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概要と使いどころ
Multi-project portfolio analytics dashboard. Aggregate KPIs across projects, track portfolio health, compare performance, and support executive decision-making.
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。
Monitor construction progress using computer vision. Analyze site photos and drone imagery to track work completion, detect safety issues, and compare against BIM models.
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。
Tendering and reporting in OpenConstructionERP: prepare tender BOQs, compare bids, manage risks, and generate reports (weekly, monthly, closeout). Use when a project goes to tender or needs reporting.
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。
Turns a screenshot, mockup, Figma export, or photo of a UI into working front-end code - React + Tailwind CSS v4 by default, or Next.js, Vue, or plain HTML/CSS - matching layout, spacing, colors, and typography, then renders the result and compares it against the image to close visual gaps. Use whenever the user shares an image of a website, app screen, dashboard, component, or wireframe and wants it built, cloned, recreated, or "made real", or says "code this up", "build this design", or "match this screenshot".
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。
Aggregate and analyze customer reviews from G2, Capterra, Trustpilot, App Store, and other platforms. Performs sentiment analysis, identifies pain points, extracts feature feedback, generates marketing claims, and compares competitor reviews. Use when users need review analysis, competitive intelligence, or customer feedback insights.
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。
Review contracts for concerning clauses, extract key terms, compare to standard terms, and flag unusual provisions. Use when user needs contract review, legal document analysis, or agreement evaluation.
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 to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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。
Formats review deliverables with consistent structure for comparable findings. Use when finalizing any review or analysis that must be shared or compared.
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。
Online (incremental) covariance, correlation, and precision estimation in Python — the streaming complement to sklearn.covariance. Use when code needs a covariance/correlation matrix updated per observation, recomputes np.cov/np.corrcoef in a rolling loop, must judge or compare covariance estimates, or proposes a new covariance methodology. Points to task-specific 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。
Rigorously and honestly assess a NEW or proposed covariance / correlation / precision estimator, or a new covariance scoring rule, using precise. Use when someone proposes, asks to evaluate, or wants to compare a covariance methodology. Covers implementing it to the contract, conformance, benchmarking against the registry, out-of-sample validation, and statistically defensible inference.
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。
Score and compare covariance estimates with precise's assessor panel. Use when you need to judge an estimate out-of-sample or rank competing estimators — and especially in high dimensions, where the plain held-out likelihood is misleading.
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 face_recognition to detect faces, extract landmarks and encodings, compare identities, run the face_recognition and face_detection CLIs, and troubleshoot dlib/model 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。
Routes substantive ML, AI, data, scientific-computing, and software-engineering requests to the smallest useful set of managed repository skills. Invoke proactively when a request names or implies a package, framework, model family, dataset, modality, workflow, backend, deployment target, evaluation method, or implementation approach that may benefit from repository guidance, even if no repository is named. Narrow progressively from area to family to repository root: inspect only the one or two most likely area pages; compare candidates by capability, task surface, model/data format, training versus inference versus evaluation intent, runtime constraints, and root-skill description; then open only the selected root and relevant sub-skills, references, or scripts. Select multiple repositories only when each adds a distinct capability. Do not load the whole collection, treat dependencies or incidental integrations as capabilities, choose by name alone, or force a match when no exact taxonomy family applies.
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。
Show Honey's benchmark scoreboard — the committed quality and token results per task tier (code, user-facing, agent-to-agent) from bench/. Reports only the reproducible committed figures, never invents per-repo numbers. Use when asked how much Honey saves, how it compares to Caveman / Ponytail / no-skill baseline, or for the headline numbers.
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。
Recover and review local human-AI conversations from Claude Code, Codex, opencode, Grok Build, and Cursor. Use when the user wants to revisit, search, extract, summarize, or compare work across Agent session stores, time ranges, projects, or named conversations. Follow the user's requested output; question clustering, problem-space mapping, artifact review, drift analysis, and insights are optional lenses.
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。
Compares PIA/DPIA methodologies: CNIL PIA tool, ICO DPIA template, NIST Privacy Framework, and ISO 29134. Provides methodology selection criteria based on regulatory jurisdiction, organisation maturity, processing complexity, and resource availability. Covers regulatory acceptance, tool features, and cross-methodology mapping. Keywords: PIA methodology, CNIL, ICO, NIST Privacy Framework, ISO 29134, DPIA comparison, assessment.
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。
Diff a live page's accessibility violations against a baseline — by default compares uncommitted changes (stash-based), or pass --branch [<name>] to diff against a branch. Reports only new violations introduced, violations fixed, and pre-existing count. Use `scan` for a full audit with no diffing.
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 should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
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。
Discover AAS skills for an explicit task and compare their complete instructions without installing them.
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。
Researches an open-ended question — options, possible solutions, prior art, trade-offs, or how something works — and produces a durable, evidence-backed, adversarially-validated report that recommends an option without committing the team to any artifact. Use when you want to research approaches, weigh options, survey prior art or the state of the art, or understand how something works before committing to a direction. Does not diagnose a bug, failure, or root cause — use investigate. Does not specify a feature — use plan-a-feature. Does not create or update a coding standard — use coding-standard. Does not compare two concrete artifacts for gaps — use gap-analysis. Does not assess an existing module's architecture — use architectural-analysis. Does not capture feedback on Han's own skills — use han-feedback.
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 architecture foundations before implementation. Use when asked to design or refactor architecture, choose Rust/Go crate, package, module, runtime, workflow, or service boundaries, compare mature project architecture, prevent stacked one-off PRs, audit migration debt in existing repos, or produce a spec before coding.
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。
Filter and screen stocks by financial metrics like P/E ratio, market cap, dividend yield, and growth rates. Analyze and compare stocks from CSV data.
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。
Comprehensive US stock analysis including fundamental analysis (financial metrics, business quality, valuation), technical analysis (indicators, chart patterns, support/resistance), stock comparisons, and investment report generation. Use when user requests analysis of US stock tickers (e.g., "analyze AAPL", "compare TSLA vs NVDA", "give me a report on Microsoft"), evaluation of financial metrics, technical chart analysis, or investment recommendations for American stocks.
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。
Google Search Console indexing assistant. Use when the user wants to track page indexing status, compare sitemap against GSC indexed pages, manage submission tracking, or move false positives (pages indexed per URL Inspection but not yet in GSC export) to the indexed list. Maintains two tracking files (indexed.md, to-index.md) with prioritization support.
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。