3,874 件 ・ 関連度順
概要と使いどころ
Generate educational practice questions from lecture notes to test student understanding. Use when users request practice questions, exam preparation materials, study guides, or assessment items based on lecture content.
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。
Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance 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。
Create or resume an evidence-based RPI implementation plan with an optional independent critique. Use when a task needs an implementation plan or an existing plan needs revision.
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。
Decision-driven HVE-Core installer with multiple clone-based and extension install methods, environment detection, and selective component installation
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 diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
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 LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests); use `write-code-eval`. To validate an existing judge, use `validate-evaluator`.
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。
Calibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with unit tests per `write-code-eval`).
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 custom browser-based annotation interface tailored to your data for reviewing LLM traces and collecting structured feedback. Use when you need to build an annotation tool, review traces, or collect human labels.
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。
Capture a website and create a HyperFrames video from it. Use when: (1) a user provides a URL and wants a video, (2) someone says "capture this site", "turn this into a video", "make a promo from my site", (3) the user wants a social ad, product tour, or any video based on an existing website, (4) the user shares a link and asks for any kind of video content. Even if the user just pastes a URL — this is the skill to use.
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 video compositions, animations, title cards, overlays, captions, voiceovers, audio-reactive visuals, and scene transitions in HyperFrames HTML. Use when asked to build any HTML-based video content, add captions or subtitles synced to audio, generate text-to-speech narration, create audio-reactive animation (beat sync, glow, pulse driven by music), add animated text highlighting (marker sweeps, hand-drawn circles, burst lines, scribble, sketchout), or add transitions between scenes (crossfades, wipes, reveals, shader transitions). Covers composition authoring, timing, media, and the full video production workflow. For CLI commands (init, lint, preview, render, transcribe, tts) see the hyperframes-cli 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。
Answer questions using the Arkon knowledge base. Searches wiki first, drills into raw sources only when needed. Supports quick, standard, and deep modes. Triggers on: what do we know about, find in KB, look up, query:, arkon query, search the wiki, tell me about, based on the KB.
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。
Reviews skill quality with evidence-based design rubrics and read-only batch inventories. Use for self-review, external skill or repository audits, and authorized auto-PR improvements. Separates design quality from measured task benefit and host compatibility.
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。
Transforms raw meeting transcripts into structured, evidence-based meeting minutes — merges multiple versions without losing content, reviews existing minutes against the transcript for missing items, and identifies anonymous speakers (Speaker 1/2/3, 发言人1). Triggers on 会议纪要, 会议记录, 整理纪要, 妙记转纪要, "write meeting minutes", "merge these minutes". Not for fixing ASR/STT errors first (use transcript-fixer).
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。
Runs a 6-dimension evidence-based health audit of this skills marketplace repo — code safety, docs/SSOT, security/PII, PR/issue triage, manifest integrity — via a parallel workflow, then verifies findings by priority. Use for 全面体检 / 检查仓库状态 / 审计一下仓库, a pre-release sweep, or "is this repo OK" even without saying "workflow". Not for building marketplace.json (use marketplace-dev) or cache drift (use skill-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。
Clones and audits competitor repositories into evidence-based intelligence with file:line citations. Use to track competitors, add a competitor repo, review competitor code, compare capabilities, build a competitor landscape, or check for code updates: 竞品分析 / 竞品 / competitor scan / analyze competitor / compare with X / latest competitor code. Not for market research without code (use deep-research).
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。
Run a fast, honest competitive scan — the dimension table built from public evidence (sites, docs, pricing pages, changelogs, reviews), the claims-vs-observed discipline, and the so-what synthesis that ends in moves, not a landscape mural. Use when asked what are competitors doing, quick scan of these three rivals, how does our pricing/feature set compare, or prep the competitive slide honestly. Produces the evidence-based comparison table, the marketing-vs-reality flags, the so-what synthesis, and the staleness date.
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 a tailored code review checklist for any pull request based on the language, type of change, and risk level. Use when asked to review code, check a PR, review a pull request, or generate a code review checklist. Produces a focused checklist with language-specific checks, risk-level-appropriate depth, and a clear approve/request-changes recommendation.
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。
Prepare a promotion defence (晋升答辩) for a level-based promotion process common at Chinese technology companies: the materials, the presentation structure, the evidence for the next level, and a rehearsal of the committee's questions. Use when asked 帮我准备晋升答辩, 晋升述职, 晋升 PPT, how do I prepare for my promotion committee, or 答辩问题. Produces a level-gap analysis against the target level's criteria, the defence presentation outline in Chinese, a timed talk track, and a question bank with answer points, then a mock committee round.
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。
Assess physical and transition climate risk for a site, product, or portfolio with scenario-based structure. Use when asked to run a climate risk assessment, evaluate physical or transition risk, prepare TCFD/ESRS-style climate risk analysis, or assess how climate scenarios affect an asset or business. Produces a hazard-exposure-vulnerability matrix across 2030/2040/2050 horizons and labelled scenarios, with financial-impact ranges, confidence levels, and adaptation options.
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 interview notes into a structured candidate scorecard and hire recommendation. Use when asked to write an interview scorecard, a candidate evaluation, an interview debrief, or to summarize feedback into a hire/no-hire call. Produces a per-competency assessment with evidence and ratings, an overall recommendation with confidence, and the open questions for the next round — evidence-based, bias-aware, and decision-ready.
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。
Cut through the model soup to buy the right appliance — the features that actually matter for you, the ones that are marketing, and when to buy. Use when asked which [fridge/washer/dishwasher] should I buy, help me choose an appliance, what features do I need, or is this appliance worth it. Produces a needs-based feature shortlist (must-have vs nice-to-have vs marketing fluff), fit and capacity checks, reliability and running-cost considerations, warranty/extended-warranty guidance, and timing tips — flagging to verify current models, prices, and specs before buying.
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。
Octane bindings for TanStack DB. useLiveQuery hook with dependency arrays (8 overloads: query function, config object, pre-created collection, disabled state via returning undefined/null). useLiveSuspenseQuery for Octane Suspense with Error Boundaries (data always defined). useLiveInfiniteQuery for cursor-based pagination (pageSize, fetchNextPage, hasNextPage, isFetchingNextPage). usePacedMutations for debounced Octane state updates. Return shape: data, state, collection, status, isLoading, isReady, isError. Import from @octanejs/tanstack-db (re-exports all of @tanstack/db).
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 users navigate product failure by diagnosing root causes, evaluating conviction, and executing successful pivots or turnarounds based on proven industry frameworks.
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 users build robust infrastructure for measuring, monitoring, and iterating on AI product performance using human, code-based, and LLM-as-a-judge methodologies.
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。