「dags」の検索結果

Apply rapport-based elicitation to resistant or semi-cooperative humans and agents. Use when coercive prompting or brittle handoffs damage cooperation. NOT for routine cooperative tasks, pure technical debugging, or already reliable exchanges.

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 face recognition systems with InsightFace, ArcFace, enrollment pipelines, HDBSCAN clustering, and privacy-compliant architecture. Activate on: face recognition, face enrollment, face clustering, identity verification, facial search. NOT for: general object detection (computer-vision-pipeline), emotion analysis (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。

Message queues, pub/sub, event sourcing with Kafka, RabbitMQ, Redis Streams. Activate on: event-driven, message queue, pub/sub, Kafka, RabbitMQ, event bus, async messaging, dead letter queue. NOT for: CQRS projections (use cqrs-event-sourcing-architect), real-time WebSocket (use websocket-realtime-expert), observability (use observability-apm-expert).

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。

Expert in temporal event detection, spatio-temporal clustering (ST-DBSCAN), and photo context understanding. Use for detecting photo events, clustering by time/location, shareability prediction, place recognition, event significance scoring, and life event detection. Activate on 'event detection', 'temporal clustering', 'ST-DBSCAN', 'spatio-temporal', 'shareability prediction', 'place recognition', 'life events', 'photo events', 'temporal diversity'. NOT for individual photo aesthetic quality (use photo-composition-critic), color palette analysis (use color-theory-palette-harmony-expert), face recognition implementation (use photo-content-recognition-curation-expert), or basic EXIF timestamp extraction.

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 CI/CD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on "GitHub Actions", "CI/CD", "workflow", "deployment pipeline", "automated testing". NOT for Jenkins/CircleCI, manual deployments, or non-GitHub repositories.

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 error handling strategies for TypeScript and Python applications — exception hierarchies, Result/Either types, retry patterns, error boundaries, and structured error logging. Use when designing error handling architecture, choosing between exceptions and Result types, implementing retry logic, or building error recovery flows. Activate on "error handling", "exception hierarchy", "Result type", "retry pattern", "circuit breaker", "error boundary", "Pokemon exception". NOT for debugging specific runtime errors, logging infrastructure setup, or monitoring/alerting configuration.

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。

Manage database schema with Drizzle ORM and SQLite migrations. Use when adding tables, modifying columns, creating indexes, or running migrations. Activates for database schema changes, migration generation, and Drizzle query patterns.

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。

Diagnose and eliminate the GraphQL N+1 resolver explosion using Facebook DataLoader's per-request batching, plus the second-order defenses (persisted queries, depth limits, cost analysis). Use when a GraphQL query that returns N items in a list triggers N+1 database round trips, when designing nested resolvers that load associations, when a list query times out under load, or when you need to budget query cost before resolvers run. NOT for REST-style endpoint design (use api-versioning-strategy), generic database query optimization (use database-query-optimizer), or schema design (use graphql-server-architect).

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。

Expert landscape designer transforming yards through photo mapping, 3D visualization, seasonal planning, and deep plant knowledge. Specializes in fast-growing privacy screens (knows arborvitae pitfalls), architecture-appropriate design, outdoor living spaces, and realistic maintenance expectations. Activate on "landscape design", "yard design", "garden planning", "plant selection", "privacy screen", "outdoor living", "backyard makeover", "arborvitae", "hedge", "fast growing tree", "landscaping ideas". NOT for interior design (use interior-design-expert), hardscape construction (consult contractors), or lawn care chemicals (consult local experts).

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。

Analytical methods for decomposing expert performance into cognitive and physical task components

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。

Expo managed workflow expert for EAS Build, Submit, Update, config plugins, and custom dev clients. Activate on: Expo project, EAS Build, EAS Update, config plugin, expo-dev-client, Expo Router, Expo SDK 52, app.json configuration. NOT for: bare React Native (use react-native-architect), native module authoring (use react-native-architect), Flutter (use flutter-bloc-state-manager).

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。

Use when designing matrix builds (OS x version x dimension), composing reusable workflows, gating jobs on conditions, doing OIDC to AWS/GCP/Cloudflare without long-lived secrets, sharding tests across runners, or fixing cache invalidation. Triggers: workflow_call between repos, fail-fast: false tradeoffs, exclude/include matrix surgery, environment protection rules, OIDC trust policy setup, concurrency cancellation on duplicate pushes, artifact reuse across jobs, conditional job matrices computed at runtime. NOT for GitLab CI / CircleCI / Jenkins (different yaml dialects), self-hosted runner administration, or local act dev.

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 running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).

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。

Decision-theoretic framework for agents embedded within the environments they model and act upon

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。

Manage DAG execution lifecycles including start, stop, pause, resume, and cleanup. Activate on 'execution lifecycle', 'stop execution', 'abort DAG', 'graceful shutdown', 'kill process'. NOT for cost estimation, DAG building, or skill selection.

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 dashboards for migrations and runtimes. Use for authority drift, verifier status, burn-down, runtime health, or pain panels. NOT for vanity analytics, ad hoc charts, or duplicate reporting.

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 professional emails for various contexts including business, technical, and customer communication. Use when the user needs help writing emails or composing professional messages.

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。

Structured interview and observation techniques for extracting tacit knowledge from domain experts

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。

Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone 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。

Benchmark multi-agent coordination systems with experiment design, power analysis, human rating protocols, bootstrap confidence intervals, and reproducible reporting. Use for salvage latency, recovery fidelity, coordination overhead, and protocol comparison studies. NOT for ML model benchmarking, web-product A/B testing, survey design, or general-purpose data science.

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。

Environment configuration manager for 12-factor config, feature flags, and multi-environment management. Activate on: environment variables, ConfigMap, feature flags, 12-factor config, dotenv management, config per environment, runtime config. NOT for: secret storage (use secret-management-expert), IaC provisioning (use terraform-module-builder), CI/CD variables (use github-actions-pipeline-builder).

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。

Git branching strategies, conflict resolution, rebase workflows, monorepo patterns, and advanced operations like cherry-pick, bisect, and reflog recovery. Activate on "git", "branching strategy", "merge conflict", "rebase", "cherry-pick", "monorepo git", "git bisect", "git reflog", "squash commits". NOT for GitHub Actions CI/CD (use github-actions-pipeline-builder), GitHub API/webhooks, or PR review processes (use code-review-checklist).

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。