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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.

설치 방법 보기

포함된 파일(153)

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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

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括:AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**:本 skill 走批量 SQL · 离线大数据(PostgreSQL);v2 走实时 API · 单查询。两者互补:单条 NCT 实时状态用 v2,百万级历史挖掘用本 skill。支持云端公共 PostgreSQL(aact-db.ctti-clinicaltrials.org · 零部署)和每日 dump 本地还原(高性能 · 离线)两种连接方式,自动检测优先用本地。跨平台(macOS/Linux/Windows)参数化 SQL 防注入,read-only 强制保护。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

EthanYoQ/Skill-hub112026년 10월 19일 업데이트

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据库进行大批量临床试验历史分析与数据挖掘。触发场景包括:AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、clinical trials data warehouse、疾病领域全景分析、设计相似试验匹配、跨年度试验趋势聚合。本 skill 通过 SQL 接口处理百万级试验记录,支持云端公共 PostgreSQL 服务(aact-db.ctti-clinicaltrials.org)和每日 dump 本地还原两种连接方式,自动检测并优先使用本地高性能模式。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

EthanYoQ/Skill-hub112026년 10월 19일 업데이트

Use when work produces a deliverable, factual claim, dataset, code change, decision, publication, deployment, automation, or irreversible action whose failure would matter; when the user asks for a quality gate, acceptance criteria, QA, completeness, validation, audit, evidence, preflight, release readiness, or a definition of done; or before claiming completion on medium- or high-risk work. Automatically decide whether a formal gate is warranted, derive task-specific pass/fail criteria, gather evidence, and block unsupported completion. Make sure to use this skill even when the user does not say "quality gate" if consequential work needs acceptance criteria or completion evidence. Skip formal gating for trivial, reversible, low-impact requests.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

EthanYoQ/Skill-hub112026년 10월 19일 업데이트

Create a changeset file in .changeset/ that describes a user-facing change for the release notes

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

EthanYoQ/Skill-hub112026년 10월 19일 업데이트

Add a new competitor to the AI Visibility Tool Directory — researches the tool, generates data, takes a screenshot, and inserts into the codebase

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

EthanYoQ/Skill-hub112026년 10월 19일 업데이트

add-lang

무료

Add tree-sitter language support to codegraph end-to-end — wire the grammar + extractor, write tests, then benchmark extraction quality and retrieval value on 3 popular real-world repos. Use when the user runs /add-lang <language> or asks to add/support a new language (e.g. Lua, Elixir, Zig, OCaml) in codegraph.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

EthanYoQ/Skill-hub112026년 10월 19일 업데이트

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