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概要と使いどころ
FLOW framework integration — evidence-led SEO using the Find → Leverage → Optimize → Win loop. Surfaces stage-specific AI prompts from the FLOW knowledge base (41 prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "seo flow", "evidence-led SEO", "find leverage optimize win", or wants stage-specific SEO prompts.
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。
FLOW framework integration for bloggers. Evidence-led content workflow using the Find, Optimize, Win loop with stage-specific AI prompts from the FLOW knowledge base (30 blog-applicable prompts). Use when user says "FLOW", "FLOW framework", "blog flow", "evidence-led blogging", "find optimize win", or wants stage-specific blog prompts.
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。
Suede Labs skill that finds and organizes the rights gaps in a creator project before packaging: ownership, contributors, splits, samples, licenses, provenance, metadata, licensing readiness, and royalty-routing readiness, each marked confirmed or unknown against an evidence trail. Use when a song, release, or creative project needs a rights check before registry, licensing, sync, or payout discussion; when splits, sample clearance, or chain of title are unclear; or when someone asks whether they have the rights to release, license, or get paid for a work. Organizes evidence only: clears no rights, confirms no ownership, moves no money, writes to no registry. NOT FOR: building the transfer package itself (use suede-rights-passport); linting a release folder's files and metadata (use suede-release-linter); a sync one-sheet or pitch (use suede-sync-packaging).
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。
Suede-owned customer-research discipline for interview design, transcript and ticket synthesis, review and forum mining, quote banks, jobs, and evidence-backed personas. Use when discovering or synthesizing what a defined customer segment actually says, does, needs, and resists. NOT FOR: competitor-only profiling (use suede-competitor-profiling), writing final marketing copy (use suede-copy), or deciding product priorities without product evidence (use suede-product-marketing).
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。
Suede AI blunt A-F ship grade for a code change across correctness, security and permissions, data and state, domain truth, UX and release behavior, tests and verification, and deploy readiness, with Instant-F triggers and evidence-based grade caps on auth, payment, migration, and public-API surfaces. Use when asked to grade this, give it a letter, is this an A, how ready is this to ship, or should this merge: when the caller wants the verdict without a findings list. NOT FOR: findings, evidence, and fix briefs (use suede-code-review, or suede-code for findings plus grade); enforcing the verdict in CI (use suede-ci-gate); eval coverage for AI behavior (use suede-ai-eval).
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。
Capture the before/after rendered PNGs a UI-affecting PR in this repository needs as visual evidence, including bringing an Android SDK up in a fresh sandbox. Use when a change touches a Compose @Preview, a catalog, the VS Code webview, an overlay, a theme, an icon or a fixture, and the PR body needs real pixels.
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, write, art-direct, generate, and package a coherent 2-10 image minimal-zine social series from notes, articles, photographs, travel stories, products, reviews, cultural topics, data, PDFs, or mixed subjects. Use for Xiaohongshu/小红书图片, Instagram carousels, social visual essays, recommendation posts, photo diaries, evidence-backed explainers, or any multi-image editorial sequence. Produces analysis, card roles, subject-aware composition, saved prompts, real raster images, natural caption copy, evidence provenance when needed, and series-level QA.
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。
Revenue-Centric Design (RCD) — evidence-backed principles for making a SaaS or startup product convert, retain, and monetize. Use when the user works on a landing page or CRO ("my page isn't converting"), onboarding/activation ("users sign up but don't stick"), churn/retention ("customers keep canceling"), pricing/monetization ("how should I price this"), checkout/forms ("dropdowns kill conversion", "annotated checkout"), dashboards/data viz ("cognitive load", "KPI shortlists"), positioning/ICP/go-to-market, feature scope, A/B-test rigor, or AI-era differentiation — or asks for the behavioral-science mechanism behind a design choice. Also use when another skill needs the principle or evidence behind a conversion/retention/pricing/checkout move. Never apply to gambling, betting, or casino products.
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。
Map evidence-based connections between a wellbeing initiative and specific academic learning outcomes. Use when justifying wellbeing programmes, integrating SEL, or linking pastoral and academic work.
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。
After scaffolded practice, run an unassisted check — a problem with no AI help. Separates what the learner can do with support from what they can do independently. Critical for preventing phantom attainment.
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。
Design a focused lesson observation protocol with specific look-fors and evidence collection methods. Use when planning peer observations, coaching visits, or developmental classroom visits.
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。
Orchestrate evidence-first engineering for WeChat and other mini programs from a vague idea or existing repository through project discovery, product specification, architecture, implementation, debugging, device adaptation, verification, and release readiness. Use when users ask to build a mini program from zero to one, take over an existing mini-program project, deliver a cross-stage feature, diagnose project status, coordinate development and testing, or judge whether a mini program is ready to upload or release. Enforces fact discovery, change boundaries, stage gates, evidence-calibrated status, sensitive-information redaction, and continuity after side questions.
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。
Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.
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。
Reconstruct an open-source project's growth channels, key content, and stage-by-stage attribution from GitHub history and public web evidence. Use this skill when asked to research GitHub star growth, identify launch and amplification channels, explain viral milestones, find representative posts or videos, compare Product Hunt/Show HN/Reddit/KOL impact, or produce an evidence-backed OSS growth attribution report.
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。
GPS-aligned genealogical research assistant for family history, ancestor research, record analysis, source-information-evidence classification, conflict resolution, citations, FAN research, privacy-sensitive document analysis, and proof statements, summaries, or arguments. Use for census, vital, probate, land, military, church, newspaper, DNA-plus-documentary, and other genealogy evidence tasks. Not for GEDCOM creation (use gedcom-creator), general history questions, DNA-kit shopping, or generic writing tasks.
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 wants web research: gathering cited, multi-angle evidence on a specific question. Triggers on: "research X for me", "do web research on", "look up sources for", "find citations for", "gather evidence on", "what does the web say about X". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), local file search, LLM-as-judge evaluation, or paywalled/auth-gated scraping. Produces a written plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.
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。
Act as a product-manager copilot that turns evidence into product decisions and artifacts: backlogs and prioritization, PRDs and user stories, metrics and OKRs, experiments, launch plans, feedback triage, and pricing/packaging, kept in a per-product workspace. Use whenever the user wants to decide what to build, spec it, or ship it: "write a PRD for…", "prioritize our backlog", "what should we build next", "plan OKRs", "define our north star metric", "design an A/B test", "plan the launch", "triage this customer feedback", "platform or feature?", in any language ("viết PRD", "ưu tiên tính năng nào", "quản lý sản phẩm", "プロダクト管理", "产品经理"), even when they never say "product manager". Also use when the user returns to a product workspace this skill created (a directory holding `product.md`, `state.md`, and `decisions.md`). Not for deep user discovery (design-thinking), desk market facts (market-researcher), or company-level bets (strategy-board).
Facilitate a full design-thinking engagement (Empathize, Define, Ideate, Prototype, Test) grounded in real user evidence. Use whenever the user wants to understand users deeply and design a solution for them: "run design thinking", "understand our users", "design user interviews", "synthesize these interview notes", "build personas", "write How-Might-We questions", "prototype this concept", "design a usability test", "our users are churning and we don't know why", in any language ("tư duy thiết kế", "nghiên cứu người dùng", "phỏng vấn khách hàng", "デザイン思考", "设计思维"), even when they never say "design thinking". Also use when the user drops raw interview notes, transcripts, or survey exports and wants insights, personas, or product decisions. Not for "is there a market for X" desk questions (market-researcher).
Analyze healthcare claims data for real-world evidence (RWE) studies. Use when working with medical/pharmacy claims (837/835), calculating utilization metrics, building patient cohorts, or analyzing treatment patterns. Triggers include claims data, RWE, real-world evidence, 837, 835, medical claims, pharmacy claims, utilization, treatment patterns, HEDIS, healthcare analytics.
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。
Link claim-side evidence objects to observation-side evidence objects, score support or contradiction heuristically, and persist a compact claim-observation link artifact for board review and challenge work.
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。
Primary and secondary source analysis for historical reasoning. Covers source classification, sourcing (author/context/purpose), corroboration across multiple sources, contextualization within time and place, and bias detection. Use when evaluating historical evidence, assessing source reliability, or constructing evidence-based historical arguments.
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。
Historical argumentation, evidence-based writing, and oral history methods. Covers thesis development for historical arguments, evidence selection and deployment, counterargument construction and rebuttal, historical writing conventions, and the methodology of oral history as both source and practice. Use when constructing historical arguments, writing analytical essays, conducting oral history interviews, or assessing the quality of historical writing.
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 you have captured session evidence — session-retro / session- observatory-live traces in .planning/patterns/, tool logs, correction records — and want to induce a reusable skill from it. Segments the traces into candidate skill units (an LLM judgment, not a deterministic parse) and decomposes each candidate into a four-part structured spec: workflow structure, execution semantics, and runtime attachments (verification, safety, rollback, state). It emits a spec object, NOT a finished SKILL.md, and hands that spec to skill-forge. It sits between skill-integration (upstream frequency detector) and skill-forge (downstream author). Backed by Agent-Trace-to-Skill Induction (arxiv 2606.06893v1). Triggers on inducing a skill from captured traces, turning a repeated pattern into a skill spec, and preparing evidence for skill-forge.
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。
Trace bugs, CI failures, and unexpected behavior from reproduced evidence to a causal mechanism and governing contract. Distinguish product, architecture, test-oracle, and harness defects; investigate intermittent failures with controlled experiments and report unresolved evidence explicitly.
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。