5.2万 件 ・ 関連度順
概要と使いどころ
Expert pricing strategy and monetization guidance for SaaS and digital products. Use when designing pricing tiers, creating pricing pages, setting value metrics, developing bundle strategies, planning price increases, writing pricing copy, analyzing competitive pricing, or optimizing annual vs monthly positioning. Use for packaging decisions, discount policies, enterprise pricing, and behavioral pricing tactics.
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。
Detects seven structural anti-patterns in Claude Projects that cause unpredictable output, ignored instructions, and degraded quality. Diagnoses Monolith, Orphan File, Echo Chamber, Phantom Conversation, Kitchen Sink, Misaligned Hierarchy, and Blurred Layers. Use when user says "what's wrong with my project," "Claude ignores my instructions," "diagnose my project," "why is output inconsistent," "review my project setup." Also trigger on symptom-phrased: "Claude doesn't follow my rules," "my instructions keep getting overridden," "my Project isn't behaving as designed." Use alongside rootnode-project-audit if available for deeper structural analysis. Activate whenever the user describes symptoms of unreliable, inconsistent, or degraded Claude Project output, even if they do not name a specific pattern. Do NOT use when the user's primary request is Memory-layer rebalancing (use rootnode-memory-optimization if available) or scoring a single prompt (use rootnode-prompt-validation if available).
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。
Eight tested identity approaches for Claude prompts, each shaping depth, vocabulary, reasoning style, and what Claude treats as obvious vs. requiring explanation. Use when the user wants a specific identity template — retrieving, reviewing, customizing, or building a role definition. Trigger on: "give me the Strategic Advisor identity," "show me the identity template for," "I need a Technical Architect role," "build a custom role for," "show me all available identities." Also use when reviewing a prompt's identity layer or when output lacks domain-appropriate depth. Provides 8 tested approaches across strategy, technical, research, communications, and operations domains plus a template for building custom identities. If the user is unsure which identity fits their task, use rootnode-block-selection first if available. Do NOT use for evaluating complete prompts — use rootnode-prompt-validation if available. Do NOT use for full prompt assembly — use rootnode-prompt-compilation if available.
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。
Diagnoses and fixes Claude behavioral issues in system prompts and Projects using tested countermeasure templates. Use when "Claude is too verbose," "keeps hedging," "agrees with everything," "claims it did something it didn't," "won't use a tool," "fix Claude's output," "tune Claude's behavior," "Claude ignores my preferences," "adds unsolicited disclaimers," "uses too many lists." Covers ten tendencies: agreeableness (output-content + persistent-preference), hedging, verbosity, list overuse, fabricated precision, over-exploration, tool miscalibration (over- and under-triggering), LaTeX defaulting, editorial drift, self-referential fabrication. Also use when auditing a system prompt for behavioral calibration or recalibrating a pre-4.7 prompt, and when users describe recurring output problems without naming a tendency. Do NOT use for scoring a prompt's overall quality — use rootnode-prompt-validation if available.
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。
Selects the active profile from triggering context. Evaluates a rule set against inputs (current time, calendar state, geofence, manual command, custom signals) and returns the profile name that consuming Skills (handoff-trigger-check, critic-gate, future Skills) should apply. Configuration-driven; rule set lives in a router config file conforming to the included schema. Deterministic precedence — manual override beats calendar beats time-window beats default. Required default profile guarantees a routing decision always exists. Use when an orchestrator needs to pick a profile automatically rather than having the caller specify one, when wiring calendar/time/Telegram triggers to gate behavior, or when a Skill's behavior should change with availability mode. Trigger on: "what profile should I use," "route to a profile," "which mode am I in," "auto-select profile," "active mode," "current profile." Do NOT use to evaluate work readiness or review proposed changes. Always returns a profile name.
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。
プロダクトの基盤情報（課題仮説・ソリューション・市場スコープ・原体験・ビジネスモデル・競合・差別化）を ユーザーとの対話で掘り下げ、構造化されたドキュメント（product-context.md）として整理するスキル。 「自分のプロダクトって結局何なの？」を一緒に整理する壁打ち相手として機能する。 ペルソナ作成、LP設計、機能優先順位、マーケティング戦略など、 プロダクトの方向性に関わるあらゆる意思決定の「前提情報」を1ファイルにまとめる。 コードベースがあれば自動で読み取れる部分は埋め、残りをユーザーにヒアリングする。 Use when: 「プロダクトを整理したい」「ターゲット整理」「コンテキスト作成」 「ペルソナの前に」「誰向けのプロダクトか整理」「プロダクト情報まとめ」 「product context」「プロダクト定義」「プロダクトの前提」 「product discovery」「誰向け」と言われた時。 新しいプロジェクトの立ち上げ時や、方向性を見直したい時にも使う。 ペルソナ作成スキルを使う前に、まずこのスキルでプロダクトの前提を整理すべき。 Triggers: "product discovery", "product context", "プロダクト整理", "ターゲット整理", "コンテキスト作成", "プロダクト定義", "プロダクトの前提", "ペルソナの前に", "誰向け", "プロダクト情報まとめ", "プロダクトって何", "何を作ってるか整理", "どんな需要があるか"
Plan a sprint by refining backlog items, defining a sprint goal, calculating team capacity, selecting items, and decomposing them into tasks. Produces a SPRINT-PLAN.md with goal, selected items, task breakdown, and capacity allocation. Use when starting a new sprint in a Scrum or agile project, re-planning after significant scope change, transitioning from ad-hoc work to structured sprint cadence, or after backlog grooming when items are ready for inclusion.
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。
End-to-end meeting workflow skill. Pre-meeting: research attendees, build agenda, prepare talking points, create pre-read. Post-meeting: process notes into action items, draft follow-up emails, log decisions, update project notes. Useful for preparing for important calls, during post-meeting processing, when following up with attendees, and for building a running decision log. Use when the user says "prepare for meeting", "meeting prep", "process meeting notes", "meeting follow up", "meeting machine", or "prep for my call".
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 Spring Boot 4.x projects for correct dependencies, configuration, and migration readiness. Use when analyzing pom.xml, build.gradle, application.yml, discussing Spring Boot project setup, dependency versions, configuration validation, version compatibility, migration to Spring Boot 4, deprecated dependencies, or when user mentions "verify project", "check dependencies", "upgrade Spring Boot", "migration readiness", "Jackson 3", "@MockBean deprecated", or "Spring Security 7".
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。
Smart code scanner that detects Spring Boot patterns and routes to appropriate skills. Use when editing Java or Kotlin files in Spring Boot projects, working with pom.xml/build.gradle containing spring-boot-starter, or when context suggests Spring Boot development. Detects annotations (@RestController, @Entity, @EnableWebSecurity, @SpringBootTest) to determine relevant skills and provides contextual guidance. Uses progressive automation - auto-invokes for low-risk patterns (web-api, data, DDD), confirms before loading high-risk skills (security, testing, verify).
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 an existing Lamport-style hierarchical mathematical proof forward for validity and whether its hierarchy, scope, dependencies, side conditions, and proof constructs discharge the stated theorem. Use when the central question concerns a top-down proof with numbered levels, subproofs, ASSUME/PROVE, SUFFICES, PICK, CASE, DEFINE, Q.E.D., or prose equivalents. For an ordinary proof that first needs source-preserving hierarchy, use convert-lamport; for conclusion-first AND/OR obligation reconstruction, use reverse-lamport. Do not use to generate or replace a proof, or to audit Lean, Coq, Isabelle, or other proof-assistant source.
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。
Make game-ready 2D characters, creatures, props, icons and FX as master stills, sprite sheets or animation clips. Plan size, motion and finish; generate the art (image generation is the default, through generate2dmedia route_media.py - the API when a key is configured, else the local Codex or Grok CLI); approve one master still per character; then key, slice, register, scale, palette, QA and export frames for a game engine (Aseprite JSON, Godot SpriteFrames or Sprite3D). HD finish by default, pixel finish on request. Character animation is one image-to-video clip per action from the master (video2dsprite); sheets are for FX, icons and props. Use for sprites, masters, sheets, frame-by-frame animation and packaging frames from any source. Not for maps, tiles or backgrounds (generate2dmap), video clips (video2dsprite), code-drawn art (codeart2d, only on request) or calling an image or video API by itself (generate2dmedia).
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。
Email consent management in Marketing Cloud: configuring Subscription Centers, building Preference Centers on CloudPages, enforcing CAN-SPAM and GDPR requirements, and handling opt-out propagation. Trigger keywords: unsubscribe handling, CAN-SPAM compliance, GDPR consent tracking, publication lists, subscription center, preference center, opt-out, double opt-in, Privacy Center, MC consent. NOT for subscriber key and suppression lists — use data/subscriber-data-management. NOT for the CRM consent data model or MC-to-CRM sync — use architect/marketing-consent-architecture.
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。
Manage Spec Kit presets via `specify preset`. USE FOR: listing installed presets as text or JSON, installing/removing presets, searching the preset catalog, showing preset info, resolving which template a preset name maps to, enabling/disabling presets, setting resolution priority, or managing preset catalogs. DO NOT USE FOR: full artifact composition stacks (use speckit-artifact), extensions (use speckit-extension), bundles (use speckit-bundle), or workflows (use speckit-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。
Generate advertising images automatically from a product URL + brand profile. ✅ USE WHEN: - User provides a product URL (e-commerce link) - Want automated product scraping + image generation - Have a brand profile to apply (70+ brands available) - Need funnel-stage targeting (awareness/consideration/conversion) - Want AI to auto-select model, scene, lighting based on brand ❌ DON'T USE WHEN: - User provides local product image file → use morpheus-fashion-design - Don't need a person in the image → use nano-banana-pro - Want manual control over model, scene, packs → use morpheus-fashion-design - Already have hero image, need variations → use multishot-ugc - Need video output → use veed-ugc after image generation INPUT: Product URL + brand name (optional) + funnel stage (optional) OUTPUT: PNG advertising image with product + model
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。
PRD 到研发 Issue 拆解 / implementation issues：当用户已有 PRD、需求文档、handoff、产品方案或 GitHub PRD issue， 需要拆成可独立领取、可验收、适合 GitHub Issues 承接的开发任务时使用。可用中文唤起： “把 PRD 拆成 issue”“需求文档拆任务”“生成 GitHub issues”“PRD 拆工单”“拆 implementation issues” “按 vertical slice 拆开发票”。不用于从零写 PRD；那类请求用 prd-architect。不用于评审 PRD 是否完整； 那类请求先用 prd-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。
Use when refining a draft user prompt before sending it to a coding agent. Decomposes goal/context/constraints/acceptance/output-format, detects ambiguity and model-coupled framing, then emits a model-agnostic rewrite. Supports --analyze, --score, and --deep. NOT for prompts from scratch (mk:brainstorming), plans/reviews (mk:elicit), implementation plans (mk:plan-creator), or general codebase scouting (mk:scout).
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 CPU usage is high, memory grows unboundedly, goroutines leak, mutex contention shows in traces, or escape analysis suggests excess heap allocation. Triggers: net/http/pprof endpoint exposure, go tool pprof analysis, flamegraph generation, allocs vs inuse_space heap profiles, runtime/trace event timeline, mutex profiling, block profiling, goroutine dumps, GC pressure measurement. NOT for non-Go languages, distributed tracing (use OpenTelemetry skill), or production telemetry pipelines.
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。
Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.
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。
Constructs and validates polygenic risk scores using LDpred2-auto, SBayesRC, MegaPRS, PRS-CS, PROSPER, MUSSEL, BridgePRS, JointPRS, PRSmix, or PGS Catalog Calculator with ancestry-aware reference panels (HapMap3, UKB-LD), ancestry-conditional calibration, and PRS-RS reporting standards. Use when computing PRS for cohorts, applying absolute-risk transformation, assessing cross-ancestry portability (Martin 2017 / Ding 2023 continuous ancestry), or auditing PRS manuscripts against the 22-item PRS-RS reviewer checklist.
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Calculate and interpret the PREVENT-ASCVD risk score (10-year and 30-year) for primary prevention of atherosclerotic cardiovascular disease per the 2026 ACC/AHA multisociety dyslipidaemia guideline. Covers inputs required, risk-category thresholds (low <3%, borderline 3–<5%, intermediate 5–<10%, high ≥10%), when to use the 30-year calculation (adults 30–59), and how PREVENT differs from the older Pooled Cohort Equations (lower risk estimates by 40–50%, race-agnostic). Use when a clinician asks what the 10-year ASCVD risk is, how to calculate PREVENT, PCE vs PREVENT, risk category for this patient, does my patient need a statin, 30-year risk calculation, primary prevention risk assessment. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.
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。
Planifie le prochain sprint : calcule la capacité (vélocité glissante 3 sprints), reporte les tickets non livrés du sprint courant, puis complète depuis le backlog par priorité jusqu'à la capacité. Présente le plan, attend validation, puis assigne les tickets au sprint. Usage: /plan-sprint [<titre-sprint-cible>]
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。
Reduza o tamanho de LLMs e acelere a inferência usando técnicas de pruning como Wanda e SparseGPT. Use para comprimir modelos sem retreinamento, alcançando 50% de esparsidade com perda mínima de acurácia, ou ativando inferência mais rápida em aceleradores de hardware. Cobre pruning não estruturado, pruning estruturado, esparsidade N:M, pruning por magnitude e métodos one-shot.
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copy_tuner（CopyTuner / copy_tuner_client）で集中管理している i18n データを、prefix（正規表現）単位で Rails 標準の config/locales（YAML）管理へ移すスキル。gem の local_first_key_regexp を使うので、 gem を残したまま特定 prefix だけをローカル管理にできる（部分ローカル化）。1 回の実行で 1 prefix。 全 prefix を移して gem ごと撤去したい場合は繰り返し、完了後 copy-tuner-to-locales-cleanup スキルへ進む。 対象 prefix はスキル引数で指定でき、未指定なら export を俯瞰して選定する。