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概要と使いどころ
AWS cost optimization, monitoring, and operational excellence expert. Use when analyzing AWS bills, estimating costs, setting up CloudWatch alarms, querying logs, auditing CloudTrail activity, or assessing security posture.
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。
Validates whether an API request is correct based on provided inputs (method, URL, headers, body, auth, query params).
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。
This skill should be used when the user asks to "process beta reader feedback", "alpha reader feedback", "feedback round", "synthesize reader feedback", "reader notes", "beta feedback", "reader readiness check", "review copy", "send the draft to readers", "share with readers who don't use GitHub", "triage the reader panel", or wants to collect, reconcile, and act on external reader feedback for a story project. NOT for running the simulated reader panel itself (use reader-panel), rounds with a professional editor (use editorial-review), or for checking a manuscript is ready to query or publish (use submission or publishing).
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 "write a sequel", "write a prequel", "start book two", "continue the series", "companion novel", "spin-off", "link books in a series", "carry characters into the next book", "series continuity", "series bible", "pitch my series", "series pitch", "multi-book pitch", or needs to plan, pitch, or keep canon consistent across multiple Story Skills projects. NOT for a standalone book (use story-init), continuity within one book (use revision-continuity), or a query, pitch, or synopsis for a single book (use submission).
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。
Web search using curl + multiple search engines (Baidu, Google, Bing, DuckDuckGo). Activates when user asks to search, look up, or query something online. Includes security enhancements: input sanitization, command injection protection, and URL encoding.
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 the contract for an API change inside one codebase — a component's props, a function surface, URL or query parameters, an event payload, or a module boundary — through a discovery pass, an options document with one recommendation, a question round, and an adversarial validation round, with every element of the contract justified from one stated goal. Use when you want to design, shape, decide, or nail down an interface, contract, signature, or API change for a capability you can already describe, sized for roughly one pull request. Produces a design document and changes no code. Does not specify what a feature should do — use plan-a-feature. Does not plan delivery or sequencing — use plan-implementation. Does not assess the architecture of existing code — use architectural-analysis. Does not write the code — use tdd. Does not restructure existing code — use refactor. Runs its rounds without pausing for review; to review each round as it lands, use pairing.
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, diagnose, and verify performance budgets, latency targets, load tests, capacity estimates, bottleneck analysis, caching strategy, query efficiency, queue throughput, and regression gates. Use when a feature may be slow, a system must scale, a performance regression is suspected, or release readiness depends on throughput, cost, memory, CPU, or response time.
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。
Shioaji Taiwan financial trading API guide. Use when trading stocks/futures/options on Taiwan markets, subscribing to real-time market data, querying account info, or building automated trading systems. Shioaji 台灣金融交易 API 指南。適用於：股票/期貨/選擇權交易、即時行情訂閱、帳務查詢、自動交易系統開發。
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。
Conduct deep web research using the openbrowser-ai agent: decompose a query, investigate sub-questions across multiple sources, and produce a cited markdown report plus structured JSON under local_docs/research/. Trigger when the user asks to: research a topic, do a deep dive, investigate, gather evidence, compare options, write a literature review, build a briefing, or produce a cited report.
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。
Investigate a Rezolus performance recording (.parquet, .rez, .dendro) through the rezolus MCP server. Discovery before query, verdicts and marks written back, links to the viewer.
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 empty results when paginating Wayback Machine CDX API with wildcard URL queries. Use when: (1) CDX query with url=foo/* and page=0 returns empty/0 bytes but works without page parameter, (2) CDX pagination returns no data for wildcard prefix searches, (3) Need to paginate large CDX result sets using wildcard URL matching. Covers showResumeKey, offset, and page parameter incompatibilities.
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 Nostr relay queries using NDK batch fetching. Use when: (1) Checking existence of many events one-by-one is slow, (2) Loop with individual fetchEvents calls causing N+1 query problem, (3) Need to verify multiple addressable events (Kind 30000+) exist. NDK's fetchEvents accepts arrays for tag filters (#d, #p, #e, authors), enabling batch queries that reduce hundreds of round-trips to a single request.
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 silent data loss when mutating Nostr replaceable events (Kind 0 profile, Kind 3 contact/follow list, Kind 10002 relay list, etc.) in client apps. Use when: (1) Following someone wipes the user's entire follow list, (2) Updating profile metadata loses existing fields, (3) Fresh browser session or mobile login causes data loss on first action, (4) Replaceable event mutation uses stale or null cached state. Root cause: Nostr replaceable events are full-replace (no partial update), so publishing based on stale/unloaded cache overwrites the canonical version. Applies to any Nostr client using React, Flutter, or similar reactive frameworks where query state may not be loaded when a mutation fires.
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。
Debug "API returns data that doesn't exist in database" when multiple Kubernetes clusters exist (production, staging, POC). Use when: (1) REST API returns records that direct database queries can't find, (2) Data counts match approximately but specific records don't overlap, (3) kubectl context points to a different cluster than the one serving the public domain, (4) ClickHouse/Postgres queries return stale or different data than the API, (5) "completely different datasets" despite same table names and schemas. Root cause: querying the wrong cluster's database.
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。
Debug ClickHouse deserialization errors in Rust caused by column ORDER mismatch when using SELECT alias.* on a VIEW that adds computed columns. Use when: (1) Some query variants (e.g. sort=trending) return 500 but others (sort=recent) succeed, (2) Queries use SELECT view_alias.*, extra_cols FROM view JOIN ..., (3) The Rust Row struct has the right number and types of fields but deserialization fails anyway (wrong type error, not "not enough data"), (4) The VIEW was recently extended with a computed column (e.g. trending_score).
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 Cloud migration failures caused by multi-table RENAME statements. Use when: (1) Migration fails with "Database X is Shared, it does not support renaming of multiple tables in single query", (2) golang-migrate or other migration tools show dirty database version after a table-swap migration on ClickHouse Cloud, (3) Schema migration works on self-hosted ClickHouse but fails on ClickHouse Cloud. ClickHouse Cloud uses SharedMergeTree engine which has restrictions not present in regular MergeTree.
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。
Fix incorrect priority ordering when using denormalized aggregate columns. Use when: (1) Records are processed in wrong order despite ORDER BY on count/sum columns, (2) Top items by some metric aren't being selected first, (3) Aggregate columns show 0 or NULL for records that should have high values, (4) Priority queue processes low-value items before high-value ones. The root cause is often that denormalized columns (vine_count, loop_count, total_orders, etc.) weren't backfilled or maintained properly. Solution: JOIN with source tables to compute actual aggregates at query time.
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 the user's task involves Ant Design (antd) — writing antd components, debugging antd issues, querying antd APIs/props/tokens/demos, migrating between antd versions, or analyzing antd usage in a project. Triggers on antd-related code, imports from 'antd', or explicit antd questions.
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。
Analyzes and optimizes code for better performance, memory usage, and efficiency. Use when code is slow, memory-intensive, or inefficient. Supports Python and Java optimization including execution speed improvements, memory reduction, database query optimization, and I/O efficiency. Provides before/after examples with detailed explanations of why optimizations work, complexity analysis, and measurable performance improvements.
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 or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common 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。
Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
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 correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
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。