726 件 ・ 関連度順
概要と使いどころ
Adapt a classroom task for specific learner needs while preserving the core learning objective intact. Use when differentiating for SEND, EAL, gifted, ADHD, dyslexia, or anxiety.
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Tags harness-decomposed curriculum items (KUDs, LTs, criteria) with a school's developmental band metadata while preserving source voice and labels. Supply the band schema; the skill derives mapping rules from it.
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。
Adapt a historical primary source for classroom use — modifying complexity and length while preserving features for sourcing, close reading, and corroboration. Use when a primary source is too complex for the target age group.
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。
Simplify recently changed code for clarity, reuse, quality, and efficiency while preserving behavior. Use for tidy/refactor passes; use ce-debug for bugs.
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。
Control the user's real Safari browser on macOS through AppleScript, page JavaScript, screenshots, and carefully verified System Events input. Use when an agent needs to inspect or operate the user's existing Safari tabs and login sessions, including reading pages, navigating, clicking, filling non-sensitive forms, taking screenshots, observing page-level fetch/XHR metadata, or troubleshooting Safari-specific UI behavior.
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。
Structural editor that proposes cuts, reorganization, and simplification while preserving comprehension. Use when user requests structural review or editorial review of structure
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。
Implement bounded features and fixes in existing or new mini-program codebases while preserving repository rules, user-owned changes, approved behavior, assets, framework conventions, and evidence boundaries. Use when users ask to write or modify mini-program code, implement a confirmed specification or architecture, add a scoped feature, remove behavior semantically, update internal documentation required by a change, or carry out a well-defined small fix that does not require root-cause investigation. Establishes a baseline and change boundary, applies test-driven small steps, distinguishes source from generators and build artifacts, verifies the affected contract, and never reports source completion as device validation, formal acceptance, upload, or 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。
Design, preview, implement, and assess mini-program interfaces against an approved prototype, screenshot, visual reference, design system, or bounded UI request while preserving product semantics and existing accepted behavior. Use when users ask to reproduce a mini-program screen, refine visual hierarchy, adapt layouts across device sizes, handle safe areas or keyboards, inspect touch targets, scrolling, media, or gesture conflicts, compare an implementation with a reference, or prepare a device-verification matrix. Separates proposed visual previews, user approval, source integration, local checks, and real-device evidence; never invents product functionality for visual completeness or claims device validation without device-specific proof.
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。
How a downstream app consumes the shared @kolu/surface stack (@kolu/surface · surface-app · surface-remote · surface-mcp) — declaring a typed reactive surface, serving it, consuming it (SolidJS hooks or a CLI), and mirroring a remote surface over ssh. Grounded in the real consumers: kolu, drishti, odu, and the TUIs. Load when wiring a surface server/client/mirror, or reaching for makeSession / a link / the `.use()` hooks. CHANGING the framework is gated separately — `.claude/rules/surface.md` (a paired, CI-green drishti PR pinned to final kolu HEAD).
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。
Delegate long-context analysis, research, and large-document summarization to Google Gemini CLI (1M-token context) via non-interactive `gemini -p`, preserving Claude context. Use when the user says 'use gemini'/'hand off to gemini' or a task needs 100K+ tokens of context.
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 Claude into a CFA-level financial analyst for specialty finance, regulatory compliance, and industry-specific analysis. Use when performing private credit pricing, insurance reserving, FP&A variance analysis, wealth planning, restructuring recovery analysis, real estate valuation, venture capital analysis, ESG assessment, regulatory capital analysis (Basel III), compliance reporting (MiFID II, GIPS), credit derivatives (CDS, CVA), convertible bond analysis, lease accounting (ASC 842/IFRS 16), pension funding/LDI, sovereign risk assessment, real option valuation, equity research (SOTP, target price), commodity spread trading, treasury operations, PPP infrastructure modelling, onshore fund structuring, offshore fund structuring, transfer pricing analysis (BEPS, Pillar Two), tax treaty optimisation, FATCA/CRS compliance, economic substance analysis, regulatory reporting (AIFMD, Form PF), AML/KYC compliance, crypto/DeFi analysis, municipal bond analysis, structured products, trade finance, fund of funds (J-curve, commitment pacing, manager selection, secondaries), bank analytics (NIM, CAMELS, CECL, deposit beta, loan book), carbon markets (credit pricing, ETS, CBAM, offsets, shadow carbon price), or private wealth (concentrated stock, philanthropic vehicles, wealth transfer, direct indexing, family governance) analysis is required. Pairs with corp-finance-mcp tools for computation.
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 the corp-finance-mcp server tools for specialty finance, regulatory, and compliance calculations. Invoke when performing private credit (unitranche, direct lending, syndication), insurance (loss reserving, premium pricing, Solvency II SCR), FP&A (variance analysis, break-even, working capital, rolling forecast), wealth management (retirement planning, tax-loss harvesting, estate planning), restructuring (recovery analysis, distressed debt), real assets (property valuation, project finance), venture capital (dilution, convertible instruments, fund returns), ESG (scoring, climate/carbon, green bonds, SLL), regulatory capital (Basel III, LCR/NSFR, ALM), compliance (MiFID II best execution, GIPS reporting), credit derivatives (CDS pricing, CVA/DVA), convertible bonds (binomial tree pricing, scenario analysis), lease accounting (ASC 842/IFRS 16, sale-leaseback), pension & LDI (funding analysis, liability-driven investing), sovereign risk (bond analysis, country risk), real options (binomial valuation, decision trees), equity research (SOTP, target price), commodity trading (spread analysis, storage economics), treasury management (cash management, hedge effectiveness), infrastructure finance (PPP models, concession valuation), crypto (token valuation, DeFi analysis), municipal bonds (pricing, credit analysis), structured products (notes, exotic), trade finance (LC, supply chain), fund structuring (US onshore, UK/EU, Cayman/BVI offshore, Luxembourg/Ireland), transfer pricing (BEPS/Pillar Two, intercompany pricing), tax treaty (treaty network optimization, holding structures), FATCA/CRS (reporting, entity classification), economic substance (multi-jurisdiction testing), regulatory reporting (AIFMD Annex IV, SEC Form PF, CFTC CPO-PQR), AML compliance (KYC risk scoring, sanctions screening), fund of funds (J-curve, commitment pacing, manager selection, secondaries pricing), bank analytics (NIM analysis, CAMELS rating, CECL provisioning, deposit beta, loan book), carbon markets (credit pricing, ETS compliance, CBAM, offset valuation, shadow carbon price), private wealth (concentrated stock, philanthropic vehicles, wealth transfer, direct indexing, family governance). All computation uses 128-bit decimal precision.
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。
Plan and execute canary releases for model updates — traffic splitting, rollback triggers, success metrics. Use when asked to "canary the new model", "roll out this model safely", or "set up a traffic split for a model update".
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 or audit a feature store — serving, freshness, and sharing across models. Use when asked "do we need a feature store", "design a feature store", or "share features across 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。
Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to "build ML model", "train a model", "prediction pipeline", "classification", or "regression".
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。
Convert ChatGPT, Gemini, Grok, and Claude share links to clean Markdown + HTML. Use when archiving AI conversations, preserving code fences, or publishing transcripts to GitHub Pages.
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。
Bias detection and mitigation, fairness metrics, privacy frameworks, consent models, transparency requirements, and accountability structures for data science practice. Covers algorithmic bias sources, disparate impact testing, differential privacy, GDPR principles, model cards, datasheets for datasets, responsible AI frameworks, and the organizational governance needed to make ethics actionable. Use when auditing models for bias, designing privacy-preserving systems, establishing governance processes, or evaluating the social impact of data-driven 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。
Use before you shorten, compress, or rewrite the BODY of an existing SKILL.md or agent .md — the moment you are about to cut text to reduce length. First inventory the file's operational anchors (exact code / command / API snippets, fail-closed workflow guards, and rule / threshold statements), preserve every one, then judge the rewrite by expected downstream task cost (exploration, debugging, recovery tokens) rather than by the resulting line count. A shorter skill that strips an anchor makes the agent MORE expensive per task, so length is never the objective and rewrite is not compression. Backed by Preserving Operational Anchors When Compressing Agent Instructions (arxiv 2606.09421v2). Triggers on shortening or refactoring a skill or agent body.
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 MaaFramework Pipeline nodes and recognition snippets from screenshots or observed UI state. Use for OCR node generation, ROI sweep, choosing TemplateMatch/OCR/ColorMatch/CustomRecognition, preserving target-file schema style, and designing `next`/`[JumpBack]` links before merging generated nodes into pipeline JSON.
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。
Quotient redundant skill paths via coequalizers, preserving GF(3) conservation
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。
GF(3) color envelope preservation across navigator compositions
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。
Bridge between Clifford Algebras (ganja.js/Grassmann.jl) and ACSets with grade-preserving morphisms
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 pytest test effectiveness against authoritative contracts, relevant faults, behavior-preserving refactors, realistic boundaries, isolation, and diagnostics. Use for test-quality audits, coverage-gap reviews, regression-test changes, or mocking reviews; preserve existing project gates rather than inventing coverage targets.
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。
Synchronize existing documentation with an observed code/configuration/API change while preserving each document's purpose and authority. Use after implementation changes can make documentation stale, incomplete, or internally inconsistent.
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。