788 件 ・ 関連度順
概要と使いどころ
Analyze disaster prediction and early warning systems — model accuracy for flood, earthquake, wildfire, hurricane, and tsunami hazards, data pipeline reliability from sensor networks and satellite feeds, alert distribution latency and channel coverage.
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 ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science teams.
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 precision agriculture and crop management software for yield prediction model accuracy, soil analysis integration, irrigation optimization algorithms, pest and disease detection pipelines, satellite and drone imagery processing, weather data integration.
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。
**MEASUREMENT SKILL** — Benchmark framework for measuring skill effectiveness. A/B tests agent outputs with and without a target skill, scoring correctness, completeness, and pattern adherence. USE FOR: validating skill quality, comparing before/after skill changes, proving skill ROI with data. DO NOT USE FOR: general testing, unit tests, CI pipelines.
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 a practitioner needs to classify the risk level of an upcoming Salesforce deployment, define rollback trigger conditions, or assign decision authority before the release window opens. Triggers: 'how risky is this release', 'what needs rollback planning', 'classify deployment risk', 'pre-deployment risk checklist', 'rollback decision authority', 'high risk metadata types'. NOT for executing the deployment itself, writing CI/CD pipeline code, or troubleshooting failures after a deployment has already run — use admin/change-management-and-deployment for execution planning and devops/deployment-error-troubleshooting for post-failure diagnosis.
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。
Stand up reporting for a new audience — an external client, a business unit, a region, an executive team — end to end: what they need to see, whether the data exists, access and scoping, their place in the pipeline, the standard report set, and validation against numbers they can check themselves. Use when a new audience is onboarded, when an existing one adds a site, a region or a business unit, when their reporting is rebuilt after a change on their side, or when they say the numbers do not match their own records.
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 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique loops, intelligent routing, multi-agent ensemble, persistent memory, external API tools, iterative loops, data transforms, and parallel execution. Use this skill whenever the user wants to add agent capabilities to a workflow engine, build an AI orchestration framework, implement Karpathy-style AI upgrades, add new node types to a DSL runtime, or create autonomous agent pipelines. Also trigger when the user mentions AetherLang, flow engines, node types, agent frameworks, or wants to upgrade their AI system with capabilities like self-programming, reflection, routing, ensemble, memory, or tool use.
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。
Coordinate end-to-end deployments across Stripe, Supabase, and Vercel using the Composio CLI. Update pricing, apply database migrations, deploy frontend, verify changes, and announce releases from a single script.
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。
Comprehensive reference for inter-process communication mechanisms -- every way processes can talk on a computer. Covers sockets (TCP, UDP, Unix domain), WebSockets, SSE, pipes (named/anonymous), shared memory (mmap, shm_open), message queues, signals, D-Bus, XPC (macOS), gRPC, REST, stdin/stdout, file-based coordination, and clipboard. Performance benchmarks, platform availability, and code examples for each. Special focus on which IPC works best for AI agent coordination. Activate on: "IPC", "inter-process communication", "process communication", "how do I talk between processes", "Unix socket vs TCP", "shared memory", "named pipe", "WebSocket vs SSE", "gRPC vs REST", "agent coordination IPC", "XPC service", "message passing", "stdout pipe", "D-Bus", "mmap". NOT for: distributed systems design (use distributed-systems), network protocol design (use networking), message queue infrastructure like Kafka (use data-pipeline-engineer).
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。
Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).
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 building Grafana dashboards backed by Prometheus, Loki, or Tempo, designing PromQL/LogQL queries, wiring template variables, setting alert rules, building SLO dashboards, or maintaining dashboards as code. Triggers: rate() vs increase() confusion, irate vs rate, label_replace, recording rules, alerting rule expressions, multi-dimensional template variables, ad-hoc filters, dashboard JSON model, provisioning via Terraform/grafonnet, p99 / histogram_quantile usage. NOT for Datadog/New Relic dashboards (vendor-specific), Grafana plugin development, or Loki ingestion pipeline tuning.
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。
Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).
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。
Introduce people to AI, agentic AI, and AI skills with appropriate pedagogy for any audience. Covers LLM analogies, progressive complexity paths, aha-moment demos, audience-tailored explanations, workshop design, and overcoming emotional barriers to AI adoption. Activate on 'explain AI', 'introduce AI', 'AI workshop', 'AI demo', 'teach AI', 'AI literacy', 'onboard to AI', 'AI evangelism', 'explain agentic', 'explain skills to', 'AI training session', 'AI onboarding'. NOT for teaching ML engineering (use ai-engineer), not for building AI features (use ai-engineer), not for prompt engineering (use prompt-engineer), not for building training data pipelines.
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。
Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.
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。
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
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 production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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 infrastructure-first security audit of a repository - secrets archaeology, dependency supply chain, CI/CD pipeline, LLM/AI attack surface, agent skill supply chain, OWASP Top 10, STRIDE threat model, and data classification - with a confidence gate and a pre-emit verification gate that suppress speculative findings. Use when asked for a security audit, threat model, pentest-style review, OWASP pass, or a pre-release security check. Not for reviewing a single diff - use review-changes or requesting-code-review for that.
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 choosing .NET concurrency patterns for async I/O, queues, pipelines, or thread safety. Keywords: async/await, channels, dataflow, Rx, lock contention, producer consumer, parallel processing.
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。
Plataforma de observabilidade para LLM com rastreamento, avaliação e monitoramento. Use ao depurar aplicações LLM, avaliar saídas de modelos em datasets, monitorar sistemas em produção ou construir pipelines de testes sistemáticos para aplicações de IA.
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 on an existing project to decide KEEP / MERGE / ARCHIVE — never limbo. Forces a call on tools that already exist and may have stopped earning their keep. Trigger on "is this still worth keeping", "audit this repo", "tech-debt cleanup", "sweep the folder", "what's dead here", or any signal that something hasn't been touched in months. Judged from both the agent lens (still legible, still drivable, still safe) and the human lens (do I still reach for it, does it nag). Includes a non-negotiable footgun check for stale pipelines that can overwrite live data. Outputs a backed AUDIT.md and a one-line verdict note in the project root docs. Do NOT load for greenfield projects (use new-project-gate), one-off bug fixes, or when the answer is already obvious.
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。
Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).
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 production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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 AI workflows combining multiple models and services. Patterns: batch processing, scheduled tasks, event-driven pipelines, agent loops. Tools: inference.sh CLI, bash scripting, Python SDK, webhook integration. Use for: content automation, data processing, monitoring, schedul...
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。
Monitors Certificate Transparency (CT) logs to detect unauthorized certificate issuance, discover subdomains via CT data, and alert on suspicious certificate activity for owned domains. Uses the crt.sh API and direct CT log querying based on RFC 6962 to build continuous monitoring pipelines that catch rogue certificates, track CA behavior, and map the external attack surface. Activates for requests involving certificate transparency monitoring, CT log auditing, subdomain discovery via certificates, or certificate issuance alerting.
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。