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概要と使いどころ
TRIGGER THIS when building DEI strategy, creating diversity goals, designing inclusive programs, building equity analyses, developing training, addressing inclusion issues, or tracking DEI metrics. Develops comprehensive diversity, equity, and inclusion strategies aligned to business goals with measurable outcomes, training programs, metrics frameworks, and accountability 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。
Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server. Use this skill whenever the user asks for a tear sheet, company one-pager, company profile, fact sheet, company snapshot, or company overview document — especially when they mention a specific company name or ticker. Also trigger when users ask for equity research summaries, M&A company profiles, corporate development target profiles, sales/BD meeting prep documents, or any concise single-company financial summary. This skill supports four audience types: equity research, investment banking/M&A, corporate development, and sales/business development. If the user doesn't specify an audience, ask. Works for both public and private companies.
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。
Improve interface resilience through better error handling, i18n support, text overflow handling, and edge case management. Makes interfaces robust and production-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。
Use this skill whenever the user asks for an HTML-first visual design deliverable or design judgment: interactive prototype, slide deck, motion demo, infographic, dashboard, landing page, whitepaper, changelog, business card, social cover, brand system, design critique, multi-variant exploration, or export to MP4, GIF, PPTX, PDF, or SVG. It is optimized to make AI agents do the routing, template selection, verification, and export prep so humans spend less time prompt-engineering. Do not use for production web apps, SEO sites, backend systems, or pure copy edits.
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。
Enterprise BPMN 2.0 diagram generator — converts natural language process descriptions into OMG-compliant BPMN 2.0 XML files and SVG previews via a 4-phase pipeline: Intent Extraction (LLM → JSON Logic-Core) → Validation (deadlock detection, structural soundness) → ElkJS Auto-Layout → BPMN XML + SVG output. Supports: multi-pool collaborations, message flows, boundary events (timer/error/signal), loop/multi-instance markers, data objects, all gateway types with correct gatewayDirection, and all BPMN 2.0 task types. Use this skill whenever the user wants to create, generate, or model a BPMN diagram, process flow, or workflow — even if they say "draw a process", "model this workflow", "make a BPMN for...", "create a Prozessmodell", "visualize this process", or describe a business process in natural language. Also use for editing or extending existing BPMN Logic-Core 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。
Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business 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 when needing strategic project analysis from multiple independent expert perspectives. Triggers on business decisions, growth strategy, product direction, competitive analysis, or any situation where diverse C-level opinions reduce blind spots
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 auditing a product, business, or project ecosystem — analyzing data sources, decision loops, bottlenecks, and implementation contours. Triggers on "аудит продукта", "product audit", "data audit", "аудит данных", "аудит бизнеса", "проанализируй экосистему", "аудит систем".
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。
Designs effective KPI dashboards with proper metric selection, visual hierarchy, and data visualization best practices. Use when building executive dashboards, creating analytics views, or presenting business metrics.
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。
OSS-only security for OWASP Top 10, pentest, vuln testing (XSS, SSRF, CSRF, business-logic, Host header), threat modeling (STRIDE, ATT&CK), Sigma rules, SAST, code audit, AI/LLM red-team, or replacing paid tools (Burp, Nessus, Splunk) with OSS.
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。
Implements API rate limiting using token bucket, sliding window, and Redis-based algorithms to protect against abuse. Use when securing public APIs, implementing tiered access, or preventing denial-of-service attacks.
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 cumulative daily or retrospective sweep of research papers on a chosen topic, audit their claims, methods, integrity signals, and independent support, then identify overlooked but feasible project or business opportunities in a detailed source-grounded report. Use when asked to monitor papers every day, mine buried research, evaluate whether a paper is credible or reproducible, find unimplemented research ideas, or separate promising work from hype, weak evidence, and retracted or contradicted results.
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 an honest market-fit and viability audit of any project or idea and produce a decision doc, not code. Use when someone asks "is this viable as a business", "audit the market fit", "make the case and compare to competitors", "should I apply to YC or bootstrap", or wants to validate a project, product, or market before investing more time. Gathers verifiable traction data first, researches the competitive field, gets an independent cold read, writes a doc with pre-committed pass/middle/kill criteria and a concrete week-1 assignment, then hardens it with an adversarial review loop.
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。
Develop and execute a content strategy that maps content to audience needs, funnel stages, and business goals through editorial planning and performance analysis. Use when the user requests content strategy or provides relevant inputs for 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。
Draft privacy-policy language and a review checklist tailored to a business model, data practices, and relevant jurisdictions. Use when the user requests a privacy policy or needs to map disclosures for GDPR, CCPA, or similar frameworks; do not use it to guarantee legal compliance.
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 structured, engaging chatbot conversations with robust intent handling, slot filling, disambiguation, error recovery, and graceful fallback strategies. Use when the user requests chatbot conversation design or provides relevant inputs for 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。
Generate SQL queries from natural-language requirements using SELECT, JOIN, GROUP BY, window functions, CTEs, and subqueries. Use when the user needs a new query from a business question or schema; use query-optimization when an existing query or execution plan is slow.
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。
Kerberos-based Active Directory attacks driven by hand with standard tooling (Kerberoasting, AS-REP roasting, and delegation abuse: unconstrained, constrained/S4U, RBCD). Use when the target has SPN-bearing service accounts, accounts without pre-authentication, or delegation configured on computer/user objects, and you want the exact impacket/Rubeus/bloodyAD command, the hash format and hashcat mode, the Windows Event IDs that fire, and the remediation to write up.
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 Machine Learning Engineer. Use when a task calls for Machine Learning Engineer judgment. Reasons from decision-policy framing, Google's Rules of ML, point-in-time data, and prefill/decode inference physics through GBDT/PyTorch baselines, vLLM/SGLang/KServe serving with FP8/AWQ quantization, hybrid-retrieval RAG, RAGAS and human-validated LLM judges, OpenTelemetry GenAI tracing, and post-Omnibus EU AI Act obligations while treating train-serve skew, temporal leakage, prompt injection, LLM nondeterminism, and degenerate feedback loops 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 Embedded Systems Engineer. Use when a task calls for Embedded Systems Engineer judgment. Reasons from hardware timing, interrupt latency, memory maps, and deterministic WCET resource bounds through DWT CYCCNT and Saleae logic-analyzer timing proofs, SWD/JTAG (J-Link/OpenOCD) with CFSR/HFSR/BFAR fault decode, FreeRTOS/Zephyr respecting configMAX_SYSCALL_INTERRUPT_PRIORITY, MISRA C:2012 and ISO 26262 ASIL discipline, Nordic PPK2 power profiling, and MCUboot signed OTA while treating priority inversion, Cortex-M7 uncached-DMA D-cache incoherence, CAN bus-off, I2C NACK storms, and brownout-during-flash-write NVS corruption 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 Computer Hardware Engineer. Use when a task calls for Computer Hardware Engineer judgment. Reasons from CPI/AMAT and MESI/MOESI coherence through SystemVerilog RTL, PrimeTime/Design Compiler/Innovus signoff, PCIe LTSSM/TLP, SVA formal, and clock-gating power while treating CDC metastability, false-path abuse, X-optimism, and coherency traffic 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 Data Scientist. Use when a task calls for Data Scientist judgment. Reasons from CRISP-DM business estimands, leakage-safe sklearn Pipelines and nested CV, SQL/warehouse semantic metrics, A/B power and SRM/AA guardrails, causal DAG covariate discipline, and Model Cards/Datasheets while treating train-test leakage, Simpson's paradox, peeking, and PSI>0.25 drift 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 Causal Inference Scientist. Use when a task calls for Causal Inference Scientist judgment. Reasons from structural causal models, potential outcomes, and identification logic through DAGs and do-calculus, doubly-robust estimators (AIPW/TMLE, IPTW, g-formula), and design-based methods (IV, RD, Callaway-Sant'Anna DiD, synthetic control) while treating colliders and M-bias, positivity/overlap failure, and unmeasured confounding (Rosenbaum bounds, E-values) 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 Agricultural Engineer. Use when a task calls for Agricultural Engineer judgment. Reasons from mass, energy, and momentum balances on variable biological media through ET-based scheduling, psychrometric grain-drying balances, ASABE D497 draft estimates, EPANET pipe networks, and RTK-GNSS/ISOBUS precision-ag validation while treating soil compaction, clogged drip emitters, grain-bin hotspots, GPS drift, and uncalibrated yield monitors 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。