228 件 ・ 関連度順
概要と使いどころ
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
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。
Guides Stripe integration decisions across API selection (Checkout Sessions vs PaymentIntents), Connect platform setup (Accounts v2, controller properties), billing/subscriptions, tax and registrations (Stripe Tax, automatic_tax, product tax codes), Treasury financial accounts, integration options (Checkout, Payment Element), migrating from deprecated Stripe APIs, and security best practices (API key management, restricted keys, webhooks, OAuth). Use when building, modifying, or reviewing any Stripe integration, including accepting payments, building marketplaces, integrating Stripe, processing payments, setting up subscriptions, collecting sales tax, VAT, or GST, creating connected accounts, or implementing secure key handling.
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。
Draft, restructure, and diagnose IEEE-style technical manuscript sections using a routed static/dynamic workflow. Use for IEEE conference, journal, Transactions, Letters, or magazine papers when writing titles, abstracts, introductions, related work, methods, experiments, conclusions, contribution statements, section outlines, or Chinese-to-English engineering paper drafts in AI, computer science, communications, control, signal processing, electrical/electronic engineering, power, robotics, circuits, and applied systems.
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。
Audits a codebase or business process for regulatory compliance across GDPR, HIPAA, SOC2, CCPA, and PCI-DSS. Scans for PII handling, data retention, encryption, access controls, audit logging, consent management, and data transfer issues. Generates a structured compliance report with findings, gap analysis, remediation steps, and evidence requirements.
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。
Vendor breach notification cascade management per GDPR Article 33(2). Covers processor-to-controller notification without undue delay, escalation paths, coordinated multi-party breach response, liability allocation, and regulatory notification coordination.
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。
Virginia Consumer Data Protection Act (VCDPA) compliance implementation. Covers 5 consumer rights, controller obligations, processor requirements, opt-in for sensitive data, data protection impact assessments, AG enforcement, and cure period provisions. Effective January 1, 2023.
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。
Manages retention exception workflows including request-approval processes, duration limits, periodic review cycles, documentation requirements, and audit trail maintenance. Covers legitimate grounds for extending retention beyond scheduled periods and governance controls to prevent indefinite data hoarding. Activate for retention exception, retention extension, data hoarding prevention, retention override queries.
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。
Kentucky Consumer Privacy Protection Act (KPPA) compliance. Effective January 1, 2026. Covers consumer rights, controller thresholds at 100,000 consumers, sensitive data processing consent, cure period provisions, and AG enforcement framework.
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。
Guides cooperation with GDPR supervisory authorities under Article 31, including procedures for responding to investigations, information requests, and on-site inspections. Covers controller and processor obligations during supervisory authority interactions. Keywords: supervisory authority, Article 31, cooperation, DPA investigation, information request, inspection.
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。
Delaware Personal Data Privacy Act (DPPA) compliance implementation. Covers consumer rights (access, correct, delete, portability, opt-out), controller obligations, processor requirements, sensitive data consent, universal opt-out recognition, DPIA requirements, and AG enforcement. Effective January 1, 2025, with no revenue threshold for applicability.
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。
Systematic application of the eight privacy design patterns per Hoepman: minimize, hide, separate, abstract, inform, control, enforce, and demonstrate. Covers pattern selection methodology per processing activity, mapping to GDPR principles, and practical implementation guidance for privacy-by-design system architecture.
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。
Fix misleading HTTP response header values when debugging with curl -I or curl -sI. Use when: (1) Response headers differ between curl testing and actual browser/client behavior, (2) Cache-Control or other headers show unexpected values despite correct middleware code, (3) Server-side middleware that only applies to GET requests appears to not work when testing with curl -I. The -I flag sends HEAD requests, and middleware that checks for GET method will skip processing, returning handler-level headers instead of middleware-overridden ones.
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 Standard Operating Procedures with step-by-step workflows, RACI matrices, process maps, approval chains, and version control
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。
Think and work like an expert Marine Geologist. Use when a task calls for Marine Geologist judgment. Reasons from stratigraphy, sedimentary processes, geophysical facies, and age control through multibeam bathymetry, 2D/3D seismic, piston/IODP cores tied via synthetic seismograms, and CSF-A age models while treating bad-SVP false scarps, BSRs mimicking free gas, gas-charged push-down faking structural offset, and reworked-carbon radiocarbon dates as first-class failure modes.
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。
Think and work like an expert Histologist. Use when a task calls for Histologist judgment. Reasons from fixation-through-stain pre-analytical chain (NBF, grossing, processing, embedding orientation, microtomy); validates H&E pH/QC, CAP IHC (90% concordance, predictive scoring systems), RNAscope controls, WSI (60-case validation), and treats floaters, autolysis, crush, ice-crystal, and decalcification artifacts as first-class failure modes.
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。
Think and work like an expert Composites Engineer. Use when a task calls for Composites Engineer judgment. Reasons from CLT/ABD laminate mechanics, Halpin–Tsai micromechanics, and CMH-17/NCAMP allowables through autoclave/OOA/RTM process control, ASTM D30 mechanical qualification, ultrasonic C-scan and CAI damage tolerance while treating fiber waviness, void content, under-cure, and quasi-isotropic strength traps as first-class failure modes.
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。
Think and work like an expert Additive Manufacturing Engineer. Use when a task calls for Additive Manufacturing Engineer judgment. Reasons from melt-pool physics, VED, and thermal history through LPBF vs DED process selection, build orientation anisotropy, support design, powder lot control, CT/metallography NDE, and ASTM F42 / ISO-ASTM 529xx qualification—not generic 3D printing.
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 dark-themed, animated technical diagrams as self-contained HTML+SVG files — flowcharts whose connectors visibly flow, and architecture diagrams where requests travel as light dots through the system (Diagrid/Temporal landing-page style). Use this skill whenever the user asks for a flowchart, workflow, pipeline, process diagram, state machine, system architecture, infrastructure, cloud, microservices, or network topology diagram — and especially when they mention "animated", "flowing", "dynamic", "alive", "GIF-like", or want a diagram for a landing page, README, docs, or product demo. Also use it to convert Mermaid source (a mermaid code block or .mmd file) into an animated diagram — "animate this mermaid", "make this flowchart move". Prefer this over static diagram output whenever the diagram represents anything that moves: requests, events, data, jobs, messages, or control flow.
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。
Git is deceptively simple to learn and incredibly hard to master. The difference between a team that ships and a team that fights merge conflicts all day comes down to workflow discipline. Your git history is documentation - make it tell a story future you can understand. This skill covers branching strategies (trunk-based, gitflow, GitHub flow), commit hygiene, merge vs rebase, conflict resolution, and the commands you actually need. Key insight: most git disasters come from not understanding what you're about to do. 2025 lesson: Trunk-based development with short-lived branches has won. Long-lived feature branches are a code smell. If a branch lives more than a few days, something is wrong with your architecture or process. Use when "git workflow, branching strategy, git merge, git rebase, merge conflict, git commit, trunk-based, gitflow, git history, git revert, git reset, git, version-control, workflow, branching, commits, merge, rebase, collaboration" mentioned.
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 systematic orchestration of AI-powered marketing workflows that combine content generation, approval processes, multi-channel distribution, and quality gates into cohesive automation systems. This skill integrates AI generation tools (Jasper, Claude, GPT) with automation platforms (Zapier, Make, n8n) and marketing systems to build scalable content pipelines. It focuses on maintaining brand consistency, implementing rigorous quality gates, and balancing automation with strategic human oversight. Key capabilities include designing parallel approval flows, monitoring costs, and architecting "invisible" automation that enhances productivity without sacrificing quality.Use when "AI workflow, automate content, content automation, workflow automation, AI pipeline, automated marketing, content distribution automation, approval workflow, scale content production, AI orchestration, automation, workflow, ai-orchestration, content-pipeline, approval-workflow, multi-channel, quality-gates, cost-control" mentioned.
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。
Use when the diff builds a query, a shell command or argv, a subprocess environment, a template, an XML parse, an eval or a deserialization from data that may be attacker-controlled - SQL/NoSQL, OS command, argument, env-var, template and code injection, XXE, unsafe deserialization
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 tmux from the command line to run, drive, and monitor terminal programs as a coding agent (detached sessions, send-keys input injection, capture-pane output polling, formats, completion detection, and control mode). Use when a task needs long-running or interactive processes (builds, tests, servers, REPLs, watch modes, TUI apps) kept alive across turns or inspected live; skip when plain `cmd > log 2>&1 &` is enough.
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。
Write, restructure, classify, and review documentation with the Diataxis framework. Use when the user asks to create, improve, audit, or reorganize documentation of any kind: knowledge bases, help centers, onboarding guides, process docs, manuals, runbooks, product or API docs, tutorials, how-to guides, references, explanations. Also use when the user mentions Diataxis, docs taxonomy, or documentation architecture, or asks why a document feels confusing. Also use for Simplified Technical English (ASD-STE100) or controlled-language rewrites of English technical docs. Not for marketing, legal, sales, press, or fiction writing unless the user wants a Diataxis-style analysis.
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。