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概要と使いどころ
Use when a task needs the judgment of a Clinical Research Coordinator — managing patient recruitment/enrollment against a trial's screen-fail rate, handling a protocol deviation, tracking informed consent and regulatory documentation, or coordinating a multi-visit study schedule against patient retention risk.
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。
Coordinate structured thinking and multi-agent parallel execution for complex tasks. Use when tackling multi-step projects, planning parallel work, breaking down complex problems, coordinating specialist tasks, facing architectural decisions, or when user mentions "workflow", "orchestration", "multi-step", "coordinate", "parallel execution", "structured thinking", "break this down", "plan this out", "how should I approach", or needs help planning complex implementations.
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。
Craft high-quality natural-language image prompts for any modern text-to-image or image-edit model that accepts flowing English. Trigger when the user wants help writing, rewriting, improving, or translating an English natural-language image prompt — including "write me an image prompt", "improve this image prompt", "describe this scene for an image model", or "convert these tags into a natural language prompt". Do NOT trigger for requests that are purely about dispatching to an image API, choosing samplers/schedulers, picking LoRAs, or setting up ControlNet — those belong to a runtime skill.
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。
Family coordination engine. Use this skill whenever the user mentions meals, meal planning, groceries, weekly schedule, family logistics, kid activities, custody schedule, co-parent coordination, family dashboard, weekly briefing, weekly prep, "what's for dinner", school events, pickup/dropoff, babysitter, pediatric nutrition, grocery list, grocery cart, "load the cart", "load the carts", "bargain hunt", "find the deals", "compare prices", "price check", "smart split", "save money on groceries", family calendar review, or anything involving coordinating schedules and meals for a family. Also trigger when the user says "parent helper", "family mode", "week ahead", or "plan the week". Trigger on local event queries: "what's going on", "things to do", "events this weekend", "anything happening", "stuff to do", "what can we do with the kids", "family stuff this weekend". This skill is the central hub — if it touches the family schedule, the family's food, or local family-friendly events, it belongs here.
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。
Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver 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。
Author and run offline evals for Agentforce agents: fixture format, scoring rubrics, hallucination checks, regression baselines, CI integration, prompt-change safety. Trigger keywords: agent eval, hallucination, doesn't hallucinate, agentforce offline evals, rubric for agent response. NOT for deciding which layers of testing the agent needs — use agentforce/agentforce-testing-strategy. NOT for Testing Center, AiEvaluationDefinition or sf agent test runs — use agentforce/agent-testing-and-evaluation.
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 this skill when designing, troubleshooting, or reviewing FSL Mobile worker workflows — job lifecycle management, offline data capture, customer signature collection, parts consumption, and geolocation-triggered status transitions. Trigger keywords: FSL mobile, field service mobile workflow, offline job lifecycle, technician mobile, service appointment status, briefcase priming, parts consumption mobile, signature capture FSL. NOT for installing, branding, or extending the mobile app itself — use admin/fsl-mobile-app-setup. NOT for offline priming limits and sync conflict resolution — use architect/fsl-offline-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。
Use this skill when mapping care coordination process requirements for Health Cloud: designing care team workflows, transition of care handoffs, social determinants of health (SDOH) barrier tracking, and care gap identification. NOT for building care plan templates, problems, and goals — use admin/care-plan-configuration. NOT for referral routing and provider networks — use admin/referral-management-health.
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 a web app feel native on a phone — the small CSS and meta-tag fixes that separate "a website in a browser" from something that feels installed. Covers sticky hover states, tap highlight flashes, the 100vh bug, inputs that zoom the page, laggy taps, pull-to-refresh hijacking scroll, content under the notch, long-press selecting button text, carousels that scroll the wrong way, mismatched status bars, and the rule that you test on real hardware. Use when a web app is being built for or reviewed on mobile, when something "works in Chrome but feels wrong on my phone", when building a PWA, a bottom sheet, a carousel, a full-screen layout, or any touch interaction. For motion itself use animate; for React Native use animate-expo.
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。
Cycle de coordination ai-01 (coordinateur UNIQUEMENT — jamais sur un worker). Lit memoire + dashboards + inbox + GitHub, merge les PRs pretes, tranche les design-gates, dispatche des grains par lane, reporte. Arguments: [--dispatch] [--focus <topic>]
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。
Produces a plain-language comparison of advance directives and POLST/MOLST forms, covering legal status, clinician signatures, emergency precedence, clinical appropriateness, and document coordination. Use when the user asks about advance directive vs. POLST, living will vs. DNR, which document EMS follows, POLST vs. MOLST vs. POST, whether a healthy person needs a POLST, or document coordination in elder law, estate planning, or serious illness contexts.
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 answering factual or explanatory questions about the current project — codebase, architecture, conventions, workflows, constraints — with minimal cited evidence. Triggers on "how does X work here", "what does X mean", "why is X structured this way", "is this claim true in the repo", "explain X with sources". NOT for brainstorming or comparing approaches (mk:brainstorming), trade-off decisions (mk:party), debugging/root cause (mk:investigate), code review (mk:review), plans (mk:plan-creator), security audits (mk:cso), visual explanations/diagrams (mk:preview --explain), test-coverage gaps (mk:nyquist), library docs (mk:docs-finder), or workflow navigation (mk:help).
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。
Install and configure PostHog analytics SDK in React Native applications, including event tracking, feature flags, user identification, and error tracking. Use when the user needs to add PostHog analytics, session recording, or feature flags to a React Native app.
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。
Digital signatures and time stamps through Openapi - eIDAS-compliant electronic signatures (SES with OTP, qualified QES, e-seals, massive automatic signing) and qualified time stamping of documents. Use when the user needs a document signed electronically or time-stamped with legal validity.
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。
Comprehensive reference for inter-process communication mechanisms -- every way processes can talk on a computer. Covers sockets (TCP, UDP, Unix domain), WebSockets, SSE, pipes (named/anonymous), shared memory (mmap, shm_open), message queues, signals, D-Bus, XPC (macOS), gRPC, REST, stdin/stdout, file-based coordination, and clipboard. Performance benchmarks, platform availability, and code examples for each. Special focus on which IPC works best for AI agent coordination. Activate on: "IPC", "inter-process communication", "process communication", "how do I talk between processes", "Unix socket vs TCP", "shared memory", "named pipe", "WebSocket vs SSE", "gRPC vs REST", "agent coordination IPC", "XPC service", "message passing", "stdout pipe", "D-Bus", "mmap". NOT for: distributed systems design (use distributed-systems), network protocol design (use networking), message queue infrastructure like Kafka (use data-pipeline-engineer).
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。
Benchmark multi-agent coordination systems with experiment design, power analysis, human rating protocols, bootstrap confidence intervals, and reproducible reporting. Use for salvage latency, recovery fidelity, coordination overhead, and protocol comparison studies. NOT for ML model benchmarking, web-product A/B testing, survey design, or general-purpose data science.
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 fabricated content, false citations, and unverifiable claims in agent outputs. Uses source verification and consistency checking. Activate on 'detect hallucination', 'fact check', 'verify claims', 'check accuracy', 'find fabrications'. NOT for validation (use dag-output-validator) or confidence scoring (use dag-confidence-scorer).
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。
Naturalistic causal reasoning for failures and disputed attribution. Use when postmortems or root-cause debates need reframing. NOT for causal inference, fault trees, or fully observed failures.
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。
Choose multi-agent architectures using environment-first analysis, coordination pressure, commitment tuning, and knowledge levels. Use for autonomy design, protocol choice, and coordination failures. NOT for single-agent planning, centralized schedulers, or prompt-only swarms.
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。
Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing 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。
This skill recommends discussing and offering age-appropriate vaccinations (influenza, herpes zoster, pneumococcal) to patients with Cushing’s syndrome due to increased infection risk. Use when reviewing preventive care for a CS patient; triggers include discussing preventive care, vaccination status, or infection risk in a CS patient.
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Determines the appropriate IV pamidronate dose (60‑90 mg) and infusion duration (2‑24 h) for adults with hypercalcemia of malignancy based on renal function. Triggered when a clinician orders pamidronate and asks how to adjust for kidney dysfunction (eGFR < 60 mL/min/1.73 m²).
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。
Expert-level discrete mathematics knowledge. Use when working with logic, set theory, combinatorics, graph theory, relations, functions, induction, recursion, boolean algebra, or discrete probability. Also use when the user mentions 'combinatorics', 'permutations', 'combinations', 'pigeonhole principle', 'inclusion-exclusion', 'generating functions', 'recurrence relations', 'graph theory', 'trees', 'logic', 'propositional calculus', or 'boolean algebra'.
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。
Agentes de voz representam a fronteira da interação com IA - humanos falando naturalmente com sistemas de IA. O desafio não é apenas reconhecimento e síntese de fala, mas alcançar fluxo de conversa natural com latência inferior a 800ms enquanto se lidam com interrupções, ruído de fundo e nuances emocionais. Esta habilidade cobre duas arquiteturas: fala-para-fala (OpenAI Realtime API, menor latência, mais natural) e pipeline (STT→LLM→TTS, mais controle, mais fácil de depurar). Insight-chave: latência é a restrição. Hu
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。