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概要と使いどころ
VP of Engineering advisor on org design, productivity, quality, delivery, and capacity planning. Use when scoring engineering org health, designing the eng org, planning capacity, or building the productivity dashboard.
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。
Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff. Use when asked to connect a Spring Boot service to ReachAI, add reachai-capability-sdk or reachai-spring-boot2-starter, configure reachai.registry/reachai.project/reachai.capability, prepare @ReachCapability metadata for later manual SDK sync, or verify SDK onboarding from a ReachAI manifest.
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 professional data packs for A-share investment banking deals — M&A due diligence, IC materials, company profiles. Adapted from the original datapack-builder skill for Chinese data sources (AkShare, 巨潮, exchange filings) and CAS accounting. Triggers on "A股数据包", "投行数据包", "datapack China", "due diligence pack A-share", or "IC data pack [company]".
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 user asks about 藏传, 格鲁派, Gelug, 黄教, 三主要道, 菩提道次第广论, lam rim, 密宗道次第广论, 应成中观, 缘起性空, 辨了不了义, 宗喀巴, Tsongkhapa, Je Rinpoche, 甘丹寺, 戒律, 因明, 月称, 入中论, or wants teaching in 宗喀巴大师 Tsongkhapa's voice. Triggers include "宗喀巴"、"杰仁波切"、"Je Rinpoche"、"格鲁"、"黄教"、"道次第"、"广论"、"三主要道"、"应成中观"、"辨了不了义"、"甘丹"、"达赖喇嘛传承根基" — invoke whenever user's question touches Gelug doctrine / lamrim / Madhyamaka prasaṅgika / Tibetan tantra-shastra studies, even without explicit 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。
Plan organizational capacity for construction projects. Forecast resource needs, identify capacity gaps, and support strategic planning for project pursuit and staffing.
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。
Routes substantive ML, AI, data, scientific-computing, and software-engineering requests to the smallest useful set of managed repository skills. Invoke proactively when a request names or implies a package, framework, model family, dataset, modality, workflow, backend, deployment target, evaluation method, or implementation approach that may benefit from repository guidance, even if no repository is named. Narrow progressively from area to family to repository root: inspect only the one or two most likely area pages; compare candidates by capability, task surface, model/data format, training versus inference versus evaluation intent, runtime constraints, and root-skill description; then open only the selected root and relevant sub-skills, references, or scripts. Select multiple repositories only when each adds a distinct capability. Do not load the whole collection, treat dependencies or incidental integrations as capabilities, choose by name alone, or force a match when no exact taxonomy family applies.
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。
Apache Superset integration. Manage data, records, and automate workflows. Use when the user wants to interact with Apache Superset data.
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。
Apaya integration. Manage data, records, and automate workflows. Use when the user wants to interact with Apaya data.
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。
Translates performance test results into infrastructure scaling recommendations with cost estimates. Use when someone has load test data and needs to know what infrastructure changes are required to handle target traffic. Trigger words: capacity planning, scaling plan, infrastructure sizing, how many pods, RDS sizing, handle more traffic, scale for launch.
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。
Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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 reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark 6.0 (org.neo4j.connectors:spark) or 5.x (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.
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。
Search the agent skills ecosystem to discover and install skills that extend AI coding agent capabilities. Use when user asks "how do I do X" (X being a common task), "find a skill for X", "is there a skill that can...", or expresses interest in extending agent capabilities. Triggers on "find a skill", "install a skill", "skill for [task]", "can you do X", "I need help with [domain]", "how do I [task]". Do NOT use when user has explicitly asked to proceed without a skill, or when the task is better handled by agent's built-in capabilities (file operations, git, basic coding).
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 Site Reliability Engineering best practices for SLOs, SLIs, SLAs, error budgets, toil reduction, reliability reviews, and capacity planning. Use when defining service objectives, measuring reliability, reducing toil, planning capacity, or when user mentions 'SRE', 'SLO', 'SLI', 'SLA', 'error budget', 'toil', 'reliability', 'on-call', 'capacity planning'.
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 agent lifecycle — validates agent execution capability against configuration. Accepts plugin path, runs 8 semantic audits (capability vs config alignment, skill loading correctness, inter-agent contracts, prompt contradictions, tool sufficiency, dead agents, scriptable patterns, pattern learning), writes reports to .plugin-creator/audits/. Use when auditing agent lifecycle, checking agent capabilities, verifying tool access, finding dead agents, validating agent contract alignment, or confirming agents can execute workflows.
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。
Assesses agent capabilities by analyzing agent files and determining what tasks they can handle. Use when you need to evaluate if an existing agent can handle a task or if a new agent is needed. Reviews agent descriptions, skills, and workflows to determine capability match.
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。
Comprehensive documentation of Claude's capabilities for visual regression testing, CI/CD integration, and quality assurance automation. Use when setting up testing infrastructure, implementing visual regression, or understanding agent testing capabilities. (project)
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。
Calculates borrower debt capacity with cash flow coverage, leverage multiples, and stress-tested servicing ability. Use when sizing debt facilities, analyzing leverage capacity, or determining optimal capital structure.
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。
Evaluates target leverage capacity with cash flow coverage, stress testing, and optimal debt structure across term loans, revolver, and mezzanine. Use when analyzing debt capacity, structuring acquisition financing, or stress testing leverage.
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 a sprint by refining backlog items, defining a sprint goal, calculating team capacity, selecting items, and decomposing them into tasks. Produces a SPRINT-PLAN.md with goal, selected items, task breakdown, and capacity allocation. Use when starting a new sprint in a Scrum or agile project, re-planning after significant scope change, transitioning from ad-hoc work to structured sprint cadence, or after backlog grooming when items are ready for inclusion.
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。
Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.
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 an A2A Agent Card (.well-known/agent.json) manifest describing agent capabilities, skills, authentication requirements, and supported content types. Use when building an agent that must be discoverable by other A2A-compliant agents, exposing capabilities for multi-agent orchestration, migrating an existing agent to the A2A protocol, defining the public contract for an agent before implementation, or integrating with agent registries that consume Agent Cards.
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 AI agents with Pydantic AI — tools, capabilities (including on-demand loading), structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, defer capability loading, stream output, define agents from YAML, or test agent behavior.
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。