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概要と使いどころ
Compatibility router for semantic skill tightening. Delegate goal recovery, authority, semantic conservation, evaluation contracts, and instruction optimization to Skill Lapidary rather than duplicating that system in Plugin Creator.
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。
Evaluate and iterate on the SDLC Layer Separation Architecture implementation. Runs validation checks (cross-references, doc completeness, layer metadata, integration points), produces a findings report, and supports iterative fixes. Use when validating first-pass implementation, before claiming layer work is complete, or when improving layer docs/schema.
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 and iterate on the SDLC Layer Separation Architecture implementation across the check categories defined in its Evaluation Checklist — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.
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, create, run, monitor, and maintain Vapi Simulations for assistants and squads. Use for turning plain-language requirements into a coverage plan, simulation personalities, scenarios, structured-output success criteria, simulations, suites, extending an existing suite, chat or voice runs, tool mocks, target variables, lifecycle webhooks, regression coverage, launch readiness, CI quality gates, run-result analysis, and simulation API validation errors. Do not use for fixed-turn mock-conversation Evals unless the user is deciding between Evals and Simulations.
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。
Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.
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。
Pattern for progressively refining context retrieval to solve the subagent context problem
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。
Codified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis, hedging strategies, load profiling, and sustainability reporting frameworks. Use when procuring energy, optimizing tariffs, managing demand charges, evaluating PPAs, or developing energy 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。
Enhanced web search and real-time content retrieval via Grok API with forced tool routing. Use when: (1) Web search / information retrieval / fact-checking, (2) Webpage content extraction / URL parsing, (3) Breaking knowledge cutoff limits for current information, (4) Real-time news and technical documentation, (5) Multi-source information aggregation. Triggers: "search for", "find information about", "latest news", "current", "fetch webpage", "get content from URL". IMPORTANT: This skill REPLACES built-in WebSearch/WebFetch with Grok Search tools.
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 and operate multi-agent orchestration patterns (ReAct loops, evaluator-optimizer, orchestrator-workers, tool routing) for LLM systems. Use when building or debugging agent workflows, tool-use loops, or multi-step task delegation; triggers: agentic, multi-agent, orchestration, ReAct, evaluator-optimizer, tool-use, handoff.
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 and implement evaluation frameworks for AI agents. Use when testing agent reasoning quality, building graders, doing error analysis, or establishing regression protection. Framework-agnostic concepts that apply to any SDK.
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。
Evaluate agent performance using a structured scoring rubric
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。
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
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。
Deterministic custom subagent selection helper. Use when you need a reproducible, auditable decision on which custom subagents to activate for a user query (runs scripts/agent_evaluator.py).
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。
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
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 RAG (Retrieval-Augmented Generation) agents with Google ADK and Vertex AI RAG Engine. Use when implementing document Q&A, knowledge base search, or citation-backed responses. Covers VertexAiRagRetrieval tool, corpus setup, and citation formatting.
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 and query a local Markdown knowledge base ("vault"). TWO functions — (1) CONVERT raw files (PDF, Word/docx, PowerPoint/pptx, Excel/xlsx, csv/tsv, images, html, md/txt, json/yaml/code, audio/video) into clean Markdown with retrieval-friendly frontmatter; local-first (pandoc / python-pptx / openpyxl / pymupdf4llm / whisper), with cloud OCR (MinerU) only as a fallback. (2) ANSWER questions over the resulting vault with retrieval discipline — self-monitor coverage, flag missing/lossy content, and propose Maps-of-Content (MOCs). Triggers: "build/sync my local knowledge base", "convert these files to markdown for AI", "整理我的资料库", "把文件转成 md 给 AI 读", "本地知识库", "读我的本地 vault 回答", "这个主题我的资料里怎么说". Not for: one-off web research, or files that are already in a single doc you can read directly.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for prompt-injection, retrieval poisoning, memory contamination, planner drift, MCP or tool-boundary abuse, and agent exfiltration challenges. Use when the user asks to analyze prompt injection, retrieval poisoning, memory contamination, planner drift, tool-argument corruption, or secret exposure caused by an agent chain. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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 Kiln to create eval datasets, rate outputs, optimize prompts and models, and ship the same AI task or agent definition from a local workbench into production 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。
Use Google Agent Development Kit to define, test, evaluate, and deploy code-first Python agents with tools, sessions, HITL confirmation, and multi-agent orchestration.
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。
Scaffold, run, inspect, and evaluate model-agnostic agents that connect to MCP servers, skills, shell tools, and workflow packs.
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。
Score deep research agents on benchmark tasks using factual verification, report-quality scoring, and process evaluation before model or workflow changes ship.
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。
Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.
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。
Nature-style remote-sensing manuscript reviewer skill for evaluating Earth-observation products, retrievals, machine-learning maps, validation design, uncertainty propagation, spatial transferability and claim-scope calibration.
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 open-source packages: health, suitability, dependency risk, and in-house alternatives.
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。