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概要と使いどころ
Design and generate CI/CD pipelines from project stack signals across GitHub Actions, GitLab CI, CircleCI, and Buildkite. Use when bootstrapping CI, migrating pipelines, adding deployment gates, or optimizing build times.
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。
One run from signal to decision. Finds the names of new AI models, products and repositories seen in the last 24 hours (X, Hugging Face, GitHub, sitemaps), then builds a keyword priority table for them, each new name and the generic search terms behind it, sized against Google Ads volume and Google Trends, with prioritize, watch or skip. Use when the user wants today's new AI keywords, an AI trend radar, or asks what new AI terms are worth building a page or tool for. Also takes names the user already has. Runs the new-term-radar command line; needs an AISA_API_KEY.
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 new AI names or unstructured AI market signals into a keyword priority table. For each name it finds the generic search terms downstream of it, screens them for relevance, and checks novelty, demand size and momentum against Google Ads volume and Google Trends, ending in prioritize, watch or skip. Use when the user has new model, product or repository names (or an AI listening report, a batch of posts, a changelog) and asks which keywords to build pages or tools for. Runs the new-term-radar command line; needs an AISA_API_KEY, and a GOANYAPI_API_KEY improves it.
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。
Answer prediction questions using market trading data, not opinions. Use when the user asks probability questions about geopolitics, economics, markets, industries, or any topic where real money is being traded on the outcome. Examples: 'What's the probability of WW3?', 'Will there be a recession?', 'Is AI in a bubble?', 'When will the Russia-Ukraine war end?', 'Is it a good time to buy gold?', 'Will SPY drop 5% this month?', 'Is NVDA options premium overpriced?'. The skill reads prices from prediction markets, commodities, equities, options chains, derivatives, yield curves, and currencies, then cross-validates multiple signals to produce a structured probability report.
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。
Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection.
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 has run multiple evaluation skills and wants a comprehensive analysis — maturity assessment, cross-skill signals, trends, prioritized actions, and an executive summary. Also use when the user mentions eval health check, evaluation audit, ship readiness, evaluation maturity, or "how good is my evaluation system itself." This is a read-only analysis 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。
End-to-end external local counsel lifecycle management for multi-jurisdiction legal matters. LC selection criteria and capability assessment, engagement setup and instruction design, performance monitoring and check-in cadence, scope enforcement, and relationship escalation. Use when selecting local counsel for a jurisdiction, designing LC instructions, managing the LC check-in rhythm, enforcing scope boundaries when LC signals overreach, or escalating a performance or relationship issue beyond the matter team. Trigger on: 'which LC should we use', 'LC instruction', 'brief the local counsel', 'LC hasn't responded', 'LC is going off scope', 'LC scope dispute', 'confirm scope with LC', 'LC check-in', 'LC is slow', 'monitor the LC network', 'LC engagement letter', 'LC selection', 'what should we tell the local counsel', 'LC onboarding', 'LC performance issue', 'LC relationship problem'.
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。
Audits a legal document against legal design principles. Scores the document on six pillars: language patterns (bureaucratese, archaisms, passive voice, nominalization), readability (Flesch Reading Ease and language-specific equivalents), structure and navigation, hidden conditions (material obligations buried away from their headings), statutory duplication (restating the law instead of citing it), and visual hierarchy where layout is available. Assessment-only; does not rewrite the document. Works on English, French, and German documents, inferring the applicable jurisdiction from the document's language and textual signals: governing-law clause, statute citations, party addresses. Use when a user shares a legal document (contract, NDA, T&C, privacy policy, internal policy) asking how good the drafting is from a legal design perspective, whether it is readable, whether it contains buried clauses or restates the law, or how it scores on Flesch / LIX / Amstad / Kandel-Moles readability metrics.
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。
When you want to monitor known sources on a schedule and feed the good stuff into your second brain. Configure sources once (YouTube, RSS/newsletters, subreddits, Hacker News, Bluesky, Mastodon, X, LinkedIn, keyword searches); each run fetches only what's new, scores it against your stated focus, writes one digest to the vault, and auto-captures high-signal items into raw/. Everything else waits in the digest until you promote it. Modes — run, digest, promote, add / sources / pause, doctor, schedule (daily launchd or systemd job). Triggers on "/radar," "run my radar," "check my sources," "what's new from my sources," "add a source," "monitor this channel," "watch this subreddit," "track this account," "daily digest," "promote item 4," "radar doctor." Complements second-brain (radar fills raw/, second-brain compiles it) and deep-research (standing surveillance vs a one-off dive).
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。
When you have a decision to make and want a structured workflow that picks the load-bearing questions, walks through them, reaches a call (or "wait"), and archives the rationale for future reference. Based on the 37signals Guide to Making Decisions (38 questions) plus house additions like Q39 opportunity cost ("what does saying yes displace?"). Triages to 6–8 relevant questions per decision instead of forcing the full set. Archives every decision to ~/.config/makerskills/decide/archive/ with a revisit date so you can check later whether the call was right. Triggers on "/decide," "help me decide," "should I [X]," "I need to make a decision about," "stuck on a decision," "deciding between," "go/no-go on," "what should I do about." This is both the decision-making workflow AND the decision log — making the decision is the act of logging it.
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。
Map a complex topic by placing factors on hexagonal tiles where adjacency signals a claimed relationship. Use when students need to surface hidden connections in a system before analysis or action.
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 testing decision trees for GdUnit4: unit vs scene vs CI gates, headless runners, snapshots, and mock networks. Use when choosing test layers, wiring CI, or validating signals/physics without beginner assert catalogs. Keywords: GdUnit4, GdUnitTestSuite, headless CI, snapshot test, mock network, scene integration test, TDD.
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 testing decision trees for GdUnit4: unit vs scene vs CI gates, headless runners, snapshots, and mock networks. Use when choosing test layers, wiring CI, or validating signals/physics without beginner assert catalogs. Keywords: GdUnit4, GdUnitTestSuite, headless CI, snapshot test, mock network, scene integration test, TDD.
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。
Best-practices reference for preparing and instrumenting applications on Temps. Covers the app runtime contract (`.temps.yaml` health, HOST/PORT, readiness, SIGTERM, replicas, migrations) and production observability (errors, traces, metrics, logs, analytics, privacy, sampling, cardinality, credential boundaries). Use whenever building or reviewing an app for Temps, configuring health checks, adding telemetry, diagnosing missing/noisy signals, or checking OTLP/Sentry/analytics ingestion. Triggers include "temps best practices", "prepare this app for Temps", ".temps.yaml health", "temps health check", "ignore health checks in otel", "temps observability", "wire up telemetry", and "instrument this app for temps". Prefer focused setup skills for a single SDK. Use temps-cli for executing deploy and resource-management commands.
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 implementing decoupled communication between nodes — global EventBus autoload with typed signals
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 writing production-grade GDScript — performance idioms, metaprogramming, @tool lifecycle, async pitfalls, signal/Callable trade-offs, profiler-driven idioms, and common pitfalls
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 debugging Godot projects — remote debugger, print techniques, signal tracing, common error patterns and fixes
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。
Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical (restaurant, healthcare, legal, home services, real estate, automotive). Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "local rankings", "service area", "multi-location", or "local search".
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。
Optimize content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. Generative Engine Optimization (GEO) analysis including brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization. Use when user says "AI Overviews", "SGE", "GEO", "AI search", "LLM optimization", "Perplexity", "AI citations", "ChatGPT search", or "AI visibility".
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。
Visual specialist for full audits. Captures screenshots, evaluates above-the-fold signals, and checks mobile rendering basics.
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。
Search Experience Optimization: reads Google SERPs backwards to detect page-type mismatches, derives user stories from search intent signals, and scores pages from multiple persona perspectives. Identifies why well-optimized pages fail to rank by analyzing what Google rewards for each keyword. Use when user says "SXO", "search experience", "page type mismatch", "SERP analysis", "user story", "persona scoring", "why isn't my page ranking", "intent mismatch", or "wireframe".
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。
Performance specialist for full audits. Measures CWV (LCP/INP/CLS), Lighthouse score signals, and emits deterministic report artifacts.
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。
Beta testing strategy for iOS/macOS apps. Covers TestFlight program setup, beta tester recruitment, feedback collection methodology, user interviews, signal-vs-noise interpretation, and go/no-go launch readiness decisions. Use when planning a beta, setting up TestFlight, collecting user feedback, or deciding if ready to launch.
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。
Growth skills for indie Apple developers — user acquisition, analytics interpretation, press/media outreach, community building, and indie business operations. Also covers the stage-by-stage store-growth audit and the post-launch store-signals loop. Use when user asks about growing their app, auditing growth levers, understanding metrics, getting press coverage, or running an indie dev business.
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。