「oms」の検索結果

Think and work like an expert Human–Computer Interaction Researcher. Use when a task calls for Human–Computer Interaction Researcher judgment. Reasons from situated context, Fitts/GOMS/KLM, and CHI contribution types; runs contextual inquiry through LMM/CLMM analysis with SUS/NASA-TLX triangulation; uses Prolific/OSF and treats demand characteristics, novelty effects, ordinal misuse, and WEIRD samples as first-class failure modes.

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Think and work like an expert Global Health Researcher. Use when a task calls for Global Health Researcher judgment. Reasons from burden, equity, and health-system building blocks through DHS/MICS/DHIS2/GBD triangulation, cluster and stepped-wedge designs, RE-AIM/CFIR implementation science, and CIOMS-fair partnership while treating survey weights, HMIS completeness, and GBD smoothing as first-class failure modes.

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Think and work like an expert Entomologist. Use when a task calls for Entomologist judgment. Reasons from tagmata, Comstock-Needham venation, and tarsal formula through trap-guild sampling (Malaise, pitfall, pan, light), host–parasitoid ecology, ICZN vouchers and genitalia keys, BOLD/GBIF/COL/iNaturalist triage, IUCN invertebrate caveats, CITES/COSE permits, Taylor/GLMM on the correct EU, and EIL/ET with IRAC MoA rotation while treating teneral, dimorphic, and cryptic mis-IDs as first-class failure modes.

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Think and work like an expert Computational Physicist. Use when a task calls for Computational Physicist judgment. Reasons from governing equations, discretization, and HPC scaling through code/solution verification, DFT, MD, Monte Carlo, and FEM/FVM workflows (VASP, LAMMPS, COMSOL, OpenFOAM, QE).

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Validate any Lattice SKILL.md against all tier conventions — atoms, molecules, and refiners. Catches structural errors, broken cross-references, and convention violations before they reach the repo. If you just wrote or modified a Lattice skill file and haven't run this yet, run it now — manual review consistently misses the same categories of errors this skill is specifically designed to catch. Use when the user says 'validate this skill', 'check this skill', 'does this follow conventions', 'review this skill file', 'check my SKILL.md', or 'skill validate'. Reports PASS/FAIL with specific file-and-section findings and actionable fixes. Standalone — does not call other skills.

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Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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Native agent-to-agent language for compact multi-agent messaging. A shared tongue agents speak directly, not a translation layer. 340+ atoms across 7 domains; 3x smaller than natural language.

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Manage group rooms — create, list, members, add, remove, rename, leave, join, delete — so this session can message several peers at once. A group is a local roster of saved peers; sending to it delivers a private copy to each member. Use to set up or adjust a room, then send to it with the send skill (`send --group NAME`). `<user>` is this session's user directory (absolute path; from init).

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CustomShader authoring — vertexShaderText and fragmentShaderText against VertexInput, FragmentInput, FeatureIds, Metadata, czm_modelMaterial. Use when reading EXT_mesh_features or EXT_structural_metadata property textures/tables, vertex displacement, or shading VoxelPrimitive.

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Expert guidance for Cosign, the Sigstore tool for signing, verifying, and attaching metadata to container images and other OCI artifacts. Helps developers implement supply chain security by signing images in CI/CD, verifying signatures before deployment, and attaching SBOMs and vulnerability scan results as attestations.

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A great product that nobody knows about is a failed product. Go-to-market strategy is how you get your product into customers' hands. Not marketing in the abstract - specific motions, channels, and sequences for your specific situation. This skill covers GTM motion selection (product-led, sales-led, community), channel strategy, launch sequencing, early customer acquisition, and the critical transitions between GTM phases. Use when "go to market, GTM, launch strategy, how to get customers, sales vs product-led, channel strategy, market entry, customer acquisition, distribution strategy, product launch, getting first customers, sales motion, bottoms up, top down sales, go-to-market, gtm, launch, product-led-growth, sales, channels, customer-acquisition, distribution, marketing" mentioned.

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Use when the user explicitly asks to use the informed-patient skill to prepare for a medical appointment, organize symptoms before seeing a doctor, or evaluate the evidence behind a diagnosis or treatment. Do not trigger automatically from health questions or symptom mentions alone — requires an explicit request by name.

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Mid-conversation reflection skill that pauses execution and zooms out from detail-mode to honestly reassess direction, assumptions, and bias. Use when the user says 'reflect', 'take a step back', 'step back', 'zoom out', 'are we missing something', 'bigger picture', 'sanity check this', 'are we on track', 'are we overthinking this', 'forest for the trees', or any variation signaling intent to break out of detail-mode and reassess. Also trigger when the conversation has gone deep on implementation details without strategic check-in, or when the user shows signs of being stuck — that's often a signal the framing needs a reset, not more detail work. Intentionally low-intake: runs the 5-dimension analysis immediately when prior context is rich enough; asks one forcing clarifier only when invocation context is too thin to reassess from.

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。

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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Investigate unexpected behavior and mysterious bugs. Use when the cause of a problem is unknown and the user needs to understand WHY something is happening — symptoms like: sudden unexplained changes in metrics or behavior, works locally but not in staging/production, inconsistent or intermittent failures, correct code producing wrong results, operations succeeding but having no effect, environment-specific failures, duplicate executions, stale data, or any "why did this change?" or "why is this happening?" situation. Covers infrastructure anomalies (cache hit rates dropping, latency spikes, queue behavior shifts) as well as code bugs. The key signal is confusion about root cause, not a request to implement a known fix. Do NOT use for feature requests, known fixes, planning, or documentation tasks.

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Use when adding or reusing Flutter UI - organize widgets as atoms, molecules, and organisms in a shared library and never hand-roll a component that already exists

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Step 2 of debugging: reason about root cause based on observed symptoms.

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Guides dependency audits and upgrades. Use when auditing dependencies, upgrading vulnerable packages, managing lockfiles or package managers, SBOMs, or update and monorepo policy.

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Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.

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Use this skill when building, debugging, or answering questions about Liveblocks. Liveblocks gives you the building blocks and infrastructure to enable people and AI to work together inside your app, powering realtime collaboration. Liveblocks features include collaboration, rooms, organizations, workspaces, comments, composer, threads, notifications, multiplayer, conflict resolution, realtime presence, avatar stacks, AI collaborators, AI agents, text editors, Tiptap, BlockNote, Lexical, React Flow, Chat SDK. Common components include AiChat, Thread, InboxNotification, Composer, Toolbar (for Lexical Tiptap), FloatingToolbar, FloatingComposer, FloatingThreads, AnchoredThreads. Common hooks include useThreads, useStorage, useMutation, useOthers, useInboxNotifications, useAiChats. Common issues are related to authentication (ID tokens vs access tokens), permissions, room limits, connection errors, user info.

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Craft CMS 5 front-end Twig development — atomic design, template architecture, components, Vite buildchain. Covers atoms/molecules/organisms, props/extends/block patterns, layout chains, view routing, content builders, image presets, Tailwind named-key collections, multi-brand CSS tokens, JavaScript boundaries (Alpine/DataStar/Vue, tabs, accordions), Vite asset loading, and front-end auth (login, registration, password reset, profiles).

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Reviews Rust code for ownership, borrowing, lifetime, error handling, trait design, unsafe usage, and common mistakes. Use when reviewing .rs files, checking borrow checker issues, error handling patterns, or trait implementations. Covers Rust 2024 edition patterns and modern idioms.

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UAT fáze workflow kontraktu — odvodí scénáře z UAT sekce a akceptačních kritérií, spustí je proti disposable instanci a dodá české ruční kroky pro uživatele. Volá ji wf-impl při uat auto, nebo uživatel ručně s cestou ke spec.

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Dirigent jednoho workflow kontraktu ve svém super.engineering worktree — deleguje implementaci na subagenta, mezi fázemi pouští wf-gate, review předá skill wf-review, otevře draft PR (guardovaný), vyřeší komentáře, spustí UAT. Použij, když task session říká, že se má kontrakt provést přes wf-impl.

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。