1.9万 件 ・ 関連度順
概要と使いどころ
Ultra-compressed communication mode. Cuts token usage ~75% by dropping filler, articles, and pleasantries while keeping full technical accuracy. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman.
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-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI 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。
Plan and execute Azure deployments safely and repeatably for applications and infrastructure. Use this skill when users ask to deploy, publish, release, or provision workloads in Azure. Covers Infrastructure-as-Code, idempotency, health validation, and rollback strategies.
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 unit and integration tests for Angular v20+ applications using Vitest or Jasmine with TestBed and modern testing patterns. Use for testing components with signals, OnPush change detection, services with inject(), and HTTP interactions. Triggers on test creation, testing signal-based components, mocking dependencies, or setting up test infrastructure. Don't use for E2E testing with Cypress or Playwright, or for testing non-Angular JavaScript/TypeScript code.
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 Azure application and infrastructure issues with a structured, evidence-based workflow. Use this skill when users ask to troubleshoot outages, latency, failures, configuration drift, or anomalies. Root cause analysis using logs, metrics, traces, and dependency mapping.
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。
Locate and summarize Azure resources quickly across scopes using consistent query patterns. Use this skill when users ask where resources are, what they are configured as, or need inventory by type, region, or tag.
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 and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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。
Validate the GenAI stack (services, authentication, models, notebooks, GPU). Arguments: [all|services|auth|models|notebooks|vram] [--full] [--auth-only] [--nodes-only] [--notebooks]
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。
Pedagogical patterns for enriching notebooks (GameTheory model). Use when adding interpretations, structuring notebooks, or creating educational content in Jupyter notebooks.
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。
Cycle 30 min du secrétaire vérificateur myia-po-2026:CoursIA-3. Atteste en tiers, du plus ancien au plus récent, les PRs ouvertes sans dossier valide (chaque READY, une fois lu et approuvé par ai-01, est mergé sans attendre son cycle), et fait circuler l'information sur workspace-CoursIA-3.
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 insights from MetaGPT paper on how structured communication, role specialization, and SOPs prevent coordination failures in multi-agent LLM systems
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。
Monetization strategies for indie developers, solopreneurs, and small teams. Covers freemium models, SaaS pricing, sponsorships, donations, email list building, and passive income for developer tools, content sites, and educational apps. Activate on 'monetization', 'make money', 'pricing', 'freemium', 'SaaS', 'sponsorship', 'donations', 'passive income', 'indie hacker'. NOT for enterprise sales, B2B outbound, VC fundraising, or large-scale advertising (use enterprise/marketing skills).
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。
Compassionate bereavement support, memorial creation, grief education, and healing journey guidance. Specializes in understanding grief stages, creating meaningful tributes, and supporting the non-linear path of loss.
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 image generation pipelines with Stable Diffusion, FLUX, ControlNet, LoRA, and ComfyUI workflows. Activate on: image generation pipeline, ComfyUI workflow, ControlNet, LoRA training, diffusion model. NOT for: video generation (ai-video-production-master), image classification (computer-vision-pipeline).
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。
Implementing the `Idempotency-Key` HTTP header pattern (Stripe-pioneered, IETF-standardizing) so retries don't double-charge or duplicate side effects — full Postgres schema with recovery-point state machine, fingerprint check, in-progress lock with 409 response, and 24h retention. Grounded in Stripe blog/docs, the IETF httpapi WG draft, and Brandur Leach's canonical implementation.
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。
Application of Six Sigma quality methodology to AI agent process improvement and reliability
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 CPU usage is high, memory grows unboundedly, goroutines leak, mutex contention shows in traces, or escape analysis suggests excess heap allocation. Triggers: net/http/pprof endpoint exposure, go tool pprof analysis, flamegraph generation, allocs vs inuse_space heap profiles, runtime/trace event timeline, mutex profiling, block profiling, goroutine dumps, GC pressure measurement. NOT for non-Go languages, distributed tracing (use OpenTelemetry skill), or production telemetry 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 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。
Process, analyze, and visualize geospatial data at scale. Handles drone imagery, GPS tracks, GeoJSON optimization, coordinate transformations, and tile generation. Use for mapping apps, drone data processing, location-based services. Activate on "geospatial", "GIS", "PostGIS", "GeoJSON", "map tiles", "coordinate systems". NOT for simple address validation, basic distance calculations, or static map embeds.
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 for sensemaking, frame revision, anomaly handling, questioning strategy, and ambiguity diagnosis with the Data/Frame model before action is chosen. NOT for deterministic classification, settled execution planning, or situations where the frame is already stable.
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。
FIPA standard for agent platform management including lifecycle, directory, and communication services
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。
Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-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。
FIPA standard library of agent interaction protocols for structured multi-agent communication
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。