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概要と使いどころ
CesiumJS entities and data sources - Entity, EntityCollection, DataSource, GeoJsonDataSource, KmlDataSource, CzmlDataSource, Graphics types, PathGraphics, PathMode, Visualizers. Use when adding points, labels, models, polygons, polylines, or time-segmented paths, loading GeoJSON/KML/CZML/GPX data, or working with the high-level Entity API.
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。
PM skill for Claude Code, Codex, Cursor, and Windsurf. Diagnoses SaaS metrics, critiques PRDs, plans roadmaps, runs discovery, coaches PM career transitions, pressure-tests AI product decisions, and designs PLG growth strategies. Seven knowledge domains, 12 templates, 40+ frameworks, and an opinionated interaction style that labels assumptions and names tradeoffs.
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。
UX-тексты на русском языке: кнопки, формы, ошибки, пустые состояния, онбординг, подтверждения, уведомления, пуши и микрокопия. Use this skill whenever the user asks to write, rewrite, review, choose, or improve interface copy, product messages, UI strings, transactional emails, push notifications, toasts, snackbars, forms, empty states, onboarding, errors, confirmations, labels, tooltips, or button text. Триггеры: «текст для кнопки», «сообщение об ошибке», «текст в форму», «пустое состояние», «подпись к полю», «текст тоста», «снекбар», «онбординг», «пуш-уведомление», «микрокопия», «UX-текст», «что написать на экране…», «лейбл поля», «тултип», «подтверждение удаления», «письмо-уведомление». Для статей и постов см. skills statya и redaktura; для проектного словаря — redpolitika.
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 a rough idea, a bug, or a batch of unsolved problems into well-structured GitHub issue(s) and file them with `gh`, auto-classifying type + labels from the content (recommend, then confirm) and following Rove's conventions (beginner-friendly framing, concrete file pointers, an acceptance checklist, zero AI/Anthropic attribution). Handles single, batch, and "file the unsolved problems" modes. Use when the user says "file an issue", "open a GitHub issue", "draft a good first issue", "batch these as issues", "上报 issue", "提个 issue", "报个 bug", "把没解决的发上去", or wants to hand low-priority tasks to outside contributors. Distinct from the daemon-owned internal issue store (`rove api issue-*`); this skill is for outward-facing GitHub issues.
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。
Transcribes and analyzes speech with the Deepgram API: pre-recorded audio, real-time streaming, text-to-speech and voice agents. Use when a user asks to convert speech to text, implement real-time transcription, add speaker labels or summaries to a recording, detect languages, build a voice agent with turn detection, or integrate Deepgram SDKs (JavaScript or Python).
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。
AssemblyAI is a hosted speech-to-text API that transcribes audio and video files or live streams and adds speaker labels, sentiment, entity detection, PII redaction and LLM analysis of the transcript. Use when a user asks to transcribe a recording, label who said what, analyze call sentiment, redact personal data, stream live transcription, or summarize and question a transcript with LLM Gateway (the replacement for LeMUR).
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 whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".
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。
Assign items to a CLOSED label vocabulary that is too large to put in a prompt — product taxonomies, category hierarchies, tag vocabularies, routing tables, ICD/SIC-style code lists. A cheap model writes the label it thinks the vocabulary would use, and an embedder snaps that writing onto the nearest legal value, so the schema is never transmitted and the output is always in-vocabulary. Use for "classify these into our taxonomy", "tag these against the existing tag list", "map these queries to categories", "the enum is too big to send", or a Literal/enum that hits a provider cap. NOT for a vocabulary that fits in a prompt — structured output measured 0.701 acc@1 there against this pattern's 0.564. NOT for open-ended labelling with no fixed vocabulary, and not for ranked retrieval over documents (bm25).
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。
Text-prompted image zone detection using TIPSv2 B/14 on CPU. Produces `focus_targets` / `focus_edges` bbox lists from natural-language labels, ready to feed into `svg-portrait-mode`. Use when you want automatic foreground/background separation from prompts like "dog face" + "wooden floor" instead of hand-annotating bboxes.
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 frontend with a deliberate visual direction held through palette, typography, structure, and texture, with on-screen strings that name real information rather than fabricated demo data or filler labels. Use when building or restyling a frontend.
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。
Cluster a list of keywords into topical groups with search intent labels, validated search volume, KD, and CPC data. Use this skill whenever a user provides a list of keywords (pasted, uploaded as CSV/Excel, or via Google Sheet URL) and asks to cluster, group, map, organize, or categorize them. Also trigger when a user says "keyword cluster", "cluster my keywords", "group these keywords", "keyword map", "keyword strategy from this list", "organize keywords by topic", or pastes or uploads a list of keywords and wants structure from it. Always use this skill — do not attempt keyword clustering manually without following this workflow.
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。
Applies the existentialist and feminist philosophy of Simone de Beauvoir, French philosopher and author of The Second Sex. Reach for this skill whenever the user is grappling with existential dilemmas, moral ambiguity, gender dynamics, societal expectations, or relationship ethics. Trigger this skill for questions about freedom versus responsibility, bodily autonomy, marginalization, or when the user feels trapped by external labels or "immanence." Use this to help the user reclaim their agency, recognize the social construction of their situation, and make authentic choices that expand their transcendence while respecting the interconnected freedom of others.
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。
Make action labels explain what happens next.
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 reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark 6.0 (org.neo4j.connectors:spark) or 5.x (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-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。
Diagnoses and fixes slow Neo4j Cypher queries by reading execution plans, identifying bad operators (AllNodesScan, CartesianProduct, Eager, NodeByLabelScan), and prescribing fixes (indexes, hints, query rewrites, runtime selection). Use when a query is slow, when EXPLAIN or PROFILE output needs interpretation, when dbHits or pageCacheHitRatio are poor, when cardinality estimation diverges from actuals, or when deciding between slotted/pipelined/parallel runtimes. Covers USING INDEX / USING SCAN / USING JOIN hints, db.stats.retrieve, SHOW QUERIES, SHOW TRANSACTIONS, TERMINATE TRANSACTION. Does NOT write new Cypher from scratch — use neo4j-cypher-skill. Does NOT cover GDS algorithm tuning — use neo4j-gds-skill. Does NOT cover index/constraint creation syntax details — use neo4j-cypher-skill references/indexes.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。
Use when you write any words a user reads in a mockup or a hand-off - content language and voice, buttons, labels, errors, empty states, confirmations, numbers and dates, and the longest-string check
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 building or changing any interactive mobile UI - VoiceOver/TalkBack labels, grouping, headings, focus order, Dynamic Type/font scale, reduce motion, contrast, and the automated check for each stack.
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 on every UI change - semantic HTML, labels for controls, keyboard-navigable dialogs/menus, visible focus, and never color as the only signal
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 methods a financial-ML result has to survive before it is evidence — purged cross-validation with an embargo, triple-barrier labelling, sample uniqueness under overlapping labels, fractional differentiation, meta-labelling, and multiple-testing correction. Written because the invariants were required of quant-researcher and nothing in the project explained how to satisfy them: a rule without a method produces either an invention or a block. Applied whenever a backtest, a feature or a label is being designed or judged.
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 improve the accessibility of the timepicker-ui LIBRARY (app/src) - ARIA, keyboard operability, focus trap/management, screen-reader announcements, labels - against WCAG 2.2 AA and the WAI-ARIA Authoring Practices. Use when the user wants an a11y audit/fix of the picker itself (clock, wheel, plugins, modal, inputs). Drives the accessibility subagent in an audit → approve → apply flow. Does NOT cover docs-app (use /docs-a11y for 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。
飞书邮箱 — draft, compose, send, reply, forward, read, and search emails; manage drafts, folders, labels, contacts, attachments, and mail rules. Use when user mentions 起草邮件, 写一封邮件, 拟邮件, 草稿, 发通知邮件, 发送邮件, 发邮件, 回复邮件, 转发邮件, 查看邮件, 看邮件, 读邮件, 搜索邮件, 查邮件, 收件箱, 邮件会话, 编辑草稿, 管理草稿, 下载附件, 邮件文件夹, 邮件标签, 邮件联系人, 监听新邮件, 收信规则, 邮件规则, draft, compose, send email, reply, forward, inbox, mail thread, mail rules.
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。
Extract text from images using OCR. Use when the user shares a screenshot and you need to read the text content, copy UI labels, or extract copy from a design mockup.
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。
Help return an item or request a refund from any retailer. Identifies the item, finds the return policy, navigates the process, and handles shipping labels or phone calls.
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。
Maintain HitKeep dashboard localization. Use whenever a contributor adds or changes user-visible dashboard text, Transloco keys, locale JSON files, OptimusUI locale behavior, language switching, translated labels, localized layout, date/number/duration formatting, or supported dashboard languages.
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。