「poi」の検索結果

Builds a product messaging library from your positioning, your website and customer evidence: a description, the status quo and alternatives, pain points with their knock-on consequences, capabilities, functional, emotional and social benefits, branded features, the cost of doing nothing, common objections with responses, and core messaging blocks (tagline, elevator pitch, value props, proof, per-audience lines). Every claim sourced. Triggers: "product messaging", "messaging library", "messaging framework", "value props", "capabilities and benefits", "what are our differentiators in words". Run after /positioning. Not for choosing the position itself (use /positioning).

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Builds an ideal customer profile from evidence: case studies, testimonials, customer logos, solutions pages, reviews and any sales notes you have. Produces market sizing layers, firmographic segments, a champion and an economic buyer persona, a negative ICP, intent signals, customer proof points and a voice-of-customer section in the buyers' own words, every claim sourced. Triggers: "ICP research", "ideal customer profile", "who are our customers", "customer segments", "buyer personas", "who should we target". Run after /company-context. Not for competitor analysis (use /competitor-research) or call transcript patterns (use /win-loss).

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Step-by-step execution of the OWC protocol with mandatory checkpoint at each step. Prevents skipping steps (including verification).

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IWE extensibility catalog: what can be customized, which extension points exist, which parameters are available, how to install a third-party extension.

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Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and best practices.

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Create or update root and nested AGENTS.md files that document scoped conventions, monorepo module maps, cross-domain workflows, and (optionally) per-module feature maps (feature -> paths, entrypoints, tests, docs). Use when the user asks for AGENTS.md, nested agent instructions, or a module/feature map.

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。

Guide plan → instrument → execute → validate with explicit checkpoints and questions. Use for ambiguous tasks or when enforcing a consistent agent workflow.

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 tailored resume from job + source resume. Orchestrates the rewriting of bullet points to emphasize relevant experience without fabrication.

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 a 10-component messaging library from your positioning + ICP + competitor aggregate. Produces positioning statement, value propositions, key differentiators, taglines, CTA variants, proof points, messaging hierarchy, status-quo alternatives. Writes to marketing/messaging/messaging.md as the canonical messaging library every downstream content / paid / outbound / lifecycle skill reads. Triggers - "product messaging", "messaging library", "value props", "messaging framework", "build messaging", "messaging components"

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Build and query a local Markdown knowledge base ("vault"). TWO functions — (1) CONVERT raw files (PDF, Word/docx, PowerPoint/pptx, Excel/xlsx, csv/tsv, images, html, md/txt, json/yaml/code, audio/video) into clean Markdown with retrieval-friendly frontmatter; local-first (pandoc / python-pptx / openpyxl / pymupdf4llm / whisper), with cloud OCR (MinerU) only as a fallback. (2) ANSWER questions over the resulting vault with retrieval discipline — self-monitor coverage, flag missing/lossy content, and propose Maps-of-Content (MOCs). Triggers: "build/sync my local knowledge base", "convert these files to markdown for AI", "整理我的资料库", "把文件转成 md 给 AI 读", "本地知识库", "读我的本地 vault 回答", "这个主题我的资料里怎么说". Not for: one-off web research, or files that are already in a single doc you can read directly.

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Use after an Anabasis v2 comparison, checkpoint or system change, and before any sentence claims that a harness, a model or a patch improved something. States what one reading may be said to show: the identities it was made under, the counts kept apart, whether it was repeated, which part of a harness it belongs to, whether a mechanism was live in the run, and whether the measurement is valid as distinct from whether it was exploited. Ends in one fixed report.

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Run SQL against Amazon Redshift — submit statements, poll status, page through results, and browse databases/schemas/tables. Use this whenever the user wants to query Redshift (provisioned cluster or Serverless), ask "what tables are in this schema", check a query's status, or mentions `redshift-data`, a Redshift cluster identifier / workgroup name, or a `redshift-data.{region}.amazonaws.com` endpoint. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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The branded entry point to ServiceGraph — use whenever the user explicitly names **ServiceGraph** — "use ServiceGraph to…", "what datasets does ServiceGraph have", "search ServiceGraph for…", "look this up in ServiceGraph", "pull contacts from ServiceGraph for these domains", "how many credits do I have on ServiceGraph". ServiceGraph is a multi-dataset platform of metrics-enriched business data for founders — where to launch, who to email, who to hire. This skill explains how to drive the API (api.servicegraph.co / mcp.servicegraph.co) against ANY dataset — discover what datasets exist, discover a dataset's schema and filters, search free brief rows, and unlock contact + metric detail with credits. Dataset-agnostic by design — it discovers everything through the API and never assumes which datasets or fields exist. When the user describes an intent WITHOUT naming ServiceGraph (e.g. "find a PR agency in NY"), defer to the matching specific skill (find-pr-agency, find-marketing-agency, find-law-firm, …); this skill is for explicit ServiceGraph requests and for datasets no specific skill covers yet. Skip non-US firms, consumer/personal services, and individual freelancers.

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。

Maintain portal TOTP and WebAuthn login checkpoints and enrollment, server-side challenges, factor-specific verification, replay prevention, and real TLS MFA journeys.

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Learn unfamiliar technologies used in a repo by studying how the repo actually uses them. Finds patterns, explains concepts in context, and points to examples in the codebase. Use when a repo uses frameworks, languages, or patterns you haven't worked with before. Not for mapping a repo's architecture or domain language. Use oss-explore-repo for that.

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。

Wrap a raster image in an SVG and overlay a numbered red "step" circle for course screenshots. Use when running /create-svg-image, or when asked to add a numbered callout badge to an image (e.g. a red circle with a "1" pointing at a UI control) that must still render on a GitHub README.

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 YouTube community-tab posts: text updates, polls, image-caption posts, and questions that drive comments and bring subscribers back between video uploads. Tuned to the community tab's role as a low-friction touchpoint that keeps a channel warm and feeds session signals. Returns a copy-paste block (community posts have no publishing API, so this is always manual by design). Use to write a community post or poll. Not for video titles or descriptions (use yt-title-optimizer and yt-description-writer).

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 a weekly YouTube upload plan: long-form vs YouTube Shorts mix, posting cadence, per-slot title and thumbnail pairing, hook angle, community-tab posts between uploads, and a goal balance across CTR, retention, and subscribes. Maps Shorts as top-of-funnel that points at long-form, and long-form as the loyalty and watch-time engine. Use to plan a week or a content calendar instead of shipping ad hoc. Not for writing a single title, description, or hook (use the dedicated yt skills).

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Turn a YouTube video idea into a designer-ready thumbnail brief: focal subject, face and emotion, a text overlay of 4 words or fewer that the title does not repeat, contrast and composition, and 2 to 3 A/B concepts for Test and Compare. Built on 2026 principles (one focal point, legible at 120 pixels, complements the title). Outputs the brief plus the Publora attach step (the image upload itself is out of band). Use to plan a thumbnail. Not for title words (use yt-title-optimizer).

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Draft replies to comments on your Facebook Page's posts, in your Page voice. Use when the user pastes the comments under one of their Page posts and wants on-brand replies that earn more comments and shares. Drafts 1-3 variants per comment using proven templates (answer, thank plus add value, handle the complaint, turn it into a share). Publora has no Facebook comment endpoint, so every draft returns as a copy-paste block to post yourself. Not for writing top-level posts (use fb-post-writer).

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Build and maintain a provenance-clean proof library — inventory every real proof asset (analytics, reviews, permitted testimonials, case studies, press, certifications), categorize it, rewrite each cleared item in 3 lengths, fuse top assets with narrative, and gate everything through FTC testimonial rules before it can ship. Use when "proof library", "gather our proof", "authority builder", "collect testimonials", "social proof assets", "proof points for the sales page", "what results can we claim", or any request to assemble evidence for marketing claims. NEVER invents proof — missing proof is reported as a gap.

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。

Audit any URL against 17-point technical SEO checklist + algorithmic authorship rules. Uses /browse if available, falls back to checklist-based audit.

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Plan and batch-produce an entire email system for a product. Maps every lifecycle touchpoint (onboarding, activation, conversion, retention, transactional, win-back), generates all emails with quality gates, outputs Loops-ready copy. Use when "create all emails", "email system", "build email sequences", "lifecycle emails", "onboarding sequence", "set up transactional emails", "plan all the emails for [product]", or any request to systematically produce a complete set of emails for a platform.

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Work with a running QDash instance through qdash-client and saved local profiles. Use when an agent needs to inspect chips, metrics, task results, calibration data, provenance, issues, flows, executions, project files, project-scoped QDash API data, or OpenAPI endpoints from QDash using qdash-client, ~/.config/qdash/config.ini, XDG_CONFIG_HOME/qdash/config.ini, QDASH_* environment variables, or a local oqtopus-team/qdash checkout.

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。