2,828 件 ・ 関連度順
概要と使いどころ
Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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Swift concurrency patterns — actors, structured concurrency, cancellation, async streams, GCD migration, strict-concurrency diagnostics, common bug patterns. Use when writing, reviewing, or debugging concurrent Swift code.
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Formats review deliverables with consistent structure for comparable findings. Use when finalizing any review or analysis that must be shared or compared.
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Implements structured usage logging and audit trails for cost and session tracking. Use when adding audit trails, usage analytics, or cost tracking to a skill.
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Use when reviewing someone else's manuscript for a journal, such as after a review invitation or for a revised R1/R2 version. Drafts a structured, constructive review in the journal's format. Never for your own manuscript; that is /self-review.
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Use when someone has a business problem, strategic question, or founder challenge they need to think through clearly. Triggers on "I'm stuck on", "should I", "what's wrong with my", "diagnose", "strategy problem", "business problem", "founder problem", "help me think through". Runs structured consulting-style diagnostic lenses to produce a comprehensive analysis.
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Use when you want to build a project but don't have a spec yet and need to brainstorm the idea into a design doc structured for pipeline DOT generation. Use when starting from a vague idea, project concept, or feature request that needs to become a headless autonomous build pipeline.
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Use when you have a spec, design doc, structured plan, or prompt_plan and need to generate a pipeline Graphviz DOT file for headless autonomous building. The agent reads the spec, extracts build pipeline phases, determines tech stack, asks about human gates and model preferences, generates a validated DOT digraph with rich self-contained prompts, and saves it to the project root. Handles any language or framework.
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Use AdalFlow to build, debug, evaluate, optimize, trace, and operate LLM task pipelines, RAG systems, agents, tools, and structured-output workflows.
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Analyzes and improves prompts using 31 frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like "help me write a better prompt", "improve this prompt", "what framework should I use", "make this prompt more effective", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, high-quality result.
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 for a "simulated beta read", "reader panel", "persona read", "first read before my beta readers", "how would a genre reader react", "would a reader keep going", "pre-beta read", "mock beta readers", or wants structured persona reads of a chapter range before human readers see it. NOT for real reader feedback (use feedback-triage), a paid sensitivity or authenticity read (use editorial-review), or the agent's own line edit (use line-editing).
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 adding service-wide Spring Boot 3 Actuator, Micrometer metrics, distributed tracing, OpenTelemetry export, structured logging, health probes, dashboards, or alerts. Combine with ai-observability only for Spring AI-specific token, prompt, and model telemetry.
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Use when adding service-wide Spring Boot 4 Actuator, Micrometer Observation and Tracing, OpenTelemetry or OTLP export, structured logging, health probes, dashboards, or alerts. Combine with ai-observability only for Spring AI-specific token, prompt, and model telemetry.
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 integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI 2.0 ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.
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 integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.
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。
Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
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Authorized security assessment of REST, GraphQL, WebSocket, and SOAP APIs: discovery, authentication and authorization flaws (BOLA/IDOR, JWT/OAuth), rate-limit testing, and a structured multi-phase methodology.
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Run structured AI red team exercises for jailbreak resistance, data exfiltration risk, harmful output controls, and agent tool abuse resilience.
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Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.
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Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
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Provides a structured risk scoring methodology for Data Protection Impact Assessments aligned with ENISA threat taxonomy and ISO 29134. Covers likelihood and severity assessment, risk matrix construction, inherent vs residual risk calculation, and risk appetite thresholds per EDPB WP248rev.01 guidance. Keywords: risk scoring, DPIA risk matrix, likelihood, severity, ENISA, ISO 29134, residual risk, risk appetite.
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Determines whether a personal data breach triggers notification obligations under GDPR Articles 33 and 34 using structured risk assessment methodology. Covers breach type classification (CIA triad), data sensitivity scoring, volume assessment, identifiability analysis, and consequence severity evaluation. References EDPB Guidelines 01/2021 with 18 breach scenarios. Keywords: breach risk assessment, GDPR, Article 33, Article 34, EDPB, notification threshold.
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Framework for evaluating and selecting Consent Management Platforms (CMPs). Covers TCF v2.2 certification requirements, Global Privacy Control support, multi-regulation compliance (GDPR, CCPA, LGPD), A/B testing capabilities, API integration options, reporting features, and a structured vendor comparison methodology.
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。