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概要と使いどころ
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it. Use for /arena, 'arena this', 'throw it in the arena', or when one attempt at a non-trivial artifact would lock in the wrong shape.
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Apply when concurrent actors might write to the same file, branch, key, or state object. Eliminate the sharing first; serialize structurally only when one shared writer is a real invariant.
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。
Apply when designing commands, lifecycle steps, or processing loops that run amid crashes, restarts, and retries. Converge to the same end state regardless of partial prior runs.
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Vulnerability audit and dataflow analysis built on the neug CPG engine. file_audit (default, recommended): first builds a function-level summary graph (tier1) over the entire project, then incrementally builds detailed CPGs (tier2) on demand for chosen functions; both tiers coexist in a single neug database (unified schema, cross-layer refines edges; C/Java/Python/Rust). All capabilities are exposed as CLI tool subcommands (audit/overview/build/query/methods/paths), so an agent can drive the audit step by step via shell; the audit subcommand can also run the built-in LLM loop. The same tooling also supports a dataflow analysis mode (Workflow C): no vulnerability hunting, only variable-level dataflow tracing for functions of interest, producing dataflow_analysis.json. repo_audit: multi-round LLM/Cypher audit over a full-repo CPG. Use when the user asks to audit a codebase/project for vulnerabilities, scan a project with CPG/neug, run repo_audit/file_audit, find source-to-sink taint paths, or analyze dataflow inside a function. Supports C, Java, Python, and Rust.
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。
Makes working code easier to read and change without altering what it does, and proves the behaviour stayed the same. Use when someone says "simplify this", "clean up this function", "this file is a mess", "reduce the complexity", "refactor for readability", "tidy my diff before the PR", or when a linter reports high cyclomatic complexity or deep nesting. Covers pinning current behaviour with characterization tests, measuring complexity, choosing a named refactoring, applying it in small verified steps, and reporting suspected bugs found on the way instead of fixing them silently.
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Writes the App Store "What's New in This Version" text for an app update from the repository's commit history: finds the range since the last shipped version, separates changes users can notice from internal work, drafts plain-text notes within Apple's 4,000-character limit and review guidelines, and returns a trace from every sentence back to its commits. Use when someone asks to "write release notes for the App Store", "generate What's New from git", "summarize changes since the last tag for the store listing", or needs the same notes cut to Google Play's 500 characters.
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 comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Professional LaTeX formatting, strategic analysis frameworks (Porter's Five Forces, PESTLE, SWOT, TAM/SAM/SOM, BCG Matrix), and deep research integration.
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。
A/B and multivariate testing methodology. Design experiments, calculate sample sizes, determine statistical significance, avoid common pitfalls, and interpret results. Platform-agnostic framework applicable to landing pages, emails, ads, pricing, and product features. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, or conversion experiments.
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 in building custom tools that solve your own problems first. The best products often start as personal tools - scratch your own itch, build for yourself, then discover others have the same itch. Covers rapid prototyping, local-first apps, CLI tools, scripts that grow into products, and the art of dogfooding. Use when "build a tool, personal tool, scratch my itch, solve my problem, CLI tool, local app, automate my, build for myself, " 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。
Design and implement Monte Carlo methods for uncertainty quantification, risk analysis, and probabilistic simulations across scientific and financial domains. Use when "monte carlo, random sampling, uncertainty quantification, risk analysis, stochastic simulation, MCMC, variance reduction, probabilistic, " 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。
The craft of designing icons that communicate instantly across cultures, contexts, and scales. Icon design bridges semiotics, cognitive psychology, and visual craft to create symbols that users understand without thinking. Great icons are invisible in the best way - they convey meaning so naturally that users never pause to decode them. This skill covers icon grid systems, optical alignment, stroke consistency, metaphor selection, scalability across sizes, SVG optimization, and icon set coherence. The best icon designers understand that icons are a visual language - each icon must speak the same dialect while carrying its own distinct meaning. Use when "icon, iconography, symbol, glyph, icon set, icon library, pictogram, svg icon, icon grid, icon pack, feather icons, lucide, phosphor, heroicons, icon system, icon style, icons, iconography, svg, symbols, glyphs, pictograms, ui-icons, icon-set, visual-design, design-system" mentioned.
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Use when implementing object detection, semantic/instance segmentation, 3D vision, or video understanding - covers YOLO, SAM, depth estimation, and multi-modal visionUse when ", " 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。
CI/CD is the backbone of modern software delivery. Continuous Integration catches bugs early. Continuous Deployment gets features to users fast. But poorly designed pipelines can be slow, flaky, and a source of constant frustration. This skill covers GitHub Actions (the most popular), GitLab CI, and general pipeline design principles. The focus is on pipelines that are fast, reliable, and maintainable. 2025 reality: Your pipeline is infrastructure code. It deserves the same care as your application. A flaky test in CI is worse than no test - it teaches developers to ignore failures. Use when "github actions, gitlab ci, ci cd, pipeline, workflow, deploy automation, continuous integration, continuous deployment, build pipeline, yaml workflow, ci-cd, github-actions, gitlab-ci, devops, automation, deployment, pipelines" 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。
Patterns for designing and analyzing clinical trials, including survival analysis, endpoint selection, sample size calculation, interim analyses, and regulatory considerations. Covers FDA/EMA guidelines and modern adaptive designs. Use when ", " 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。
Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization. Use when ", aws, lambda, serverless, api-gateway, dynamodb" 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。
Your most loyal users aren't just customers - they're potential advocates, contributors, and community members who can become the engine of your growth. Community-led growth isn't about broadcasting to an audience - it's about creating a space where your users connect with each other, share knowledge, and become invested in your success. This skill covers community strategy, platform selection, engagement tactics, ambassador programs, and the metrics that matter. Whether you're building a Discord for developers or a forum for power users, the goal is the same: creating genuine value that makes members want to stay and contribute. Use when "community, discord, forum, user group, ambassador, champions, developer relations, devrel, community engagement, community growth, power users, superfans, community, discord, engagement, devrel, ambassador, forum, user-community, growth, retention, champions" 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。
World-class character and art style consistency for AI-generated images and videos - ensures visual coherence across series, maintains character identity, and provides rigorous QA before deliveryUse when "character consistency, art style, same character, consistent character, visual continuity, series, turnaround sheet, character sheet, reference image, character bible, style guide, anime character, consistent look, face consistency, outfit consistency, lora training, ip-adapter, flux kontext, visual qa, art quality, generation review, style drift, character drift, character-consistency, art-style, visual-qa, ai-art, image-generation, video-generation, anime, illustration, lora, ip-adapter, flux, midjourney, stable-diffusion" 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。
The specialized craft of writing copy that sells—headlines that stop thumbs, body copy that persuades, CTAs that convert. This is copywriting specifically tuned for advertising: paid media, performance marketing, direct response. Ad copy is different from content writing. Every word costs money (CPM/CPC). Every character competes for attention. There's no time for warm-up—you have one chance to hook, persuade, and convert. The best ad copy feels effortless but is engineered word by word. In the age of AI-generated creative at scale, ad copywriting becomes both more important (you need more variations) and more challenging (everyone has access to the same tools). The differentiation is in the strategy, the insight, and the craft that AI assists but doesn't replace. Use when "ad copy, headline, ad text, CTA, call to action, Facebook ad, Google ad, ad variations, direct response, performance copy, primary text, ad headline, ad-copy, headlines, direct-response, performance, conversion, cta, advertising" 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。
The science of learning through controlled experimentation. A/B testing isn't about picking winners—it's about building a culture of validated learning and reducing the cost of being wrong. This skill covers experiment design, statistical rigor, feature flagging, analysis, and building experimentation into product development. The best experimenters know that every test, positive or negative, teaches something valuable. Use when "a/b test, experiment, hypothesis, statistical significance, sample size, feature flag, variant, control, treatment, p-value, conversion rate, test winner, split test, experimentation, testing, statistics, feature-flags, hypothesis, growth, optimization, learning, validation" 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。
Writes a handover document so another person, another agent, or a different tool can pick up multi-session work cold. Use when handing work to someone else, switching tools, or pausing work that will not resume in the same session.
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。
Extracts and records architectural decisions from diffs, conversation context, and explicit choices. Supports automatic extraction from staged changes, deduplication against prior decisions, and persistent ADR-format logging with spec traceability. Use after a significant design or architecture choice, before committing a diff that embeds implicit decisions, or when the same question keeps resurfacing.
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 experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code line-by-line for fake/derived ground truth, score self-normalization, phantom results (a paper number with no backing file/key), dead/uncalled metric code, verified-scope inflation, method-described ≠ method-evaluated drift, synthesized-looking results, placeholder/fake data still wired into a released result, code-output ≠ reported-number mismatch, and missing reproducibility artifacts (an empirical/agent/LLM paper shipping neither code nor the prompts/configs its results need) — every finding span-anchored to a ledger claim_id. At L0/L1 (PDF / source only) the same patterns are surfaced as info-level 'could-not-verify' signals where the ledger gives an anchor (observability_level_required:2) — NEVER a fraud verdict from a PDF. The reviewer PROPOSES findings; tools/adjudicate_findings.py computes the verdict. Detect-only. Triggers: "experiment forensics", "audit the results", "check the eval code", "实验诚实度".
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。
AWS Advisor workflow skill. Use this skill when the user needs Expert AWS Cloud Advisor for architecture design, security review, and implementation guidance. Leverages AWS MCP tools for accurate, documentation-backed answers. Use when user asks about AWS architecture, security, service selection, migrations, troubleshooting, or learning AWS. Triggers on AWS, Lambda, S3, EC2, ECS, EKS, DynamoDB, RDS, CloudFormation, CDK, Terraform, Serverless, SAM, IAM, VPC, API Gateway, or any AWS service. Do NOT use for non-AWS cloud providers or general infrastructure without AWS context and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
@arm-cortex-expert workflow skill. Use this skill when the user needs Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。