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Generate atmospheric sound-design / SFX (NOT speech, NOT the front-of-blog BGM) via OpenRouter using Google Lyria 3 Pro.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | openrouter-text2music |
| description | Generate atmospheric sound-design / SFX (NOT speech, NOT the front-of-blog BGM) via OpenRouter using Google Lyria 3 Pro. |
Text → audio via OpenRouter. Default model: google/lyria-3-pro-preview.
Role in Data2Story: atmospheric sound-design / SFX only — never the front BGM. Use this for a sound the story needs that has NO findable real recording: an ambient bed (tension drone, texture, room tone) or best-effort sharp foley (e.g. a glass-shatter texture). Lyria's strength is ambient/textural beds, not crisp transients, so sharp foley is best-effort. The result is an opt-in, event/section-bound, reader-controlled element. The front-of-blog BGM must be a SOURCED real track (sourced_bgm via the Scout) — never generated here.
⚠️ This is a generative audio model, not TTS. It produces 48kHz stereo audio; for narration/voiceover use a dedicated TTS tool (e.g., OpenAI tts-1, ElevenLabs).
Resolve TOOL_DIR = the directory containing this SKILL.md. Commands below use TOOL_DIR as a symbolic placeholder; replace it with the resolved, quoted path before running Bash.
export OPENROUTER_API_KEY=sk-or-v1-...
python3 TOOL_DIR/scripts/generate_music.py \
--prompt "Low rumbling tension drone, sub-bass swell, no melody, slowly building unease" \
--download PROJECT_DIR/assets/sfx_drone.wav
Describe a TEXTURE, not a song — no genre/BPM/lead/vocals. Examples:
"low rumbling tension drone, sub-bass swell, no melody""sharp glass-shatter texture, brittle high transients" (foley — best-effort)"distant wind and creaking-ice ambience, cold and hollow"| Flag | Default | Description |
|---|---|---|
--prompt | required | Text prompt — describe the atmospheric texture / SFX (mood, materials, motion); avoid song structure |
--download | required | Output audio file path |
--model | google/lyria-3-pro-preview | Alt: google/lyria-3-clip-preview (shorter clips) |
lyria-3-pro-preview: $0.08 per callPOST /api/v1/chat/completions with modalities: ["audio","text"].message.audio, a content part with type=audio, or a data:audio/... URL embedded in the text content.Exhaustively profile a dataset and list ALL possible analyses — distributions, correlations, rankings, trends, group comparisons, anomalies. Reads detective.json for context. Runs after the Detective/Scout, before the Editor. Outputs analyst.json with ana_xx IDs and chart-ready data_tables.
Audit a generated Data2Story blog for build correctness across ALL modalities by ACTUALLY RENDERING it in a real headless browser (when available) — catching blank/0-width charts, broken/oversized media, desktop+mobile overflow, and dead interactions that source inspection cannot see — then having vision agents review the screenshots, playtesting every interactive, and walking the flagship capability contract. Plus VIEW every image for a wrong/AI-faked subject. Fixes layout in place; sends content-correctness problems back to the owning role. Use at Stage 6 after the Programmer assembles index.html and before the Critic; re-runs each revision round. Triggers: a built index.html exists, or you need to confirm charts/media/interactions actually render.
Design the MANDATORY cinematic scroll experience (every blog ships one): a full-bleed, scroll-driven background — primarily from the Scout's verified real imagery — plus the global motion choreography, so the narrative unfolds like a film as the reader scrolls. Owns the BACKGROUND layer + page-level motion (not the Designer's per-section visuals, not the Interaction centerpiece — it stages them inside the scroll). Outputs cinematographer.json (cin_xx scenes). There is no off path: a topic with no sourceable real imagery falls back to a generative-atmosphere or data-driven-spine background, never to a bare column.
Name the piece — re-write the masthead (headline + standfirst + kicker), every section title, and every figure/photo/table caption to a research-driven titling standard, killing the AI-tell patterns (the 'flat statement. flat counter-statement.' two-beat above all) a competent default falls into. Reads editor.md/json + analyst.json + the resolved topic_profile; writes copywriter.json — STRINGS ONLY (masthead{headline,standfirst,kicker}, items{edt_xx:{title}, des_xx:{caption}}), each backed by a real ana_*. Names, never edits: it touches no finding, no number, no data-* id, no layout — so the Verify layer is untouched and the Programmer renders the masthead + figcaptions from copywriter.json verbatim. Runs at Stage 3.5, after the Editor, before the Designer.
Review a finished Data2Story blog against the 5 quality rubric dimensions (visual_design, narrative_pacing, data_method_transparency, claim_data_alignment, insight_value), score each 1-7 with on-page evidence, and emit critic.json with pass/fail + targeted, surgical send-back instructions. Verifies every load-bearing claim/asset against its traceability chain before scoring; applies the caveat-survival, honest-accuracy, and third-party-attribution caps. Does NOT rewrite content — scores and sends back. Use at Stage 6.5 after the Auditor and after verify.py has produced verifier.json; re-runs each revision round. Triggers: a built index.html plus verifier.json exist, or you need to judge whether the article is actually good.
Generate text embeddings via OpenRouter using Qwen3-Embedding-8B.