7,075 件 ・ 関連度順
概要と使いどころ
Updates a project changelog with entries for changes since the branch diverged from main. Use when about to open a PR, cutting a release, or when the user asks about changelog, release notes, or migration notes.
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。
Builds economic profit (EVA) models with capital charge calculation, value spread analysis, and long-term value creation measurement. Use when calculating economic profit, analyzing EVA trends, or measuring value creation.
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。
Structures operating partner involvement with portfolio company assignment, initiative tracking, and impact measurement. Use when coordinating operating partners, tracking operational initiatives, or measuring portfolio support impact.
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。
Evaluates assumption, rejection, and assignment decisions for executory contracts and unexpired leases in bankruptcy. Use when analyzing contract decisions, evaluating lease rejections, or modeling cure cost exposure.
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。
Measures implementation shortfall with paper portfolio comparison, delay cost attribution, and execution quality assessment. Use when measuring implementation shortfall, analyzing execution quality, or attributing trading costs.
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。
Validate whether a proposed new artifact is feasible in the current stack before creator workflows run.
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 want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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。
日本語の文書を、公的ガイドライン準拠の「やさしい日本語」に書き換えるスキル。 「やさしい日本語にして」「やさしい日本語に翻訳して」「外国人にもわかるように書いて」 「平易な日本語にして」「N4レベルの日本語にして」「easy Japanese にして」などで発動する。 「やさしい日本語かチェックして」「判定だけして」「スコアを出して」なら書き換えずに判定だけ行う。 対象はお知らせ・案内・手続き説明・マニュアルなど、在留外国人や日本語学習者にも 読んでほしい説明文。根拠は「在留支援のためのやさしい日本語ガイドライン」 （出入国在留管理庁・文化庁、2020）。二重否定・受身・推測表現・敬語・表記を textlint と prh 辞書で機械検出し、語彙レベル（既定 N4 相当・推定値）を同梱リストで機械判定する。 明晰化（meiseki）とは対象読者が違う。日本語ネイティブ向けの推敲は meiseki を使う。
Prepare and ship an iOS build to TestFlight — runs the submission pre-flight checks, fixes the config that blocks uploads/processing, then walks internal→external beta review. Use when releasing an iOS build to testers.
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。
Set up Android app signing safely — generate an upload keystore, enroll in Play App Signing, and wire a Gradle release signingConfig that reads secrets from a git-ignored keystore.properties / CI (never committed). Use when configuring signing for a Play release.
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。
Pre-submission audit for an App Store release — runs the pre-flight checks and walks the three privacy surfaces, the high-frequency App Review rejection guidelines, and metadata/release-option choices. Use before submitting an iOS app for App Review.
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 the ClinicalTrials.gov registry through its version 2 REST API for interventional and observational studies, their phases, enrolment, endpoints, sponsors, and posted results. Use this skill to survey who is developing what against an indication, date a competitor's programme, read primary and secondary outcome measures, find eligibility criteria, and distinguish a study that completed from one that was terminated or withdrawn. Also trigger on ClinicalTrials.gov, NCT number, trial registry, study phase, enrolment, primary outcome measure, recruiting status, trial sponsor, or competitive landscape.
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。
Exposes Claude's reasoning chain as an auditable, decomposable artifact. Quick mode (default) gives assumption inventory + weakest-link in 2 stages. Full mode (--full) adds decision branching, confidence decomposition, and falsification conditions. Triggers on "왜 그렇게 생각해", "reasoning", "근거", "show your work", "어떻게 그 결론이", "trace", "판단 근거", "why do you think that".
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。
Academic research skill for Biblical Hebrew, Semitic linguistics, cuneiform studies, and comparative Ancient Near Eastern research. Provides Sefaria API for Hebrew Bible, CDLI/ORACC for cuneiform databases, and web discovery via Omnisearch, Exa, Firecrawl, and Obscura for finding scholarly sources across JSTOR, Perseus, Persée, Google Scholar, and academia.edu. Triggers on Hebrew quotes, cuneiform, Sefaria, ANE research, Minoan, search for scholarship, find papers, literature review, scholarly search, academic search, Genesis/Tehom, Ugaritic, Talmudic sources, extract from PDF, OCR academic.
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 execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout.
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。
Log a bias-log / vipassana observation to the memex store the moment a reasoning pattern surfaces, mid-session or at close. Use when you catch a bias in your own reasoning (anchoring, confirmation, trust-the-summary), when a pace tell or verification gap appears, or when meditate/observe produces a reflection worth persisting across sessions. Primary path pipes the body to `memex add` over stdin (`--title` required) then runs the `meditate-vipassana` extractor to make it queryable; secondary path appends a numbered bullet to `docs/OBSERVATIONS.md`.
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。
Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.
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。
Add an external probabilistic classifier to a decision path without letting it take the path over. Covers finding the externally-graded rows you are already logging, measuring the confidence separation between the overrides it got right and the ones it got wrong, choosing an operating point your data licenses, failing open at the call site, and proving the whole arrangement offline with no API key. Applies to any service returning a score you can order — TypeSafe AI's System One models (Jev) are the worked example. Most of the method transfers to a local model or a second heuristic, with the parts that do not called out where they arise. Use when a rule-based path is wrong often enough to hurt, when someone proposes replacing a heuristic with a model, when a confidence threshold needs a defensible value, or when an oracle already in production has never been graded.
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 a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving consistency, serving low-latency features for real-time inference, reusing feature definitions across projects, or building a feature catalog for discovery and governance.
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。
Creation and genesis — generative ideation from void, structured emergence from ambiguity, and the discipline of bringing new patterns into existence. Maps Brahma's creative power to AI reasoning: transforming cleared space into coherent new structures, seeding possibilities, and nurturing nascent ideas through their fragile early stages. Use after shiva-bhaga dissolution has cleared stale patterns, when facing a genuinely novel problem with no obvious template, or when incremental modification has reached its limits and a fresh design is needed.
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。
Address unwanted dog behaviors through desensitization, counter-conditioning, and management. Covers reactivity (to dogs, people, sounds), separation anxiety, resource guarding, excessive barking, and leash pulling using force-free methods with systematic threshold management. Use when a dog shows reactivity toward other dogs, people, or stimuli; when separation anxiety causes destructive behavior or vocalization; when resource guarding is present; or when behaviors like excessive barking or leash pulling interfere with daily life — after basic obedience is established.
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。
AI situational awareness — internal threat detection for hallucination risk, scope creep, and context degradation. Maps Cooper color codes to reasoning states and OODA loop to real-time decisions. Use during any task where reasoning quality matters, when operating in unfamiliar territory, after detecting early warning signs such as an uncertain fact or suspicious tool result, or before high-stakes output like irreversible changes or architectural decisions.
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 WIT (Writing Is Thinking) as a human–LLM collaborative scientific reasoning skill for scientific question formulation, finding-driven research planning, next-experiment selection, Results or Discussion review, claim–evidence and reviewer stress tests, manuscript logic audits, deadline closure, and researcher growth. Use when the user invokes WIT or asks for these question-driven scientific reasoning workflows; do not use for generic copyediting, summarization, or literature search alone.
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。
Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and 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。