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概要と使いどころ
Applies a modern CSS reset covering box-sizing, margin removal, text rendering, media defaults, and root stacking context. Use when starting a new web project, creating a global stylesheet, or when the codebase lacks sensible CSS defaults.
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: implement a scoped ticket, fix a bug, add tests, prepare a PR-ready code change, or reuse a standard builder workflow across software engineer agents.
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。
Frontend development guidelines for React/TypeScript applications. Modern patterns including Suspense, lazy loading, useSuspenseQuery, file organization with features directory, MUI v7 styling, TanStack Router, performance optimization, and TypeScript best practices. Use when creating components, pages, features, fetching data, styling, routing, or working with frontend code.
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 after the Lean model changed and before touching backend code — regenerates Kotlin from Lean, lists changed generated symbols, the hand-written files and contract tests that reference them, and golden regressions, so the blast radius is known before implementation.
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 covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.
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。
Tell the user (or an agent) exactly what machine they are on — chip, cores, RAM, NPU TOPS, memory bandwidth, security posture, DSH tier, and the right Ollama model to pull. Refreshes the canonical profile at agents/core/.mesh/machine.json on every call. Reads the result via agents.core.machine — zero re-probing in application code.
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 a PostHog endpoint with the right shape on the first try — covers query kind choice, name conventions, what to expose as variables (HogQL code_name vs insight breakdown), data_freshness_seconds, and whether to materialise on day one. Use when the user says "create an endpoint", "expose this query as an API", "turn this insight into an endpoint", or asks for help structuring a new endpoint. Steers away from common mistakes: materialising a query with cohort breakdowns or compare mode, inline-only variables on a materialised endpoint, unbounded date ranges, ambiguous names.
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。
Investigate AI observability evaluations of both types — `hog` (deterministic code-based) and `llm_judge` (LLM-prompt-based). Find existing evaluations, inspect their configuration, run them against specific generations, query individual pass/fail results, and generate AI-powered summaries of patterns across many runs. Use when the user asks to debug why an evaluation is failing, surface common failure modes, compare results across filters, dry-run a Hog evaluator, prototype a new LLM-judge prompt, or manage the evaluation lifecycle (create, update, enable/disable, delete).
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。
Launch multiple explore subagents in parallel to investigate architecture, data models, auth, APIs, and deployment. Synthesize into an onboarding document.
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。
Read current Unity Console entries from a running Editor. Use during bug investigation after compile, tests, PlayMode, or dynamic code to inspect logs, warnings, errors, and stack traces.
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。
Execute C# with Unity APIs when existing uloop tools cannot inspect or edit enough. Use for scene, prefab, SerializedObject, AssetDatabase refresh/.meta generation, menu, or PlayMode automation.
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。
Turn character reference images into reusable, rigged voxel 3D assets for Blender and Three.js through an imagegen turnaround and user-approval workflow. Use when Codex needs to preserve a character's likeness across front, side, and back views; create or revise Minecraft-like cube-headed models; prevent missing rear hair or clothing; split features between block geometry and face/clothing textures; generate GLB and editable .blend files; add walk/smash-compatible rigid-part pivots; or diagnose why a voxel conversion is not recognizable.
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 Skills from HTTP MCP servers with async job patterns (submit/status/result). Use when converting MCP specifications (.mcp.json) into reusable Skills using mcp_tool_catalog.yaml. Supports --lazy mode for context-saving generation. Also use for calling async MCP tools via JSON-RPC 2.0 with session-based polling.
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。
Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.
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。
Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.
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 ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.
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。
Python and Django conventions for multi-tenant SaaS POS projects: views, services, naming, JSON APIs, PDFs, and cart patterns. Trigger: When writing any view, service, API endpoint, or model code in apps/.
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。
Django ORM patterns for multi-tenant POS projects: efficient queries, bulk operations, transactions, and multi-schema upload paths. Trigger: When writing queryset logic, model saves, or DB-touching service code.
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。
Find and fix WCAG 2.2 accessibility issues. Two modes — report (sweep a codebase or page, produce a prioritized written report, no edits) and fix (audit→edit→verify loop on a target). Prefers direct-CDP live-DOM auditing; falls back to a browser-MCP composition or HTML-string audits.
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。
Extract study sites, observational variables, coordinates, climate-forcing sources, and cited data sources from ecosystem-modeling papers, PDFs, supplements, or article text, then plan or create EcoSIM simulation workflows and run folders for those sites. Use when Codex is asked to read a paper for EcoSIM feasibility, including requests phrased as "read paper for ecosim feasibility"; read a paper for sites/data availability; reproduce a published site simulation in EcoSIM; compare EcoSIM outputs to paper observations; or convert paper-derived AmeriFlux, FLUXNET, US NLDAS-domain, non-US ERA5, crop, peatland, forest, grassland, wetland, or unmanaged field-site information into EcoSIM inputs and runnable cases.
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。
Review or refactor an existing SKILL.md and its bundled resources — a smaller always-loaded core, conditional references, preserved behavior, lean/guided consumer profiles, and an A/B protocol to compare a candidate with the previous version. Use when a skill repeats itself, loads references on every run, triggers too broadly or misses legitimate requests, or needs its efficiency measured across models. Do NOT use for writing a new skill from scratch, refactoring application code, translating a skill verbatim, or performing the target skill's own task.
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 to set up pwdev-feat in a project — 'inicializar o pwdev-feat', 'configurar o feat', 'init feat workspace' — choosing language, model profile and the optional audit trail, and creating the .planning/feat/ workspace. Do NOT use to analyze the codebase (feat-map), to write the project instructions file (feat-setup) or to create plans.
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 a backend-only action plan — API endpoints, services, models, migrations, jobs and their tests — 'plano de backend para o endpoint X', 'plan the orders API'. Writes .planning/feat/features/{slug}/plan.md with the PWDEVIA method. Do NOT use for UI work (feat-frontend), full features spanning backend and UI (feat-feature), writing code (feat-exec) or quick 1–3 file fixes (feat-quick).
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 multiple Reforge-guided section-refactor lanes in parallel, each in its own worktree/branch off origin/main, with a score-blind adversarial review panel gating every commit, one draft PR per section. Use when the user wants to reduce Reforge surface/internal score across several sections at once via architecturally-correct deletions (dead surface, entity-leak removal, cross-section read-splits) — not by relocation, parameter bags, or accessibility-dodging. Wraps the per-lane process in .codex/skills/humans-refactor. Triggers: 'refactor swarm', 'parallel section refactors', 'run reforge refactor on Users/Tickets/...', 'burn down surface score across sections'. Has an intensity dial (light|standard|deep) and a workflow|solo execution mode to trade token burn against autonomy per unit of output.
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。