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概要と使いどころ
Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build structured streaming analytics.
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 code diffs and files to identify bugs, security vulnerabilities (SQL injection, XSS, insecure deserialization), code smells, N+1 queries, naming issues, and architectural concerns, then produces a structured review report with prioritized, actionable feedback. Use when reviewing pull requests, conducting code quality audits, identifying refactoring opportunities, or checking for security issues. Invoke for PR reviews, code quality checks, refactoring suggestions, review code, code quality. Complements specialized skills (security-reviewer, test-master) by providing broad-scope review across correctness, performance, maintainability, and test coverage in a single pass.
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。
Configures monitoring systems, implements structured logging pipelines, creates Prometheus/Grafana dashboards, defines alerting rules, and instruments distributed tracing. Implements Prometheus/Grafana stacks, conducts load testing, performs application profiling, and plans infrastructure capacity. Use when setting up application monitoring, adding observability to services, debugging production issues with logs/metrics/traces, running load tests with k6 or Artillery, profiling CPU/memory bottlenecks, or forecasting capacity needs.
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。
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model 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。
Use when building Python 3.11+ applications requiring type safety, async programming, or robust error handling. Generates type-annotated Python code, configures mypy in strict mode, writes pytest test suites with fixtures and mocking, and validates code with black and ruff. Invoke for type hints, async/await patterns, dataclasses, dependency injection, logging configuration, and structured error handling.
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。
Conducts structured requirements workshops to produce feature specifications, user stories, EARS-format functional requirements, acceptance criteria, and implementation checklists. Use when defining new features, gathering requirements, or writing specifications. Invoke for feature definition, requirements gathering, user stories, EARS format specs, PRDs, acceptance criteria, or requirement 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。
Triage recent changes, CI failures, issues, and conversations. Produces a concise, actionable findings report suitable for a loop to consume. Writes structured output to a state file or Linear board.
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。
Takes a structured list of changes from changelog-scan and produces a clean, categorized, user-facing release notes draft. Follows project voice and conventions. Never publishes — only proposes a draft file or section.
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。
Classify CI failures — distinguish clear regressions from infra flakes and security-test failures. Produces structured failure reports.
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。
Scan recent merges to main (and noteworthy direct commits) since a given window (last tag or date in state). Extract titles, labels, types, linked issues, and signals for breaking changes or security. Produces structured input for a release notes drafter. Use in changelog-drafter loops.
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。
Scan recent merges/PRs/commits for release note content. Structured output for drafter.
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。
Scan merged PRs and commits since a given reference, extract titles, labels, types, and signals. Produces structured input for release notes drafting.
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。
Triage recent changes, CI failures, issues, and conversations. Produces a concise, actionable findings report suitable for a loop to consume. Writes structured output to a state file or issue tracker.
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 clear, well-structured emails — replies, outreach, and follow-ups — in the right tone.
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。
Research topics across the web and compile structured insights for content creation.
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。
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.
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。
Required when terminal_* tools are available. Explains foreground execution, outer collect_result handles, promoted job IDs and their retrieval/cancellation, deadlines, output retention, platform shell selection, and structured editing when enabled.
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。
Follow a structured recovery decision tree when tool calls fail instead of blindly retrying or giving up.
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。
Delegate read-only investigation, debugging, audit, search, or code-understanding tasks to sub-agents; synthesize only from their structured reports.
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 a codebase or directory for security issues (hardcoded secrets, injection, unsafe deserialization, weak crypto, authz gaps) and produce a structured findings report. Use when the user asks for a security review, an audit, or to check code for vulnerabilities. Report only — never fix.
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。
Deeper risk assessment for a single feature or product area when the launch review found something that needs more than a line item. Structured analysis: what could go wrong, how likely, how bad, what mitigates it. Use when user says "deep dive on this risk", "risk assessment for [feature]", "what could go wrong with", or when launch-review flags a novel issue.
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-drafting context gathering for a demand letter — parties, facts, basis, leverage, BATNA, and privilege filters — written to a structured intake.md the demand-draft skill reads. Use when the user wants to prep a demand letter, run intake before drafting, or capture context for a payment demand, breach/cure notice, cease-and-desist, employment separation, or preservation demand.
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。
Intake a new matter — uniform questions covering identification, conflicts, source, risk triage, materiality, outside counsel, owners, legal hold, and key dates; writes matter.md and history.md and appends a structured row to _log.yaml. Use when the user says "new matter", "intake this matter", or wants to bring a new matter into the portfolio.
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 intake — practice-area templates, cross-area issue spotting, conflict flags, and triage classification. Produces a formatted case summary the student analyzes and the professor reviews. Does NOT decide case acceptance. Use when starting a new client intake, running an intake interview, or writing up a new client's situation.
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。