claude/:

  • BRAND.md — palette, type, tone, do/don'ts, the trained Soul/product reference ids, and the emoji-free overlay rule. The consistency contract.
  • PIPELINE.md — the repeatable concept → generate → review → publish steps, who/what runs each, and the per-format spec (aspect, duration, safe areas).
  • CALENDAR.md — the publishing cadence + a content backlog.
  • assets/ split into raw/ (generations), edited/, published/, thumbnails/; large media .gitignored or Git-LFS.
  • assets/RIGHTS.md — provenance + license + usage-right + AI-disclosure status per input/output. Non-optional.

Stack defaults & done-bar

Default stack: higgsfield for generation (GPT Image 2-class for images/design/text, Seedance for video, generate_audio for sound; Soul for consistent faces, product-photoshoot for products), design-harvest for brand tokens, ffmpeg for assembly/encode/aspect, virality_predictor to score video, pptx for decks. Assets organized in assets/, big media kept out of git or in LFS. Done-bar (all true): a repeatable produce→review→publish pipeline is documented in PIPELINE.md and runnable again at the chosen cadence; the first real asset(s) are generated, on-brand, in the correct format/aspect, and emoji-free; identity consistency holds where required (Soul/product); for video, hooks pass the virality_predictor bar or weak ones are flagged; every input/output has a recorded right in assets/RIGHTS.md.

Guardrails

  • Disclose AI-generated media where the platform or law requires it; record disclosure status in RIGHTS.md. Don't pass synthetic media off as a real photograph where that would mislead.
  • Rights / licensing — every face, voice, song, product, and stock input must be cleared for the intended commercial use before it ships; record provenance per asset.
  • No deepfakes of real people without consent — only generate a real person's likeness/voice with their explicit permission (the Soul-ID identity flow is for consented faces only).
  • Honest performance — report virality_predictor scores straight; don't dress up a weak hook as strong, and don't claim reach/engagement you haven't measured.
  • No emojis in any on-screen text, overlay, thumbnail, caption, or UI (user's standing rule) — typographic symbols or designed glyphs only.
  • Never commit secrets / platform API keys / publish tokens.
  • Commits under the user's own name only (Skryx-L-A); never add Claude as a co-author.

Gives 0 of the 12 instructions most marketing audience skills give in ~1.8k tokens

Counted across 690 of the 894 authors here whose files we hold, read 2026-08-07

  • Apply Poppins font to headingsin 41 of 690, across 6 files
  • Apply Lora font to body textin 41 of 690, across 6 files
  • Use Arial fallback for headingsin 39 of 690, across 4 files
  • Use Georgia fallback for body textin 39 of 690, across 4 files
  • Maintain text hierarchy and formattingin 39 of 690, across 4 files
  • Use accent colors for non-text shapesin 38 of 690, across 3 files
  • Use RGB values for precise color matchingin 38 of 690, across 3 files
  • Use brand colors for primary text and backgroundsin 36 of 690, across 1 file
  • Read product marketing context file before asking questions, starting, or auditingin 35 of 690, across 23 files
  • Use active voice instead of passive voicein 26 of 690, across 10 files
  • Implement or generate appropriate JSON-LD structured datain 24 of 690, across 17 files
  • Prioritize clarity over clevernessin 22 of 690, across 8 files

Said here and by no other author read

  • lock grill-questions before generating any media
  • lock identity consistency before batch-generating assets
  • generate project sub-agents into the project directory
  • verify required tools before proceeding
  • offer to install missing tools never auto-install
  • create the required project files and asset folders

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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