126 件 ・ 関連度順
概要と使いどころ
Enforce structured JSON logging, OpenTelemetry distributed tracing, and RED metrics across backend services. Use when adding request correlation, setting up tracing spans, defining SLO burn-rate alerts, or instrumenting middleware.
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Grade a curated list of individual tests for readiness, A-F quality, and concrete improvements. ALWAYS USE FOR: grade tests, review only a named test, per-test readiness decisions, or quality bands for supplied methods, bodies, file spans, or bounded PR diffs, including existing tests. Produce a PR-ready Pass, Failed, Uncertain, or Not applicable table; unresolved or empty scopes omit the grade. Compose read-only per-test mutation evidence when available. Polyglot: .NET, Python, TS/JS, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, C++. DO NOT USE FOR: suite-wide audits (test-engineer or test-anti-patterns), writing or fixing tests, or measuring coverage.
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 need to implement or improve distributed tracing with OpenTelemetry in Java — including trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors. This should trigger for requests such as Improve tracing; Apply OpenTelemetry tracing; Add distributed tracing; Refactor tracing instrumentation; Instrument Java services with OpenTelemetry spans. Part of Plinth Toolkit
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。
Runs multi step work the way a careful senior engineer would. Triage before code, a short PRD in prds/, task.md kept current, docs synced after, and lessons recorded when a mistake repeats. Use when a task spans several files or steps, changes architecture, needs a plan the user can review, or when the user says "write a PRD", "plan this", "track this in task.md", or "what did we learn".
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。
Track complex, multi-session work with dependency graphs using bd (beads) issue tracker. Use when work spans multiple sessions, has complex dependencies, or requires persistent context across compaction cycles. For simple single-session linear tasks, TodoWrite remains appropriate.
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。
Unified CLI/Python interface to 20+ genomic databases. Gene lookups (Ensembl search/info/seq), BLAST/BLAT, AlphaFold, Enrichr enrichment, OpenTargets disease/drug, CELLxGENE single-cell, cBioPortal/COSMIC cancer, ARCHS4 expression. Spans genomics, proteomics, disease. For batch/advanced BLAST use biopython; for multi-DB Python SDK use bioservices.
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 for any task that spans more than a couple of steps, files, or sessions — refactors, migrations, multi-file features, anything that must survive a context reset or crash. Trigger when the user asks for a plan, says "keep track of this", "don't lose the thread", or when a task is large enough that forgetting step four is likely.
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 describes a research goal without naming a skill, or the task spans several skills. Classifies the request, plans the order and routes to the right medsci-skills skill(s) instead of producing their output itself.
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 repo skill for Agent Lightning package tasks: authoring trainable agents, tracing rewards and spans, running LightningStore/Trainer loops, using agl CLI services, choosing examples, and troubleshooting optional backends.
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。
Decide when to delegate to Honey's read-only subagents (hive-scout, hive-reviewer) instead of working inline, so the expensive tokens — large file reads and review passes injected back into the orchestrator's context — come back compressed (Honey Lever 3). Use when a task is search-heavy or review-heavy, spans many files, or you want to keep the orchestrator's context small. Not for trivial one-file 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。
Design privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.
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。
Maxim Master Design Intelligence — full-spectrum UI/UX orchestration. Activates all design sub-domain skills in parallel. Use when a task spans multiple design domains simultaneously: product design strategy, interaction design, information architecture, usability heuristics, design critique, cross-platform consistency, design leadership. The master conductor of all Maxim design 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。
Mintlify presents documentation infrastructure with a dual-mode aesthetic — atmospheric sky-gradient marketing heroes (cloud illustration backdrops, soft cream-to-blue washes) paired with dense developer-grade documentation surfaces. The system uses Inter for UI prose, Geist Mono for code, and a signature Mintlify green ({colors.brand-green}) reserved for accent CTAs and active states. Black-pill primary buttons dominate marketing, white-on-dark inversions appear on dark hero bands, and a 3-column documentation layout (sidebar / prose / TOC) anchors the developer experience. Coverage spans homepage, startups program, pricing comparison, and the live tabs documentation page.
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。
Routes reverse engineering, exploitation, penetration testing, malware, mobile, firmware, browser automation, documentation, and security tasks to the appropriate specialist skill. Use when a task spans modules or the correct reverse-skill entrypoint is unclear.
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MiniMax presents itself as a premium AI infrastructure brand through a striking duality — bold black-pill CTAs and stark white canvas for marketing, paired with vibrant gradient product cards (orange-red, magenta-pink, purple, blue) that turn each model release into a distinctive visual identity. The system uses DM Sans across all surfaces, employs an oversized 80px hero display, anchors major actions in deep near-black pills, and layers content density via a 3-column documentation grid with sidebar nav, prose body, and TOC. Coverage spans the marketing homepage, model showcase pages, developer documentation, and platform pricing surfaces.
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。
I am a veteran print production specialist with 15+ years in tabletop game manufacturing. I've shepherded hundreds of games from digital files to retail-ready products, working with manufacturers from The Game Crafter to Panda GM to Cartamundi. My expertise spans the entire production pipeline: color management, dieline creation, paper selection, finishing techniques, component nesting, insert design, and the critical art of the unboxing experience. I've learned the hard way about RGB-to-CMYK disasters, registration drift, humidity warp, and minimum font sizes that become illegible smudges. I know the difference between 280gsm blue-core and 320gsm black-core card stock by feel. I can tell you why your rich black needs to be C60-M40-Y40-K100, not just K100. I understand why your box needs 15mm clearance on each dimension, and why that gorgeous neon green in your design will become a muddy olive in print. Use when "print my game, packaging design, prepare files for print, dieline, print specifications, board game manufacturing, card game printing, box design, bleed and safe area, CMYK conversion, paper stock, card finish, foil stamping, embossing, unboxing experience, thermoform insert, punchboard design, component manufacturing, Panda GM, Game Crafter, Cartamundi, print-production, packaging, manufacturing, board-games, card-games, cmyk, dieline, die-cutting, offset-printing, tabletop" mentioned.
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Power systems engineering covering grid modeling, power flow analysis, energy storage dispatch, demand response, and electricity market economics. Spans transmission/distribution planning to real-time operations. Use when "power flow|load flow|grid model, energy storage|battery dispatch|ESS, demand response|load management|peak shaving, electricity market|LMP|locational marginal price, grid stability|frequency|voltage, capacity planning|resource adequacy, unit commitment|economic dispatch, transmission|distribution|power system, " mentioned.
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。
Runs a multi-phase project from an orchestrating main thread that plans, routes and verifies but does not build. Defines a few long-lived workstreams, each owning a file area end to end in its own worktree, and routes every follow-up to the owning agent instead of spawning a one-off. Covers base-SHA recording, rebase-before-commit, stale-checkout detection, and shared-machine hygiene (ports, processes, measurement noise). Use when a project spans several phases or sessions with three or more agents working at once.
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。
MEMO-ONLY prior-work overlap advisory: surfaces the two ADVISORY taxonomy signals neither a tool nor a model can decide from the paper alone — ADV-TRIVIAL-COMBINATION (standard A+B+C / 缝合 stapling) and ADV-DUPLICATE-PUBLICATION (repackaged / duplicate submission). The executor RETRIEVES candidate prior work (DBLP fuzzy-title + boolean · WebSearch · WebFetch) from the paper's own title + contribution spans in the evidence ledger; TWO fresh cross-model codex reviewers (one per axis) LAY OUT the overlap side-by-side against each anchored contribution claim. It NEVER rules 'trivial' or 'duplicate' (that is a human judgment) and absence of a match is NOT evidence of originality. Emits novelty-duplication-advisory.memo.md + an info-only findings mirror; carries NO verdict weight — tools/adjudicate_findings.py lists it in ZERO_WEIGHT_SKILLS and caps it at info. Detect-only. Adapted from ARIS novelty-check, reframed from 'is MY idea novel' to 'here is the overlap a reviewer should weigh'. Triggers: "novelty advisory", "duplication check", "prior-work overlap", "is this stapling", "缝合", "查重", "重复发表", "duplicate submission".
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。
Multi-conversation methodology for iterative stateful work with context accumulation. Use when users request work that spans multiple sessions (research, debugging, refactoring, feature development), need to build on past progress, explicitly mention iterative work, work logs, project knowledge, or cross-conversation learning.
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。
Symbol-level navigation of a local checkout using tree-sitter ASTs. Answers where a symbol is defined, what lines it spans, which symbols a file exposes, what a directory holds, and where a name is referenced — every answer carries exact line ranges to feed straight into a scoped read. Use for "where is X defined", "who calls X", "find the function/class named", "what's in this file", "give me the line range for", "show me the source of", "list the symbols in", or before editing a file you have not read. Each invocation auto-scans and is self-contained. Not for first-encounter repo orientation (use exploring-codebases), for what a codebase DOES rather than what it contains (featuring), for binding-resolved Python caller sets (searching-codebases), or for literal text and regex matching (plain ripgrep).
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。
Computes a numeric or categorical answer to a quantitative data-science question by cleaning and analyzing local data files (CSV, Excel, TSV, and scientific formats .npz/.fits/.h5) with pandas, numpy, and scipy. Use whenever a task ships its own dataset (in whatever local directory it provides) and asks you to derive a value via statistical, geospatial, temporal, ratio, percentage, growth-rate, correlation, signal-processing, orbital, or forecasting analysis. Spans domains including archeology (demographics, paleoclimate proxies, radiocarbon dating, historical conflicts); biomedical/bioinformatics (tumor histology, genes, proteins, peptides, genomic variants); environmental (water quality, bacterial/Enterococcus exceedance, rainfall correlations, environmental justice); legal/consumer (fraud, identity theft, consumer complaints by state/metro); wildfire (incidents, causes, suppression costs, acres burned, fatalities, geospatial intersections); and astronomy/heliophysics/space weather (satellite telemetry TLE/Swarm/SP3, indices OMNI/GOES, solar activity, atmospheric density, orbital mechanics, forecasting).
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 full writing stack: sharper copy for docs, pages, email, social, headlines, CTAs, product listings, and public explainers, with an SEO/AEO/AI EO pass, persona and framework selection, brand-voice alignment, and a scored ship gate. Use when a writing job spans more than one surface, needs a voice retune as well as a draft, needs discoverability metadata alongside the copy, when the document is one an agent reads such as a SKILL.md, CLAUDE.md, or AGENTS.md, or when the user asks for 'the full writing stack', a launch package, or a public explainer talk-track. NOT FOR: one standalone conversion surface in one pass (use suede-copy); stripping AI patterns from text you did not write (use suede-deslop); a researched multi-phase piece for a high-stakes public surface (use suede-ship-copy); a deep standalone SEO audit (use suede-seo-audit); copy that ships inside a design or layout build (use johnny-suede-design).
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。
MUST use for ANY query mentioning packages, monorepo, workspace, catalog, turbo, turborepo, or pnpm in a multi-package context. MUST use when sharing config (ESLint, tsconfig, prettier) across packages, fixing build order between packages, adding new packages, scoping CI installs/caching to changed packages, or debugging pnpm catalog version resolution. This skill OWNS all cross-package coordination problems — even when they look like build, CI, config, or dependency issues. If two or more packages interact in the query, this skill applies. Takes priority over other skills when the problem spans package boundaries.
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。