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Expert patterns for HubSpot CRM integration including OAuth authentication, CRM objects, associations, batch operations, webhooks, and custom objects. Covers Node.js and Python SDKs. Use when: hubspot, hubspot api, hubspot crm, hubspot integration, contacts api.
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This skill should be used when containerizing applications with Docker, creating Dockerfiles, docker-compose configurations, or deploying containers to various platforms. Ideal for Next.js, React, Node.js applications requiring containerization for development, production, or CI/CD pipelines. Use this skill when users need Docker configurations, multi-stage builds, container orchestration, or deployment to Kubernetes, ECS, Cloud Run, etc.
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Text-to-speech conversion using node-edge-tts npm package for generating audio from text. Supports multiple voices, languages, speed adjustment, pitch control, and subtitle generation. Use when: (1) User requests audio/voice output with the "tts" trigger or keyword. (2) Content needs to be spoken rather than read (multitasking, accessibility, driving, cooking). (3) User wants a specific voice, speed, pitch, or format for TTS output.
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。
Universal coding standards, best practices, and patterns for TypeScript, JavaScript, React, and Node.js development.
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。
Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.
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。
BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.
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 backend development guide for Node.js/Express/TypeScript microservices. Use when creating routes, controllers, services, repositories, middleware, or working with Express APIs, Prisma database access, Sentry error tracking, Zod validation, unifiedConfig, dependency injection, or async patterns. Covers layered architecture (routes → controllers → services → repositories), BaseController pattern, error handling, performance monitoring, testing strategies, and migration from legacy 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。
Expert patterns for Azure Functions development including isolated worker model, Durable Functions orchestration, cold start optimization, and production patterns. Covers .NET, Python, and Node.js programming models. Use when: azure function, azure functions, durable functions, azure serverless, function app.
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。
Query and manage personal finances via the official Actual Budget Node.js API. Use for budget queries, transaction imports/exports, account management, categorization, rules, schedules, and bank sync with self-hosted Actual Budget instances.
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A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.
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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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Principled action selection for autonomous agents using Expected Free Energy (EFE) under the Active Inference framework. Each candidate action is scored as G(n) = w_prag * (-E[p_n] * damping) + w_epist * (-Var[p_n] * novelty(n)), where agent beliefs are maintained as per-node Beta distributions and updated via Bayesian observation. Selection is a softmax over -gamma * G, making exploration an automatic consequence of uncertainty rather than a hardcoded epsilon. Precision weights w_prag and w_epist self-adapt via surprise history, shifting the agent between exploitation and exploration regimes without any manually tuned schedule.
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Decompose DAGs into execution chains using width bounds, stratification, virtual nodes, and matching. Use when minimizing workflow lanes in dependency graphs. NOT for cyclic graphs, generic scheduling, or one-off queries with no preprocessing payoff.
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 every destructive/gated action an agent body can attempt (git, filesystem, network, shell, github) into block/approve/allow tiers, and audit whether the pre-tool and post-tool governance backing that classification is real: blocked actions proven zero-side-effect by a negative fixture, every denial backed by a receipt and a transcript event, every block-tier denial paired with a safe alternative, and no unmanaged or same-UID body ever marked "contained." Use when building or reviewing Agent Harbor's C5 governance gate (destructive git blocker, approval request/result, denial receipts), gating a new tool surface before it can run destructive commands, or auditing whether an existing "destructive-action blocked" claim actually has evidence behind it. NOT for proving an existing sandbox boundary actually contains an adversary once an action is isolated (use sandboxed-adversarial-test-harness), deciding where in a DAG to place a human review node or designing the approval UX itself (use human-gate-designer), or designing the general work-receipt schema across an entire agent task (use agent-work-receipt-designer).
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。
Debugs recorded DAG lineage through historical inspection, deterministic decision replay when prerequisites hold, and clearly labeled counterfactual or live re-execution. Activate on "debug DAG", "replay execution", "inspect node state", "compare runs", or "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.
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。
Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).
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。
Matches natural language task descriptions to appropriate skills using semantic similarity, ranks candidates by fit and performance history, and maintains the skill catalog. Use when assigning skills to DAG nodes, searching for the right skill for a task, ranking competing skills, or browsing the skill catalog. Activate on "find skill", "match skill", "which skill", "skill for this task", "skill catalog", "rank skills", "best skill". NOT for executing DAGs (use dag-runtime), creating skills (use skill-architect), or grading skills (use skill-grader).
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。
The intelligence layer of Jury-rig. Decomposes natural language tasks into proposed Hierarchical Task DAGs (HTDAGs), matches subtasks to skills, proposes wave and revision contracts, and expands nodes only when evidence supports a revised graph. Use for 'orchestrate', 'execute DAG', 'parallel agents', 'decompose task', 'coordinate skills'. NOT for single-skill tasks or simple linear 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。
Parses complex problems into candidate DAG (Directed Acyclic Graph) execution structures. Decomposes tasks into nodes with dependencies and identifies candidate parallelization opportunities. Activate on 'build dag', 'create workflow graph', 'decompose task', 'execution graph', 'task graph'. NOT for simple linear tasks or when an existing DAG structure is provided.
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。
Builds scoped, provenance-preserving context packets between DAG nodes. NOT for execution or aggregation.
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Validates typed DAG dependency declarations, returns Kahn linear extensions or generations and residual cycle witnesses, and identifies changed consumers for revalidation. NOT for constructing graphs, scheduling resource capacity, granting effects, or executing nodes.
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Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).
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Analyze hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.
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Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget. Downgrades models, skips optional nodes, or stops early when cost exceeds thresholds. Use when managing execution budgets, analyzing cost breakdowns, or optimizing model routing for cost. Activate on "cost budget", "too expensive", "reduce cost", "cost optimization", "model downgrade", "budget exceeded". NOT for LLM model selection logic (use llm-router), pricing comparisons across providers, or billing/invoicing.
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。