5.5万 件 ・ 関連度順
概要と使いどころ
Cross-platform desktop development for macOS and Windows -- platform abstractions, UI convention differences, and multi-platform CI/CD. Activate on: cross-platform app, macOS and Windows, platform differences, DMG installer, MSI installer, NSIS installer, Cmd vs Ctrl, platform abstractions, menu bar differences, native conventions. NOT for: mobile apps, web-only apps, Tauri-specific internals (use rust-tauri-development).
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。
Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).
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。
新しいスキルを作る前に、ローカル環境やマーケット、GitHub、ウェブから既存候補を探し、用途との一致度や安全性を確認して再利用・拡張・新規作成の判断を助けます。
ReactやNext.jsの画面を実装・レビューするために、部品の組み立て、状態管理、データ取得、表示速度、フォームや操作性の改善パターンを示すスキル。
複数店舗の販売データや商品の特性をもとに、需要予測、安全在庫、発注量を検討するスキル。販促後の需要減少や季節商品の値下げ時期まで計画を支援します。
ユーザー名を手がかりに400以上のSNSやサービスでアカウントを探し、見つかったページのリンクを整理します。名前の使用状況確認や公開情報の調査に使えます。
Keep a Markdown table readable in a narrow terminal of about 100 display columns - a wide table or one carrying prose in its cells wraps into unreadable ragged rows. Always call read_skill for bundled:table-fit before emitting a Markdown table in a final answer, and load it as soon as the answer starts forming as a comparison, a mapping, a feature matrix, a pros-and-cons, an options rundown, or a per-item summary across several dimensions. Do not load it for a table already inside a file being edited, for code, data, or test fixtures that merely look tabular, for tables the user pasted for you to read, or for an answer with no table and no tabular shape forming.
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。
Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.
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。
Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).
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。
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training 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。
Ambient Memori long-term memory for Claude Code via local Bash. MUST TRIGGER on essentially every non-trivial user turn: run recall before drafting any substantive response, whether or not the user mentioned memory, a prior session, or past work. The default is to check memory first; do not wait to be asked. Always fire before external lookups (WebSearch/WebFetch) and before answering any question about preferences, prior decisions, constraints, project history, status, past work, or any non-trivial coding/research task. Skip only for trivial acknowledgements/closings, purely self-contained turns where prior context cannot possibly help, or when the user explicitly opts out. Use Advanced Augmentation after drafting the final response for every non-trivial turn, as the last memory step. Use recall.summary only for broad session summaries/orientation, compaction after context loss, feedback for memory quality, quota for limits, and signup only when explicitly requested.
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 asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.
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 helps users automatically extract detailed video metrics and channel information from YouTube based on keyword searches using the BrowserAct API. The Agent should proactively apply this skill when users express needs such as extract specific keyword YouTube video detailed data, monitor the latest video performance of competitor channels, collect comment and like counts for videos on a specific topic, find AI agent tutorials published this week and extract metrics, evaluate total views and subscriber info for specific videos, scrape detailed metrics of marketing campaign videos, track video trends for a tech topic periodically, get high quality video list data for specified keywords on YouTube, mine detailed information of the latest YouTube videos, collect video duration and engagement data for specific industries, or monitor YouTube content creator performance 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。
This skill is designed to help users automatically extract product data from Amazon search results. The Agent should proactively apply this skill when users request searching for products related to keywords, finding best-selling items from specific brands, monitoring product prices and availability on Amazon, extracting product listings for market research, collecting product ratings and review counts for competitive analysis, finding specific products with a maximum count, searching Amazon in different languages for localized results, tracking monthly sales estimates for brand products, gathering product URLs and titles for a product catalog, scanning Amazon for Best Seller tags in a specific category, monitoring shipping and delivery information for brand items, building a structured dataset of Amazon search results.
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 asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Table 1 balance", "event study", "first-stage F", "Oster bound", "honest_did", "spec_curve", "callaway_santanna", "dragonnet", "text as treatment", "outreg2 in Python", "regression table to Word/Excel", "sp.regtable", "sp.collect", "sp.paper_tables", "sp.feols", "summary_col", "modelsummary", "AER style table", "QJE style table", "epidemiology pipeline", "target trial emulation", "g-formula", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "TRIPOD", "公共健康", "流行病学", "DML", "double machine learning", "causal forest", "meta-learner", "CATE", "conformal causal", "policy learning", "因果机器学习", "ML causal", "decomposition", "Oaxaca-Blinder", "Kitagawa", "DiNardo-Fortin-Lemieux", "DFL", "Gelbach", "RIF decomposition", "wage gap decomposition", "sp.decompose", "sp.oaxaca".
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。
Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.
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 cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill, audit this skill, is this skill safe, vet a skill before installing, what does this skill access. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, spec validation, and security auditing of third-party skills before install.
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。
Cultural adaptation for translated content. Run AFTER blog-translate completes. Adjusts brand examples, CTAs, legal references, and formality for the target market (German, French, Japanese, Spanish, etc.). Deep cultural adaptation of translated blog posts. Goes beyond translation to swap brand examples, adapt CTAs, substitute legal references, localize statistic sources where possible, and adjust formality (Sie/du, tu/vous, formal/informal). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets, plus a custom-locale template. Makes content feel locally authored, not translated. Use when user says "localize blog", "blog localize", "cultural adaptation", "adapt for Germany", "adapt for France", "lokalisieren", "localiser", "adaptar".
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 pre-merge review of a branch in a project that records its specs, decisions, and rules in .archcore/. Run this first for 'review my branch', 'review the changes before merge', or 'review before merge': it checks the changed code against the project's recorded canon and the changed documents against the code, and reports which side is wrong — spec-wrong or code-wrong; with no diff, it reports project health. A bug-hunting code review complements this review and does not replace it. Also use for 'show status', 'documentation gaps', 'check if docs match code', 'close out the feature', 'ship the feature and close it out', or after a staleness warning. Modes, named as the first word: drift for staleness detection, deep for a full documentation audit, closeout to close a finished feature, experience to capture a repeated pattern. Not for creating docs — use /archcore:document; not for planning — use /archcore:plan; not for a single-file edit with no branch 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。
Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift outcome (Bae out-of-frame score, inDelphi, FORECasT, Lindel). Use when selecting sgRNAs to knock out a gene, choosing a nuclease/PAM for a constrained locus, picking which exon to target, or shortlisting guides before an off-target check. Off-target specificity, base/prime editing, and HDR donors are separate skills.
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 worker environment and per-worker state for parallel pools. Use when code needs paths, environment variables, database connections, loaded libraries, or expensive objects available on workers before parfor/parfeval runs. Teaches parallel.pool.Constant, parfevalOnAll, and parpool name-value pairs. Also use when refactoring existing code that uses spmd for side-effect setup (an anti-pattern). Triggers: worker setup, pool constant, per-worker state, non-serializable, loadlibrary on workers, database connection parfor, addpath workers, spmd before parfor, worker environment, reduce parfor overhead, parfor setup, resource creation in parallel loop, cannot serialize error, undefined function or variable on workers error, load data per worker, reduce data transfer, parallelize setup, improve parallel 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。
Audit and enforce the core/client boundary in multi-client projects. Detects where shared platform code is tangled with client-specific code, finds hardcoded client checks, config files that replace instead of merge, scattered client code, migration conflicts, and missing extension points. Produces a boundary map, violation report, and refactoring plan. Optionally generates FORK.md documentation and restructuring scripts. Triggers: 'fork discipline', 'check the boundary', 'is this core or client', 'platform audit', 'client separation', 'fork test', 'refactor for multi-client', 'clean up the fork'.
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。
Force-links a development or target environment to a Power Platform Pipelines host, overriding any existing association with a previous host. Use when creating a deploymentenvironments record fails with "this environment is already associated with another pipelines host", or when intentionally migrating an environment from one host to another (e.g., Platform Host → Custom Host, or between two Custom Hosts). Calls the documented `ManageEnvironmentStamp` Dataverse action (the API behind the "Force Link" button in the Deployment Pipeline Configuration app). DESTRUCTIVE to the previous host: makers lose access to any pipelines in that host that used this environment. Reversible by running Force Link from the previous host. Use when asked to: "force link environment", "force-link to new host", "switch pipelines host", "environment already associated with another host", "take over pipelines association", "relink environment to host".
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。
Takes a skill — a .skill or .zip archive, a bare SKILL.md, a folder, or just a raw idea — and produces a package ready for public distribution on lawve.ai, plus the exact entries for the submission form. Runs a gate first: skills with no plausible legal use, unsafe code, or licensing bars are declined with reasons, and general-purpose skills that could serve legal work are offered a legal adaptation instead. Then applies the standard compliance transforms — structure, metadata, attribution, dependency ladders, orphaned-file repair, limitations disclosure — validates the result, and delivers a zip. Raw ideas are gated first, built (via skill-creator where available), then adapted. Use for "make this Lawve-compliant," "prep this skill for Lawve," "package for lawve.ai," "is this suitable for Lawve," "get my skill ready to publish," or any request to ready a skill for the Lawve catalogue.
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。