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media-use

Agent Media OS, the single skill for every media need in a HyperFrames project. Resolve BGM, SFX, image, icon, brand logo, voice, color grade, or LUT into a frozen local file or paste-ready block + ledger record (one verb, `resolve`); generate via TTS / music / image models when the catalog misses; produce voiceover, transcription, captions, and background removal through one shared audio engine; operate on media (cut / reframe / transform); and reuse assets across projects. Also use for vague feedback that real footage looks dark, flat, boring, should feel retro/camcorder/print/ASCII, needs privacy, or needs a media reveal.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diogenesoftoronto/keating스타 36(GitHub 36 + cccskills 0)

실행 점수 미검증

설치 방법 보기

포함된 파일(152)

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  • .gitignore17 B
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SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

media-use

The media OS for HyperFrames: resolve · generate · operate · remember — every media type, one skill, zero context noise.

First run: install and sign in to the heygen CLI (the free-usage path), then verify with node <SKILL_DIR>/scripts/resolve.mjs --doctor. Setup and providers: references/setup-providers.md.

Resolve — the one verb

node <SKILL_DIR>/scripts/resolve.mjs --type <type> --intent "<description>" --project <dir>

Returns one line: resolved <id> → <path> (<type>, <metadata>). All search noise stays on disk.

TypeOne-line intent
bgmbackground music (HeyGen catalog, 10k+ tracks)
sfxsound effects (bundled 19-file library + catalog)
imagephotos, backgrounds (HeyGen asset search, 75k+ vectors)
iconicons, symbols (transparent)
logoofficial brand marks (svgl → simple-icons → GitHub avatar → favicon; never redrawn)
voiceTTS voiceover (HeyGen free-usage path; optional local Kokoro)
grademeasured correction candidate; broad polish/stylization follows Media Treatments
lutuser-provided or explicitly chosen reusable validated .cube file

Before resolving fresh, list reusable candidates with --candidates and judge fit yourself — reuse rules, all flags, ingest (--from), and adopt are in references/resolve.md.

Treat broad visual feedback as media intent

When a user explicitly asks to fix, polish, stylize, obscure, emphasize, or reveal photographic media, read references/media-treatments.md even if they do not name color grading or an effect. Inspect the real <img>/<video>, choose one primary intent, then use deterministic persistence and verification. Use a matching recipe as an optional tested seed, or inspect hyperframes media-treatment --capabilities --json, then request one relevant family/effect with --capability <id> and assemble a custom treatment from canonical controls. Never load --all for ordinary authoring. A treatment may compose correction, a preset, finishing, compatible shader effects, supported keyframes, and optional Registry overlays. Add only source-justified bounded tuning and compatible parts, never effects merely to make the result look more sophisticated. Persist the final combined payload with hyperframes media-treatment.

Use one progressively escalating workflow. For video, inspect one labeled early/middle/late contact sheet rather than reading frames separately. Apply one candidate and inspect one after-sheet for ordinary correction or polish. Escalate to individual frames or moving draft evidence only when the result is ambiguous, temporal, stylized, LUT-based, HDR/LOG-sensitive, private, or brand-critical.

For ordinary correction or polish, persist the final treatment's preset/adjustment JSON. Do not generate a .cube LUT merely to encode exposure, shadows, contrast, or warmth. Use a LUT only when the user supplies one or the selected treatment explicitly owns one. resolve --type grade --for ... --analyze is measurement evidence, not permission to replace the chosen treatment with a generated LUT. Do not recreate supported vignette, grain, blur, pixelate, color, or treatment effects with CSS/SVG overlays; that bypasses Studio controls and the canonical preview/render shader path.

Be proactive — run a media opportunity pass

The human usually can't tell which media would lift the piece. You can. When you build or review a composition, do one grounded scan and then ask once — don't silently add, and don't nag per asset.

Surface an opportunity only when a concrete signal is present:

Signal detectedOffer
On-screen text / a script with no voiceoverTTS voiceover (audio engine)
Emoji or a <div> styled as an iconresolve real icons
Image that is a placeholder, tiny, or upscaled-lookinga better image (and/or upscale — see references/operations.md)
Hard scene cuts / transitions with no soundtransition sfx
A piece over ~10s with no music bedbgm
Footage that reads under/over-exposed or color-casta corrective grade (inspect it with hyperframes media-treatment --selector '#hero' --analyze --json)
Photographic media that feels visually flat or off-topicone specific source-appropriate preset or custom treatment, with the intended target named
A meaningful media entrance/reveal that feels staticone supported seek-safe treatment animation; preserve color unless the request also justifies a preset

Rules that keep this a help, not nagware: grounded, not generic (no signal → no suggestion); opinionated + concrete (propose the specific fix with defaults chosen — the human approves all / some / none); once per project (one consolidated ask; respect "leave it"); surface, never silently mutate (color grades especially: propose and preview — a gray-world "correction" ruins an intentional sunset or neon look).

Where to look — read only the file your task needs

TaskRead
resolve / reuse / adopt / ingest, flags, cascade, inventoryreferences/resolve.md
color grading, LUTs, smart grade (--for), grade-comparereferences/grading.md
voiceover / TTS, music, SFX, captions, transcription (audio engine)references/audio.md
cut / reframe / transform existing media, exact error diffusion, HEVCreferences/operations.md
source-aware creative treatments, realtime effects, overlays, revealsreferences/media-treatments.md
install + auth, provider table, RAM ladders, --local-only, --providerreferences/setup-providers.md
remembered preferences + frozen recipes (user memory)references/memory.md
ownership matrix, usage stats, telemetry, privacy (maintainer-facing)references/meta.md

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같은 저장소의 스킬

Use when the user wants to learn, be taught, be quizzed, or understand an idea across any domain — science, math, philosophy, code, law, politics, psychology, medicine, arts, or history — with active scaffolding.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diogenesoftoronto/keating362026년 10월 20일 업데이트

ax

무료

This skill helps with using the @ax-llm/ax TypeScript library for building LLM applications. Use when the user asks about ax(), ai(), f(), s(), agent(), flow(), AxGen, AxAgent, AxFlow, signatures, streaming, or mentions @ax-llm/ax.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diogenesoftoronto/keating362026년 10월 20일 업데이트

ax-agent

무료

This skill helps an LLM generate correct AxAgent code using @ax-llm/ax. Use when the user asks about agent(), child agents, namespaced functions, discovery mode, shared fields, llmQuery(...), RLM code execution, recursionOptions, or agent runtime behavior. For tuning and eval with agent.optimize(...), use ax-agent-optimize.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diogenesoftoronto/keating362026년 10월 20일 업데이트

This skill helps an LLM generate correct AxAgent tuning and evaluation code using @ax-llm/ax. Use when the user asks about agent.optimize(...), judgeOptions, eval datasets, optimization targets, saved optimizedProgram artifacts, or recursive optimization guidance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diogenesoftoronto/keating362026년 10월 20일 업데이트

ax-ai

무료

This skill helps an LLM generate correct AI provider setup and configuration code using @ax-llm/ax. Use when the user asks about ai(), providers, models, presets, embeddings, extended thinking, context caching, or mentions OpenAI/Anthropic/Google/Azure/Groq/DeepSeek/Mistral/Cohere/Together/Ollama/HuggingFace/Reka/OpenRouter with @ax-llm/ax.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diogenesoftoronto/keating362026년 10월 20일 업데이트

ax-flow

무료

This skill helps an LLM generate correct AxFlow workflow code using @ax-llm/ax. Use when the user asks about flow(), AxFlow, workflow orchestration, parallel execution, DAG workflows, conditional routing, map/reduce patterns, or multi-node AI pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diogenesoftoronto/keating362026년 10월 20일 업데이트

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