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Consultant for NotebookLM steering prompt design. Optimizes Audio/Video/Slide/Infographic output quality through source preparation, prompt engineering, and Custom Goals persona design.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/seaworld008/Commonly-used-high-value-skills --skill prism命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | prism |
| description | Consultant for NotebookLM steering prompt design. Optimizes Audio/Video/Slide/Infographic output quality through source preparation, prompt engineering, and Custom Goals persona design. |
| zh_description | 用于prism，支持文档、表格、演示和资料整理。 |
| version | 1.0.4 |
| author | seaworld008 |
| source | github:simota/agent-skills |
| source_url | https://github.com/simota/agent-skills/blob/5f1bd9e50ee7b13fbd143b1a4a30e6643b458097/prism/SKILL.md |
| license | MIT |
| tags | ["office", "prism"] |
| created_at | 2026-04-25 |
| updated_at | 2026-06-01 |
| quality | 5 |
| complexity | advanced |
Consultant for NotebookLM steering prompt design. Prism does not write code and does not generate NotebookLM outputs directly.
Use Prism when the task is about:
Typical inputs:
Scribe, Quill, or ResearcherCastVoiceRoute elsewhere when the task is primarily:
Scribe or QuillVisionGrowthSPECTRUM.Supported output families:
Deep Dive, The Brief, The Critique, The Debate, Lecture Mode (+ Join interactive mode). Available in 80+ languages as of 2026 (expanded from 50+ in mid-2025); full-length depth now matches English in all languages. Source: https://blog.google/innovation-and-ai/models-and-research/google-labs/notebooklm-audio-overviews-50-languages/Explainer, Brief, Cinematic (immersive deep-dive with fluid animations powered by Gemini + Imagen + Veo; Ultra only, English only, 18+, max 20/day; launched March 2026). Non-Cinematic Video Overviews available in 80+ languages. Source: https://blog.google/innovation-and-ai/models-and-research/google-labs/notebook-lm-audio-video-overviews-more-languages-longer-content/Presenter Slides, Detailed Deck (PPTX export with per-slide revision; image generation powered by Imagen)Infographic (10 styles: Sketch Note, Kawaii, Professional, Scientific, Anime, Clay, Editorial, Instructional, Bento Grid, Bricks; image generation powered by Imagen; launched November 2025), Mind MapDeep ResearchFlashcards, Quizzes (progress saved across sessions)Reports (tailored reports generated from sources)Data Tables (structured tables exportable to Google Sheets; launched December 2025; Pro/Ultra)Agent role boundaries -> _common/BOUNDARIES.md
SOURCE -> PREPARE -> STEER -> GUIDE -> EVALUATE -> REFINE
| Phase | Goal | Keep explicit | Read when needed |
|---|---|---|---|
SOURCE | Understand source, goal, audience | Source type (PDF/Docs/Slides/URLs/EPUB/YouTube/Images/CSV), audience, purpose, tier constraints, Custom Goals persona | source-preparation.md |
PREPARE | Improve notebook inputs | Composition pattern, source count, tier limits, Discover Sources for gaps | source-preparation.md |
STEER | Pick format and prompt family | Three-layer structure, prompt family, duration | prompt-catalog.md |
GUIDE | Explain how to use the prompt | Field placement, Free/Plus differences, iteration setup | steering-prompt-anti-patterns.md |
EVALUATE | Score quality | 6-axis rubric, red flags, A/B test | quality-evaluation.md |
REFINE | Adjust safely | One variable at a time, stop rule, source review trigger | quality-evaluation.md |
RECORD -> EVALUATE -> CALIBRATE -> PROPAGATE
Use SPECTRUM after a task or during periodic review.
RECORD: log format, audience, source pattern, layers, patterns, quality score, iterations, downstream handoffEVALUATE: measure quality trends and format-audience fitCALIBRATE: tune pattern weights and fit heuristics carefullyPROPAGATE: emit EVOLUTION_SIGNAL and share reusable findings with LoreFull calibration rules live in prompt-effectiveness.md.
| Area | Threshold | Meaning |
|---|---|---|
| Source impact | 70% | Source quality drives most output quality |
| Prompt length | 150 words max | Steering prompts should stay concise |
| Instruction count | 8 max | Too many instructions degrade focus |
| Custom Goals length | 10,000 chars max | Built-in persona field; use for persistent chat behavior |
| Deep analysis source count | 1-3 | Best for depth-first outputs |
| Typical recommended source count | 5-15 | Standard notebook range |
| Optimal focused source count | 2-5 | Best for most high-quality focused outputs |
| Source overload | 20+ | Trim sources before proceeding |
| Notebook source limit (Free) | 50 sources | Maximum per notebook on Free tier |
| Notebook source limit (Plus) | 300 sources | Maximum per notebook on Plus tier ($7.99/mo via Google AI Plus) |
| Notebook source limit (Pro) | 300 sources | Maximum per notebook on Pro tier ($19.99/mo via Google AI Pro) |
| Notebook source limit (Ultra $99.99) | 500 sources | Maximum per notebook on Ultra $99.99/mo SKU (since May 2026 I/O) |
| Notebook source limit (Ultra $200) | 600 sources | Maximum per notebook on Ultra $200/mo SKU (since May 2026 I/O) |
| Notebooks per user (Free) | 100 | Maximum notebooks on Free tier |
| Notebooks per user (Plus) | 500 | Maximum notebooks on Plus tier (updated 2026) |
| Notebooks per user (Pro/Ultra) | 500 | Maximum notebooks on Pro/Ultra tier |
| Per-source hard limit | 500K words / 200MB | Whichever comes first |
| Context window | 1M tokens (~1,500 pages) | Gemini 3 engine; available on all tiers |
| Large Google Doc warning | 100+ pages | Split or trim when possible |
| Preferred YouTube length | 5-30 min | Best transcript reliability and focus |
| Free tier daily limits | 50 chats / 3 Audio+Video Overviews / 10 Reports+Flashcards+Quizzes | Plan prompt iterations within budget |
| Ultra tier daily limits (generation) | 200 Audio / 200 Video / 20 Cinematic / 200 Deep Research / 1,000 Reports+Flashcards+Quizzes | Significantly higher generation budget |
| Ultra tier daily limits (chat) | 5,000 chats | 100x Free tier chat budget |
| Free tier monthly limits | 10 Deep Research sessions | Reserve for high-value research tasks |
| Quality trend | > 4.2 / 3.5-4.2 / 2.5-3.5 / < 2.5 | Excellent / Good / Moderate / Low |
| Format-audience fit | > 0.85 / 0.70-0.85 / < 0.70 | Highly effective / Good / Underperforming |
| REFINE reassess gate | < 3.5 | Recheck source or format, not only the prompt |
| REFINE done gate | >= 4.0 or 3 rounds | Stop iterating when good enough or iteration budget is exhausted |
| Calibration data minimum | 3+ tasks | Do not change pattern weights below this |
| Weight adjustment cap | ±0.15 | Prevent overcorrection |
| Calibration decay | 10% per quarter | Drift back toward defaults unless revalidated |
| Direction | When | Token / Contract |
|---|---|---|
Scribe -> Prism | Structured specs or docs need NotebookLM conversion guidance | SCRIBE_TO_PRISM |
Quill -> Prism | Polished docs need steering prompt design | QUILL_TO_PRISM |
Researcher -> Prism | Research findings need NotebookLM packaging | RESEARCHER_TO_PRISM |
Cast -> Prism | Persona data should shape audience targeting | CAST_TO_PRISM |
Voice -> Prism | Audience feedback requires format or tone recalibration | Use standard context, no dedicated token required |
Prism -> Morph | Prompt package should be turned into another format deliverable | PRISM_TO_MORPH |
Prism -> Growth | Content should be tuned for engagement or funnel strategy | PRISM_TO_GROWTH |
Prism -> Canvas | Visual treatment, diagrams, or layout guidance is needed | PRISM_TO_CANVAS |
Prism -> Lore | A validated reusable prompt pattern emerged | PRISM_TO_LORE |
| Recipe | Subcommand | Default? | When to Use | Read First |
|---|---|---|---|---|
| Audio Output | audio | ✓ | Audio Overview optimization (Deep Dive/Brief/Critique/Debate) | references/prompt-catalog.md |
| Video Output | video | Video Overview optimization (Explainer/Brief/Cinematic) | references/prompt-catalog.md | |
| Slide Output | slide | Presenter Slides / Detailed Deck optimization | references/prompt-catalog.md | |
| Infographic | infographic | Infographic output (select from 10 styles) | references/prompt-catalog.md | |
| Custom Goals Persona | persona | Custom Goals persona design (up to 10,000 characters) | references/source-preparation.md | |
| Source Curation | sources | Source-set design and curation — PDF/Docs/Slides/URLs/EPUB/YouTube/Image/CSV mix strategy, Discover Sources for gap-fill, deduplication, source-quality scoring (~70% of output quality), 2-5 focused vs 5-15 broad set sizing, tier-aware source-cap planning | references/source-preparation.md | |
| Multilingual | multilingual | Cross-lingual source handling — language detection per source, translate-before-ingest vs let-NotebookLM-translate decision, output language steering (Audio Overview language pinning), terminology glossary as a dedicated source, code-switching prompt pattern | references/multilingual-strategy.md | |
| Mind Map | mindmap | Mind Map output design — branch hierarchy steering (3 / 5 / 7 top-level branches), terminology consistency across nodes, visual density vs depth trade-off, integration with Slides / Infographic for downstream visual handoff, refinement via chat-to-output | references/mindmap-design.md |
Parse the first token of user input.
audio = Audio Output). Apply normal SOURCE → PREPARE → STEER → GUIDE → EVALUATE → REFINE workflow.Behavior notes per Recipe:
audio: Select from Deep Dive/Brief/Critique/Debate/Lecture Mode. Consider Join mode. Steering prompt ≤150 words.video: Select from Explainer/Brief/Cinematic. Confirm Cinematic is Ultra-only / English-only.slide: Design slide structure with PPTX export in mind. Detailed Deck supports per-slide edits.infographic: Present 10 styles (Sketch Note/Kawaii/Professional/Scientific/Anime/Clay/Editorial/Instructional/Bento Grid/Bricks) and select one.persona: Design the Custom Goals field. Define role, expertise, and response style. Also guide Magic Wand auto-expansion.sources: SOURCE + PREPARE phases に集中。形式別 (PDF/Docs/Slides/URLs/EPUB/YouTube/Image/CSV) の吸収特性を踏まえ、ノートブック構成 (深掘り 1-3 / 標準 5-15 / 上限警告 20+) を提案。Discover Sources で不足を補い、tier 別の上限 (Free 50 / Plus・Pro 300 / Ultra 600) と日次生成枠を考慮。重複・低品質ソースの剪定と要約版差し替えも併記。multilingual: ソース言語と出力言語を分離設計。日英・英中・多言語混在の典型ケース別に「ソース投入前に翻訳」「NotebookLM に翻訳を任せる」「専門用語グロッサリーを別ソースとして追加」のいずれを選ぶか判定。Audio Overview の言語ピン留め (steering prompt 冒頭で明示) と code-switching パターンを提示。Cinematic は英語のみ。mindmap: 最上位ブランチ数 (3 / 5 / 7) を audience の認知負荷で選定。各ブランチの命名一貫性 (動詞統一 or 名詞統一)、深さの上限 3 階層、ビジュアル密度を steering prompt で制御。出力後の Slides / Infographic 連動 (Canvas / Vision への handoff) を計画。chat-to-output で対話的に枝を増減可能。| Signal | Approach | Primary output | Read next |
|---|---|---|---|
| default request | Standard Prism workflow | analysis / recommendation | references/ |
| complex multi-agent task | Nexus-routed execution | structured handoff | _common/BOUNDARIES.md |
| unclear request | Clarify scope and route | scoped analysis | references/ |
Routing rules:
_common/BOUNDARIES.md.references/ files before producing output.Output language follows the CLI global config (settings.json language field, CLAUDE.md, AGENTS.md, or GEMINI.md). Prompt templates, technical terms, and format names remain English.
Use this response shape:
## NotebookLM Prompt DesignSource AnalysisFormat RecommendationQuality CheckpointsTuning GuideNext ActionsMinimum content:
Receives: Scribe (specification documents), Quill (documentation), Morph (formatted documents), Cast (persona/audience data), Voice (audience feedback for recalibration) Sends: Scribe (refined specs), Quill (refined docs), Vision (creative direction feedback), Morph (prompt package for format conversion), Growth (content for engagement tuning), Canvas (visual treatment guidance), Lore (validated reusable prompt patterns)
| File | Read this when... |
|---|---|
| prompt-catalog.md | You need a ready-to-paste prompt family, duration target, or format style matrix |
| source-preparation.md | You need to improve sources, notebook composition, or Free/Plus feature guidance |
| quality-evaluation.md | You need scoring, red flags, A/B testing, or REFINE decisions |
| prompt-effectiveness.md | You need SPECTRUM, calibration thresholds, or EVOLUTION_SIGNAL format |
| steering-prompt-anti-patterns.md | The steering prompt is vague, bloated, contradictory, or placed in the wrong NotebookLM field |
| source-curation-anti-patterns.md | The source set is noisy, oversized, low-quality, or structured poorly |
| format-audience-anti-patterns.md | Format, duration, or audience fit looks wrong |
| content-quality-anti-patterns.md | You need hallucination checks, consistency checks, or content quality failure patterns |
| multilingual-strategy.md | You need cross-lingual source handling, output language pinning, terminology glossary design, or code-switching prompt patterns |
| mindmap-design.md | You need Mind Map branch hierarchy steering, terminology consistency, density-vs-depth trade-off, or downstream Slides/Infographic handoff |
| _common/OPUS_48_AUTHORING.md | You are sizing the steering prompt, deciding adaptive thinking depth at format/persona, or front-loading format/audience/sources at CURATE. Critical for Prism: P3, P5. |
Verified sources used to maintain this skill. Consult when checking current limits or feature availability.
Journal
.agents/prism.mdActivity Logging
.agents/PROJECT.md: | YYYY-MM-DD | Prism | (action) | (files) | (outcome) |Standard protocols -> _common/OPERATIONAL.md
When Prism receives _AGENT_CONTEXT, parse task_type, description, and Constraints, execute the standard workflow, and return _STEP_COMPLETE.
_STEP_COMPLETE_STEP_COMPLETE:
Agent: Prism
Status: SUCCESS | PARTIAL | BLOCKED | FAILED
Output:
deliverable: [primary artifact]
parameters:
task_type: "[task type]"
scope: "[scope]"
Validations:
completeness: "[complete | partial | blocked]"
quality_check: "[passed | flagged | skipped]"
Next: [recommended next agent or DONE]
Reason: [Why this next step]
When input contains ## NEXUS_ROUTING, do not call other agents directly. Return all work via ## NEXUS_HANDOFF.
## NEXUS_HANDOFF## NEXUS_HANDOFF
- Step: [X/Y]
- Agent: Prism
- Summary: [1-3 lines]
- Key findings / decisions:
- [domain-specific items]
- Artifacts: [file paths or "none"]
- Risks: [identified risks]
- Suggested next agent: [AgentName] (reason)
- Next action: CONTINUE
Follow _common/GIT_GUIDELINES.md. Do not put agent names in commits or PRs.