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概要と使いどころ
- Turn findings into a defensible intelligence product — BLUF key judgements, standardised estimative probability language, per-claim sourcing with timestamps and archived copies, separated observation, inference and assessment,… Use when writing an intelligence report, due-diligence memo, evidence pack or executive summary, or when asked to write up an investigation so it survives challenge
日本語の概要は準備中です。原文の説明を表示しています。
Researches Reddit using a brand's Brand DNA to find promotable pain-point discussions, target subreddits, and real user search language. Produces a prioritized Reddit opportunity list for content seeding, helpful replies, and prompt research inspiration.
日本語の概要は準備中です。原文の説明を表示しています。
Pick up and continue a serialized build-in-public book: read the current state (which days are written in which languages, what is uncommitted, where the gaps are), then keep writing through the established pipeline. Triggers: "/journey", "the book", "continue the journey".
日本語の概要は準備中です。原文の説明を表示しています。
Runtime release controls for deployless repositories. Use when a repo needs feature flags, kill switches, capability gates, targeted rollout, remote config, routing rules, or a provider-neutral release-control facade independent of language and provider.
日本語の概要は準備中です。原文の説明を表示しています。
Continuous production path for deployless repositories. Use after mainline governance and release controls exist, when a repo needs CI/CD changes to automatically deploy, publish, or promote artifacts from main across any language, runtime, or hosting platform.
日本語の概要は準備中です。原文の説明を表示しています。
Polish, restructure, or translate academic prose into Nature-leaning English using writing-strategy principles, curated Nature/Nature Communications article patterns, and phrase-level support from Academic Phrasebank. Use whenever the user asks to polish a manuscript paragraph, abstract, introduction, results, discussion, conclusion, title, methods section, or Chinese academic draft for publication-quality English. Also covers LaTeX layout/typesetting (排版) fixes — loose or sparse pages, stranded section headings, figures that don't fill the page or split across pages, "Float too large", multi-panel figure arrangement, and Supplementary Information that looks empty — via references/latex-layout.md. Also trigger on general academic/scientific writing requests even without the word "Nature", including academic writing, scientific writing, SCI/paper writing, English manuscript polishing, language editing, proofreading, and Chinese phrasings such as 学术写作、科研写作、论文润色、写paper、SCI写作、英文论文润色、语言润色、润色、改写、学术英语、英文写作.
日本語の概要は準備中です。原文の説明を表示しています。
Standard operating procedure for an auditable, headless MIKE+ workflow via the mike-plus MCP server — how to plan, copy, run, QA-gate, compare, plot, audit, and safely stop. Use FIRST whenever an agent is handed a MIKE+ model (.sqlite) or result (.res1d) plus a plain-language goal; it picks the operating mode and tells the agent which mike-* skill to open next and in what order.
日本語の概要は準備中です。原文の説明を表示しています。
Generate professional documents — PDF, Word (.docx), Excel (.xlsx), and invoices — from natural language descriptions or structured data. Covers AI-powered and simple document creation, HTML-to-PDF conversion, PDF extraction/OCR, merging, watermarking, and data export. Use when you need to create campaign decks, reports, proposals, invoices, spreadsheets, or any shareable document — especially for Step 7 monthly presentations, campaign briefs, and analytics reports.
日本語の概要は準備中です。原文の説明を表示しています。
Create programmatic videos using React and Remotion. Translate natural language descriptions into working Remotion components for product demos, release announcements, explainer videos, and animated content.
日本語の概要は準備中です。原文の説明を表示しています。
Generate production-quality Excalidraw architecture diagrams from natural language. Visual structure maps to conceptual structure — fan-out for one-to-many, timelines for sequences, convergence for aggregation. Includes self-validation via Playwright render review.
日本語の概要は準備中です。原文の説明を表示しています。
Automate recruiting and hiring workflows in Ashby -- manage candidates, jobs, applications, interviews, and notes through natural language commands.
日本語の概要は準備中です。原文の説明を表示しています。
Automate Attio CRM operations -- search records, query contacts and companies with advanced filters, manage notes, list attributes, and navigate your relationship data -- using natural language throug
日本語の概要は準備中です。原文の説明を表示しています。
Automate Apollo.io lead generation -- search organizations, discover contacts, enrich prospect data, manage contact stages, and build targeted outreach lists -- using natural language through the Comp
日本語の概要は準備中です。原文の説明を表示しています。
Generates client SDK code, API wrapper libraries, request/response models, and language-specific usage patterns for any REST API.
日本語の概要は準備中です。原文の説明を表示しています。
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
日本語の概要は準備中です。原文の説明を表示しています。
Build natural-language crypto/DeFi agents and EVM MCP plugins (Claude Code, Cursor, Codex, Gemini). Aomi turns prompts into wallet-signed txs on Ethereum, Base, Arbitrum, Optimism, Polygon, Linea —
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks to "codify how our founder sounds", "build a founder voice dossier", or "set our content pillars"; runs an 80%-extraction interview over the user's OWN posts, emails, and decks (never competitor scraping, never an invented persona) and produces the versioned voice record — a per-platform register map (incl. 小红书/微信公众号), banned phrases, per-context disclosure lines (the ECHO C2 upstream), a few-shot bank built exclusively from own posts, and 3-5 content pillars with Estimated %-allocations — submitted via memory/events/channels.ndjson for channel-registry to store as voice-dossier.md, the record every Craft-phase skill reads first. Not for the brand-level voice canon — use brand-language-codifier; not for audience/persona research — use audience-mapper. 声音档案/品牌语气/创始人语气/内容支柱/披露声明
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks to "build a story bank", "collect our origin and customer stories", or "assemble reusable proof stories for the message"; assembles reusable narrative units — origin, founder, customer, transformation, and proof stories — each tagged to a claims-ledger ID and a message-house pillar, with every proof labeled Measured / User-provided / [needs source]. Not for authoring the message house or pillars — use message-system-architect; not for brand voice or naming rules — use brand-language-codifier; not for finished long-form prose — use content-writer; not for adjudicating whether a proof is true — use offer-claims-registry. 品牌故事库/起源客户转化/证据故事单元
日本語の概要は準備中です。原文の説明を表示しています。
Build or update the shared product-marketing context: product, audience, positioning, competitors, jobs-to-be-done, objections, customer language, proof and goals. Use before downstream execution when context is missing.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks to "make everyone tell the same story", "write our elevator pitch ladder", or "build a spokesperson Q&A and approved boilerplate pack"; derives from the narrative-registry canon, message house, and brand voice a repeatable enablement kit — an elevator ladder (10-second / 30-second / 2-minute), a spokesperson Q&A (tough questions with on-canon answers), an approved boilerplate/bio pack (25 / 50 / 100-word), and a do/don't language sheet — so sales, support, founders, and partners repeat one consistent story. Not for the launch-day runbook — use launch-day-conductor; not for finished per-channel copy — use content-writer or each discipline creative builder; not for launch-window battle cards — use sales-enablement-kit; this kit never adjudicates a claim. 叙事赋能包/电梯梯度/发言人问答/审定样板/该说不该说
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks to "measure how our narrative is landing", "track echo rate against our canon lexicon", or "check how AI answer engines describe our brand"; produces a resonance report — echo rate (overlap of market language with the narrative-registry canon lexicon, method declared), AI-answer perception via tavily.py --answer (proxy-labeled), share-of-voice on a locked competitor panel (reusing share-of-voice-tracker), and resonance signals from bluesky.py / gdelt.py / pageviews.py — every number labeled Measured / proxy / User-provided, feeding the TALE E dimension and the upstream of the E1 evidence-integrity veto. Not for rebuilding share-of-voice machinery — use share-of-voice-tracker; not for own-site GA4/GSC analytics — use performance-monitor; not for scoring TALE profile result — use narrative-quality-auditor; not for adjudicating claims — use offer-claims-registry. 回声率/AI回答感知/份额之声/共鸣信号
日本語の概要は準備中です。原文の説明を表示しています。
Write or rewrite high-intent marketing copy grounded in product context, customer language, claims evidence, offer structure and conversion goals.
日本語の概要は準備中です。原文の説明を表示しています。
Plan, analyze and synthesize customer research into pains, jobs, triggers, objections, language, segments and evidence-backed marketing implications.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks to "codify our brand voice", "define naming rules for our products and tiers", or "write the tone-of-voice guide with banned phrases"; produces the brand-level voice canon (register, tone spectrum, banned-phrase list, few-shot examples drawn only from the brand's own published material) and the naming tax (product / feature / tier naming rules plus approved and banned terms) that seeds the narrative-registry canon and that every channel's voice adaptation points up to. Not for per-platform voice adaptation — use channel-registry's voice-dossier; not for finished copy or blog posts — use content-writer; not for the message hierarchy itself — use message-system-architect; not for claim adjudication — use offer-claims-registry. 品牌语气/词汇表/命名税/禁用词/品牌语言规范
日本語の概要は準備中です。原文の説明を表示しています。