5,004 件 ・ 関連度順
概要と使いどころ
Use Fairlearn to assess group fairness, compute disparity metrics, visualize subgroup performance, and mitigate unfairness with preprocessing, reductions, postprocessing, or adversarial learning.
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。
Guide Django REST Framework and GeoDjango projects through django-rest-framework-gis installation, GeoJSON serialization, spatial filtering, pagination, OpenAPI schema generation, and backend troubleshooting.
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 DeepFilterNet speech enhancement, Python API/CLI, training data, model export/evaluation, and Rust/LADSPA realtime 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。
Use Brian2 for clock-driven spiking-neural-network modeling, simulation, synaptic connectivity, input generation, recording, physical-unit equations, spatial neurons, runtime code generation, and C++ standalone 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。
Use bm25s for sparse lexical retrieval: tokenize text, build and query BM25 indexes, persist or memory-map them, run the high-level or bm25 CLI workflows, and diagnose optional acceleration and integrations.
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。
Operate the Deep Researcher Agent repository for autonomous deep-learning experiments, execution backends, provider/tool dispatch, GPU resources, durable progress, and research-support integrations.
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。
AgentScope repo skill for building agents, provider connectors, RAG and memory workflows, service deployments, and local or sandboxed workspaces.
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 repo skill for Agent Lightning package tasks: authoring trainable agents, tracing rewards and spans, running LightningStore/Trainer loops, using agl CLI services, choosing examples, and troubleshooting optional backends.
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。
Stack Honey onto Superpowers-style subagent workflows: dispatched subagents skip the session hook, so inject the levers into each dispatch prompt.
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 the NIST Privacy Framework COMMUNICATE function covering CM.AW awareness raising and CM.PO communication policies. Provides transparency mechanisms, stakeholder engagement frameworks, privacy notice templates, and communication workflow guidance.
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 automated PII detection and redaction pipelines using spaCy NER, Microsoft Presidio, and AWS Macie integration. Includes confidence scoring, custom entity type definitions, batch processing workflows, and multi-format document scanning for structured and unstructured data sources.
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。
Conducts a Data Protection Impact Assessment for automated decision-making and profiling systems under GDPR Article 35(3)(a), covering algorithmic transparency, meaningful human oversight, contestation mechanisms, and Art. 22 safeguards. Activate for DPIA automated decision, profiling DPIA, algorithmic impact assessment, Art. 35(3)(a), ADM risk assessment 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。
Designs and implements privacy notices for children that comply with GDPR Articles 12-14, UK AADC Standard 4, and COPPA Section 312.4. Covers plain language, visual explanations, layered information, age-appropriate vocabulary, and interactive notice elements. Keywords: children privacy notice, transparency, plain language, visual, age-appropriate, layered notice.
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。
Liminal space and Backrooms art direction for Blender with empty transitional architecture, wrong familiarity, and endless repetition.
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。
Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. Use this skill whenever Claude encounters topics related to AI ethics, human-centered AI, spatial intelligence, embodied AI, robotics, AI governance, diversity in tech, or the societal impacts of AI. Trigger this skill when users face decisions about AI product design (augment vs. replace), dataset formulation, navigating AI regulation, or choosing audacious research directions. Channel her pragmatic optimism and focus on spatial, physical grounding over pure language 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。
Fix ClickHouse "Code: 243 Cannot reserve 1.00 MiB, not enough space" errors caused by system log tables (text_log, trace_log, processors_profile_log, query_log) filling the disk. Use when: (1) ClickHouse inserts fail with NOT_ENOUGH_SPACE error, (2) Disk is 100% full but application tables are small, (3) system database is 10-100x larger than user databases, (4) Sentry shows batch insert/commit failures across multiple tables simultaneously. Covers both self-hosted ClickHouse (Altinity operator on K8s) and ClickHouse Cloud with different remediation paths for each.
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。
Convert image references into spatial hypotheses, camera constraints, construction routes, material evidence, and measurable Blender reconstruction gates. Use whenever a user asks to replicate, recreate, match, clone, or reconstruct a scene, environment, object, material, lighting setup, or shot from one or more images, especially when room connectivity, hidden space, perspective, visible-frame fidelity, material identity, aging, wetness, or photographic treatment matters.
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 layout, spacing, and visual rhythm. Fixes monotonous grids, inconsistent spacing, and weak visual hierarchy to create intentional 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。
Research competitor paid-ad presence, messaging, creative, formats, landing pages, keyword and auction signals, transparent ad libraries, and strategic gaps across supported platforms. Use for competitor ads, ad libraries, ad spy, competitive PPC analysis, competitor creative, Google Ads Transparency, Meta Ad Library, or paid-media competitor research.
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 polished, intentional UIs with concrete CSS/Tailwind values — typography, color, layout, spacing, dark mode, accessibility, animations, components. Encodes specific, opinionated rules with exact values, not vague advice. Covers buttons, cards, forms, tables, navigation, dashboards, landing pages, onboarding, and more. Triggers on "absolute ui", "design a UI", "style this component", "make this less like AI slop", "fix the spacing/typography/color", "dark mode", "design a pricing page", "make this look better".
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 Environmental Microbiologist. Use when a task calls for Environmental Microbiologist judgment. Reasons from spatial patchiness, redox thermodynamic ceilings, and process-over-taxonomy guild function through DADA2/QIIME2 amplicons keyed to SILVA/GTDB/PR2/UNITE, metaSPAdes/MetaBAT2 MAGs vetted by CheckM/GUNC, and SIP/qSIP rate assays paired with IC/GC/ICP-MS chemistry, while treating extraction-batch effects, relic and extracellular DNA, primer-window bias, and core pseudoreplication 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 Energy Systems Engineer. Use when a task calls for Energy Systems Engineer judgment. Reasons from exergy, load duration curves, capacity factor, and grid boundary constraints through pinch analysis, hourly dispatch models (PLEXOS, HOMER Pro, SAM, PVsyst), spark-spread CHP screening, and IPMVP M&V while treating nameplate-vs-utilization confusion, average-vs-marginal grid emissions, unrealistic arbitrage spreads, and demand-charge ratchet resets 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 Algorithms Researcher. Use when a task calls for Algorithms Researcher judgment. Reasons from separating problem, model, and cost model (comparison, word-RAM, arithmetic, online) through exchange/matroid greedy proofs, subproblem-DAG dynamic programming, max-flow min-cut and Goemans–Williamson primal-dual rounding, Karp–Rabin fingerprinting, competitive ratio and Yao's principle, PTAS/FPTAS (Williamson–Shmoys), and Instance Space Analysis over DIMACS10/MIPLIB 2017/SuiteSparse while treating amortized-versus-average-case conflation, unproven greedy killed by a 4-node counterexample, Monte Carlo without a false-match probability, DIMACS10 suite overfitting, and 'linear time' hiding word-size tricks over bit-length L 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。