1,132 件 ・ 関連度順
概要と使いどころ
Use Hugging Face Evaluate to load metrics, comparisons, and measurements; compute and combine results; run evaluator pipelines; create custom modules; troubleshoot optional dependencies, cache, Hub, and CLI workflows.
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。
Answer how long agent work will take, how much longer active work needs, or what clock time applies. Estimate full AI-agent wall time, measure active progress first, and get owner-facing times from `sno report-time`. Never estimate human-developer time.
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。
Measure GTM with the metrics that matter (net developer retention, DREAM funnel) instead of vanity numbers. Use when the user has dashboards full of stars and pageviews but can't tell if go-to-market is working, or is optimizing acquisition over a leaky bucket.
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 privacy KPI dashboards tracking DSAR volume and response time, breach count and severity, DPIA completion rate, training coverage, and consent rates. Includes metric definitions, data collection patterns, visualization designs, and executive reporting templates for privacy program measurement.
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。
Creates GDPR Article 30(2) Records of Processing Activities for data processors with all four mandatory fields: processor and controller names and contact details, categories of processing, third country transfers, and security measures description. Activate for processor RoPA, Art. 30(2), processor records, sub-processor documentation.
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。
Build privacy-preserving data sharing platforms using synthetic data generation with the SDV library, data clean rooms, secure enclaves, and utility measurement. Covers end-to-end architecture for sharing analytical datasets while preserving individual privacy guarantees.
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。
Configuring Google Consent Mode v2 for privacy-compliant measurement and advertising. Covers default and update commands, consent state mapping to GA4 and Google Ads, conversion modeling with cookieless pings, and EEA requirements effective March 2024.
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。
Structures risk mitigation planning and residual risk tracking for Data Protection Impact Assessments under GDPR Article 35(7)(d). Covers mitigation measure identification, implementation tracking, residual risk acceptance, and Art. 36 prior consultation triggers. Keywords: DPIA mitigation, risk treatment, residual risk, Art. 35(7)(d), safeguards, mitigation tracking, prior consultation.
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 compliance with country-specific data localization requirements across key jurisdictions including Russia (242-FZ), China (PIPL Art. 40, CAC measures), India (DPDP Act), Turkey, Vietnam, and Indonesia. Covers localization assessment, architecture design, and exemption procedures. Keywords: data localization, data residency, PIPL, 242-FZ, cross-border restrictions.
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 implementation of the GDPR accountability principle under Articles 5(2) and 24, including documentation requirements for policies, DPIAs, RoPA, training records, and breach logs. Activate when establishing or reviewing accountability measures, preparing evidence portfolios, or demonstrating compliance to supervisory authorities. Keywords: accountability, Article 5(2), Article 24, documentation, compliance evidence, governance.
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。
Evaluating and implementing cookie-less tracking alternatives for a post-cookie era. Covers the Privacy Sandbox APIs (Topics, Attribution Reporting, Protected Audiences), server-side analytics, and privacy-preserving measurement techniques.
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。
Provides combined DPIA and AI Act conformity assessment template with integrated risk scoring matrix. Covers GDPR Art. 35 DPIA elements, AI Act high-risk system requirements, mitigation measures, and human oversight assessment. Keywords: DPIA template, conformity assessment, risk scoring, AI Act, combined assessment, high-risk AI.
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 backup and archive data under retention schedules and erasure obligations. Covers the technical infeasibility exception for backup deletion, backup cycle alignment with retention periods, restore-and-delete procedures, and interim protective measures during backup retention. Activate for backup deletion, archive erasure, backup retention, restore and delete, technical infeasibility 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。
Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.
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。
When the user wants to set up, improve, or audit analytics tracking and measurement.
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 new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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。
Scores a riso film or reworks its score. Designs the cue list from the film's FILM.md events, builds a deterministic renderAudio() from the sound kit in studies/sound.html, iterates with tools/audio.mjs, gates determinism, length, loudness and sync, and records measured results in FILM.md. Use for "score this film", "add sound/music", "the climax is too loud", "fix the handoff at 16 s", "check the mix", sound effects, foley or a soundtrack on a riso film. Not for drawing or motion (riso-film) or changing tools/.
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 new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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 substantial coding or repository-review work through a two-stage Sol commander/verifier and separate Luna Max CLI worker. Use when the user explicitly asks Sol to direct or supervise Luna, explicitly requests this router, needs an isolated auditable Luna-owned implementation with a Sol review loop, or asks to diagnose or measure the router itself. Do not use merely because a task involves coding, verification, progress reporting, or token saving. For small or straightforward tasks, keep work in Sol unless the user explicitly requests Luna; router configuration, usage, and efficiency questions should use read-only inspection without launching a worker.
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。
Plans, creates, and optimizes digital marketing campaigns including content strategy, social media, email marketing, SEO, and AI visibility (GEO). Generates UTM parameters and tracking URLs. Helps develop go-to-market strategies, campaign messaging, content calendars, and performance measurement frameworks. Use when asked to "create a marketing campaign", "plan content strategy", "build email sequences", "set up UTM tracking", "analyze marketing metrics", "launch a product", "optimize for SEO", "improve AI visibility", "optimize for voice search", "set up IndexNow", "check accessibility for SEO", or "create social media previews". Supports both marketing beginners and experienced marketers needing templates.
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。
Measure what matters with proper event tracking, funnels, cohorts, and metrics. Use when setting up analytics, tracking features, or understanding 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。
Use when starting or picking up a non-trivial effort (feature, investigation, perf probe, refactor, migration) in a shared repo where teammates or future agents must coordinate or hand off; when a repo has no durable in-tree record of decisions and dead-ends; when you're about to drop a well-measured negative result; or when bootstrapping a journal from a repo's commit history.
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 blameless incident postmortems with timeline reconstruction, root cause analysis, action items, and preventive measures
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。