1.2万 件 ・ 関連度順
概要と使いどころ
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
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。
Get a local business found on Google -- audit and optimize the Google Business Profile, local pack ranking factors, review velocity, NAP consistency across directories, and location pages. For businesses whose customers search "near me," this outranks everything else in marketing.
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。
Generates monthly/quarterly investor updates. Takes KPIs, milestones, challenges, financials. Writes professional investor-update.md with highlights, metrics dashboard, product updates, team news, financial summary, upcoming milestones, asks. Multiple tones from seed-stage casual to Series B formal. Use when founders need to communicate progress to investors.
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 email templates for various business scenarios including sales outreach, customer support, internal communications, follow-ups, and apologies. Creates personalized, tone-appropriate templates with subject lines and formatting. Use when users need to write business emails, cold outreach, or professional communications.
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。
Assesses how ready a business is for AI adoption across six dimensions. Evaluates data maturity, tech stack, team skills, process documentation, budget, and culture. Generates a comprehensive ai-readiness-report.md with scores, gap analysis, and recommended starting points. Aligned with OneWave AI's audit methodology.
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。
Route Chinese-BERT-wwm model loading, model selection, benchmark data, and troubleshooting workflows for HFL Chinese BERT-family resources.
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。
Post-engagement knowledge harvest AND harness retrospective - after a box/bugbounty/pentest/CTF is completed, first diff how the engagement was EXECUTED against the skills/hooks that governed it (what discipline was skipped) and improve the harness, then sweep the whole engagement for GENERIC reusable knowledge NOT already in wiki/ and land it via the leak-gated stage->promote pipeline. Use at close-out or when asked to "extract learnings", "what did we learn", "harvest lessons into wiki", "distill this engagement", "post-mortem into the wiki", "what did we do wrong", "improve the harness from this box".
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。
US state privacy law applicability assessment tool. Evaluates revenue thresholds, data volume thresholds, business exemptions (GLBA, HIPAA, nonprofits), employee data carve-outs, and SBA small business determinations across all enacted state privacy laws.
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 privacy program effectiveness measurement including leading and lagging indicators, KPI definition, benchmarking methodology, executive reporting formats, board-level privacy dashboards, and metric-driven program improvement. Covers operational, compliance, risk, and strategic privacy metrics across the program lifecycle. Keywords: privacy metrics, KPIs, benchmarking, executive reporting, dashboard, program effectiveness.
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 systematic review of processing documentation for completeness against GDPR Articles 5, 13-14, 24, 28, and 30. Activate when auditing documentation or preparing for inspections. Keywords: documentation review, processing records, completeness, privacy notices, RoPA.
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 preparation for supervisory authority (DPA) inspections and investigations including document readiness checklists, interview preparation for key personnel, technical demonstration procedures, on-site logistics, response protocols, and post-inspection follow-up. Covers unannounced inspections, formal audits, and complaint-triggered investigations. Keywords: DPA inspection, supervisory authority, investigation, readiness, interview preparation, response protocol.
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 creation of organisational privacy policy hierarchy aligned to GDPR chapters including top-level policy, supporting procedures, operational guidelines, and training materials. Activate when building or updating policy frameworks. Keywords: policy framework, privacy policy, procedures, guidelines, policy hierarchy.
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。
Assesses AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.
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 privacy audit risk assessment including risk universe development, inherent and residual risk scoring, control effectiveness evaluation, risk-based audit planning, heat map generation, risk appetite alignment, and audit prioritization by risk exposure. Covers the full audit risk assessment cycle from scoping through ongoing monitoring. Keywords: audit risk assessment, risk universe, inherent risk, residual risk, control effectiveness, risk-based audit planning.
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 audit follow-up and verification processes including follow-up scheduling, remediation effectiveness testing, finding closure criteria, re-testing procedures, status reporting, and escalation of unremediated findings. Implements IIA Standard 2500 monitoring requirements and ISO 19011 follow-up guidance for privacy audit engagements. Keywords: audit follow-up, verification testing, finding closure, remediation effectiveness, re-testing, follow-up audit.
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。
AI Agent Harness 设计模式 — 记忆、权限、上下文工程、委派、技能、 Hook、Bootstrap。中文版。
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。
Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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。
Retro 16-bit art direction for Blender-assisted SNES/Genesis-era color richness, dither cues, and sprite-like clarity.
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 email templates for various business scenarios including sales outreach, customer support, internal communications, follow-ups, and apologies. Creates personalized, tone-appropriate templates with subject lines and formatting. Use when users need to write business emails, cold outreach, or professional communications.
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 when reasoning about generative AI, adversarial machine learning, neural network security, algorithmic fairness, or deep learning fundamentals. This skill channels the thinking of Ian Goodfellow, inventor of Generative Adversarial Networks (GANs). Trigger this skill when the user asks about model robustness, mitigating bias, evaluating AI guardrails, designing generative models, or defending against adversarial attacks. Apply his frameworks of minimax games, adversarial feature learning, and worst-case robustness analysis to shift the user's perspective from average-case optimization to adversarial resilience.
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 or evolve a repository-specific CLI, MCP and skills harness using MetaHarness, with bounded evaluation and explicit release authority.
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 Chinese monospaced compass-style PDCA or CAPD problem-solving cards using East-West-South-North-Center layout, full-width and half-width spaces, diagonal Unicode arrows, and strict Chinese character limits. Use when Codex needs to turn a problem, incident, decision tradeoff, root-cause analysis, improvement cycle, or CAPD/PDCA workflow into a square text diagram, Hermes/Discord-ready card, or Chinese 方位九宮圖 with center ◎, north Plan, east Do, south Check, west Action, and four diagonal transition arrows.
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, calibrate, layer, and validate production Blender materials including bare and coated metal, brushed steel, rust, aged and wet wood planks, stone, concrete, plastic, glass, fabric, water, dirt, moss, and wetness. Use when material identity, UV direction, real-world texture scale, PBR channel handling, roughness response, anisotropy, normal/bump/displacement, transmission, volume absorption, aging, or surface layering determines realism.
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。
Verify code correctness statically against specifications using type checking, contract verification, and formal methods. Use when: (1) Verifying type safety and null safety in Python, Java, or C/C++ code, (2) Checking design-by-contract specifications (preconditions, postconditions, invariants), (3) Validating code against formal specifications, (4) Ensuring code quality and correctness before runtime, (5) Finding potential bugs through static analysis. Supports Python (mypy, contracts), Java (javac, JML), and provides verification scripts and contract specification guidelines.
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。