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概要と使いどころ
Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cache_read vs input), compaction pressure (baseline tokens), turn velocity (turn_count / total_turn_duration_ms), tool success ratio (PreToolUse vs PostToolUse), and the workflow intelligence API's complexity and effectiveness scores.
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。
Generate a comprehensive session report with per-model token usage (input, output, cache_read, cache_write including compaction baselines), cost breakdown via the pricing engine, tool invocations, agent hierarchy, compaction events, API errors, turn durations, and thinking block counts. Use when reviewing a specific session or summarizing activity over a date range.
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。
Break down Claude Code costs using the Agent Monitor pricing engine. Shows per-model costs (input, output, cache_read, cache_write at $/Mtok rates), per-session costs, daily trends, and compaction baseline token recovery. Use when analyzing spending, comparing model costs, or planning budgets.
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。
Compare two sessions side-by-side using Agent Monitor data — per-model token usage (input/output/cache_read/cache_write + compaction baselines), pricing engine cost breakdowns, workflow intelligence (complexity scores, tool flow transitions, subagent effectiveness), session metadata (thinking_blocks, turn_count, turn_duration_ms, usage_extras), and full event timelines with all 10+ event types.
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。
Hit the Cloudflare REST API directly for operations that wrangler and MCP can't handle well. Bulk DNS, custom hostnames, email routing, cache purge, WAF rules, redirect rules, zone settings, Worker routes, D1 cross-database queries, R2 bulk operations, KV bulk read/write, Vectorize queries, Queues, and fleet-wide resource audits. Produces curl commands or scripts. Triggers: 'cloudflare api', 'bulk dns', 'custom hostname', 'email routing', 'cache purge', 'waf rule', 'd1 query', 'r2 bucket', 'kv bulk', 'vectorize query', 'audit resources', 'fleet operation'.
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 answering Scenario admin analytics questions about Creative Unit consumption, most-used models, usage by user or project, AI adoption observability, cached usage data, CSV exports, enterprise PDF reports with charts, or a local analytics dashboard. Keywords: usage API, CU, analytics, adoption, reporting, consumption, cache.
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。
This skill should be used when the user asks to "calculate MRR", "analyze churn", "compute SaaS metrics", "do cohort retention analysis", "calculate LTV or CAC", "evaluate unit economics", or "track subscription revenue growth".
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 a mastery experience sequence that systematically builds student confidence in a skill they avoid. Use when students say 'I can't do this', avoid tasks, or show learned helplessness.
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。
Code generator skills that produce production-ready Swift code for common app components. Use when user wants to add logging, analytics, onboarding, review prompts, networking, authentication, paywalls, settings, persistence, error monitoring, CI/CD pipelines, localization, push notifications, deep linking, testing, accessibility, widgets, feature flags, app icons, image caching, pagination, HTTP caching, share cards, social export, subscription lifecycle, referral systems, watermarks, streak tracking, milestone celebrations, what's new screens, lapsed user re-engagement, usage insights, variable rewards, consent flows, account deletion, permission priming, force updates, state restoration, debug menus, offline queues, feedback forms, announcement banners, quick win sessions, Spotlight indexing, App Clips, screenshot automation, background processing, app extensions, data export, or SwiftUI preview sample data and variant matrices.
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 knowledge for Azure Managed Redis development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when using Redis tiers, Entra ID auth, clustering/sharding, persistence/geo-replication, or ARM/Bicep deployments, and other Azure Managed Redis related development tasks. Not for Azure Cache for Redis (use azure-cache-redis).
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 knowledge for Azure Large Instances development including troubleshooting, limits & quotas, and integrations & coding patterns. Use when configuring Epic SKUs, sizing volume groups, tuning EHR storage, or resolving Epic–ALI connectivity/perf issues, and other Azure Large Instances related development tasks. Not for Azure Baremetal Infrastructure (use azure-baremetal-infrastructure), Azure Virtual Machines (use azure-virtual-machines), Azure Virtual Machine Scale Sets (use azure-vm-scalesets), Azure HPC Cache (use azure-hpc-cache).
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。
Cache the parts of the prompt that don't change so a long-running loop stops paying full price on every turn. Use when the system prompt, tool defs, or reference docs are stable across many turns.
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。
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.
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。
Dung khi da co MOT master design va can ra du kich thuoc cho tat ca kenh — bang kich thuoc chuan 2025-2026 cho feed, story, reel, cover, placement ads; 3 cach xu ly scale-crop, re-compose, rebuild; quy tac safe zone va cach dat ten file. Kich hoat khi user nhac 'resize asset', 'du kich thuoc', 'adapt kich thuoc', 'bang kich thuoc chuan', 'safe zone', 'anh bi cat mat chu tren story', 'mot anh dang duoc may cho'. Khong dung cho — thiet ke moi tu concept thi dung skill 42-brief-hinh-anh; asset gap khi campaign dang chay thi dung skill 48-quick-visual-brief; liet ke toan bo asset cua campaign thi dung skill 41-campaign-asset-list.
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 Fetch API, Caching, and Revalidation strategies in Next.js. Use when fetching data, configuring cache behavior, or implementing revalidation in Next.js.
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。
Optimize Laravel queries with subqueries, joinSub, Redis cache-aside patterns, and read/write connection splitting. Use when writing complex joins, implementing Cache::remember with tags, or configuring database read replicas.
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。
Optimize Redis as cache and coordination infrastructure with TTL, eviction, and latency-aware key design. Use when implementing Redis caching, key invalidation, or Redis performance work.
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 context and prompt-cache costs without losing task state. Use for context management, orchestration cost, token budgets, large tool outputs, cache misses, or long-running conversations.
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 you need to design, review, or improve REST APIs with Quarkus REST (Jakarta REST) — including resource classes, HTTP methods, status codes, request/response DTOs, Bean Validation, exception mappers, optional runtime OpenAPI exposure (SmallRye), contract-first generation from OpenAPI, content negotiation, pagination, sorting and filtering, API versioning, idempotency (Idempotency-Key), optimistic concurrency (ETag / If-Match), HTTP caching (Cache-Control), API deprecation (Sunset / Deprecation headers), RFC 7807 Problem Details, ISO-8601 for time in contracts, and security-aware boundaries. This should trigger for requests such as Review or improve JAX-RS resources in a Quarkus project; Design HTTP APIs with validation and error handling on Quarkus; Add API versioning, idempotency, ETag concurrency, or deprecation headers; Implement pagination, sorting, or RFC 7807 Problem Details error responses; Improve Quarkus REST resources and exception mappers. Part of Plinth Toolkit
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。
Quick reference for RESTful API design patterns, HTTP semantics, caching, and rate limiting. Triggers on: rest api, http methods, status codes, api design, endpoint design, api versioning, rate limiting, caching headers.
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 AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders, parameters, Outputs and cross-stack references. Use when creating CloudFront distributions with CloudFormation, configuring multiple origins, implementing caching strategies, managing custom domains with ACM, configuring WAF, and optimizing performance.
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。
Compress-Cache-Retrieve huge repetitive array tool output before it hits context: keep a sample, cache the rest, leave a hash.
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。
Compress-Cache-Retrieve for huge, repetitive array tool output (logs, scan results, time series, event streams) before it enters context. Keeps an informative sample — endpoints, anomalies/change-points, head/tail — drops the redundant rest to a local cache, and leaves a retrievable hash. Use when a tool returns a long uniform JSON array you must read but mostly skim, and the full set is one command away if needed. Lossy-but-recoverable.
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。
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).
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。