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Query and answer questions about Volcengine API specifications. Trigger this skill whenever a user asks about Volcengine API parameters, error codes, request methods, enum values, required fields, response structures, pagination, parameter dependencies, or API comparisons — even if they don't explicitly say "API". Typical triggers include questions like "What parameters does DescribeInstances have?", "What values does Status support?", "What does InvalidInstanceId.NotFound mean?", "Does Volcengine have a batch tag deletion API?", "Which APIs does ECS support?", "How do I pass parameters to CreateDatabase?", "Is ChargeType required?", "What fields does DescribeInstances return?", "How do I paginate instance lists?", "What's the difference between these two APIs?", "RunInstances returns InvalidParameterValue". When the user needs runnable SDK code, hand off to the volcengine-sdk-generator skill. When the user needs CLI-based operations, hand off to the volcengine-cli skill. Supports both Chinese and English prompts.

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Generate complete, runnable Volcengine SDK code and provide SDK configuration guidance. Supports Go, Python, PHP, Java, and Node.js. Trigger this skill whenever the user wants to call a Volcengine API, generate Volcengine SDK code, or describes a cloud operation on Volcengine (e.g., "list ECS instances", "create a VPC on Volcengine", "query Volcengine billing with Python"). Also trigger when the user asks about Volcengine SDK configuration and best practices — including retry, timeout, authentication (AK/SK, STS, AssumeRole), proxy, connection pooling, SSL, debug mode, and error handling (e.g., "how to configure retry for Volcengine Go SDK", "volcengine python sdk proxy setup"). Trigger when the user mentions Volcengine service names such as ECS, VPC, CDN, CLB, RDS, Redis, Kafka, billing, IAM, DNS with code generation or SDK usage intent. When the user only needs API specification queries (parameters, error codes, response structures), hand off to the volcengine-api skill. When the user needs CLI-based operations, hand off to the volcengine-cli skill. Supports both Chinese and English prompts.

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火山方舟 Seedance 模型使用指导技能。用户询问 Seedance/seedance2.0/doubao-seedance-2-0/doubao-seedance-2-0-fast/seedance2.0-mini等seedance系列模型的火山方舟接入、API 调用、参数、任务创建、任务查询、结果获取、计费开通、报错排查、SDK、最佳实践时优先调用本技能；当用户提供火山引擎官网文档链接（形如 https://www.volcengine.com/docs/*）并要求解读、总结、提炼、查询其中规则或基于文档回答时，也应调用本技能并通过 fetch 获取文档全文；普通提问使用 ServiceCodes: ark 检索火山方舟官方文档。

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。

Converts and transcodes audio file formats and encoding parameters using Volcengine LAS. Audio format conversion between wav, mp3, flac, m4a, ogg, aac and other audio formats. Adjusts sample rate (resample, downsample, upsample), bit rate (bitrate), channels (mono, stereo, channel mixing), audio compression, and audio quality settings via ffmpeg parameters. Supports TOS cloud storage paths and local file upload. Use this skill when the user wants to convert audio format (wav/mp3/flac/m4a/ogg/aac), transcode or re-encode audio files, adjust audio sample rate, bitrate, channels, compress audio files, resample or downsample audio, prepare audio for downstream tasks, or do any audio preprocessing.

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Use when a kernel dump contains Driver Verifier violations; inspect flags, bugcheck subcodes, and available I/O shadow state. Not for Application Verifier user-mode stops or inferring a violation from enabled flags alone.

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Generate genuinely beautiful, on-brand UI instead of generic "AI slop" — use whenever the user asks to build, design, style, or improve a UI, landing page, dashboard, app screen, or component, whenever a PRD/spec needs a design pass before implementation, whenever the user pastes reference images/Pinterest/Dribbble links and wants the app to look like them, or whenever the user complains the AI-generated UI looks generic, boring, cookie-cutter, or "like every other AI app." Make sure to trigger this even if the user doesn't say "design" explicitly — phrases like "make this look good", "build the frontend for X", "this looks like every other SaaS site", or "match this vibe" all qualify. Also triggers on two verbs, "study"/"extract the look of" a reference screenshot or URL, and "audit"/"review"/"why does this look AI-generated" for critiquing existing UI.

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Turn a concept, feature description, blog post, or pitch into a single beautiful, on-brand illustration by matching it to a real unDraw illustration in a local library and recoloring it to the brand accent — genuine illustrator quality, not an AI-drawn approximation. Use whenever the user asks to "make an illustration," "create a graphic," "visualize this concept," "explain this visually," wants something "unDraw-style" or "Storyset-style," needs a hero/feature/blog illustration, or says an idea needs a picture. Trigger even if they don't name a style — "make something to explain X" or "I need a graphic for this tweet" both qualify.

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Use when you need to design, review, or improve REST APIs with Spring Boot — including HTTP methods, resource URIs, status codes, DTOs, versioning, deprecation and sunset headers, content negotiation (JSON and vendor media types), ISO-8601 instants in DTOs, pagination/sorting/filtering, Bean Validation at the boundary, idempotency, ETag concurrency, HTTP caching, error handling, security, contract-first OpenAPI (OpenAPI Generator), controller advice, and problem details for errors. This should trigger for requests such as Review Java code for Spring Boot REST API; Apply best practices for Spring Boot REST API in Java code; Design Spring Boot REST controllers and DTOs; Add Problem Details error responses in Spring Boot REST; Improve pagination validation or idempotency in Spring APIs. Part of Plinth Toolkit

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Use when you need to design, review, or improve REST APIs with Quarkus REST (Jakarta REST) — including resource classes, HTTP methods, status codes, request/response DTOs, Bean Validation, exception mappers, optional runtime OpenAPI exposure (SmallRye), contract-first generation from OpenAPI, content negotiation, pagination, sorting and filtering, API versioning, idempotency (Idempotency-Key), optimistic concurrency (ETag / If-Match), HTTP caching (Cache-Control), API deprecation (Sunset / Deprecation headers), RFC 7807 Problem Details, ISO-8601 for time in contracts, and security-aware boundaries. This should trigger for requests such as Review or improve JAX-RS resources in a Quarkus project; Design HTTP APIs with validation and error handling on Quarkus; Add API versioning, idempotency, ETag concurrency, or deprecation headers; Implement pagination, sorting, or RFC 7807 Problem Details error responses; Improve Quarkus REST resources and exception mappers. Part of Plinth Toolkit

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Use when you need to design, review, or improve REST APIs with Micronaut — including @Controller routes, HTTP status codes, DTOs, Bean Validation, exception handlers, pagination, idempotency, ETag/If-Match, caching headers, versioning, contract-first OpenAPI (OpenAPI Generator), optional runtime OpenAPI via micronaut-openapi, and security annotations. This should trigger for requests such as Review or improve Micronaut @Controller REST APIs; Add validation, error handling, or align controllers with the OpenAPI contract on Micronaut HTTP layer; Design Micronaut controllers and DTO validation; Add Micronaut exception handlers and Problem Details responses; Improve pagination filtering or OpenAPI alignment in Micronaut APIs. Part of Plinth Toolkit

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。

Decision protocol for wrapping a REST / GraphQL / RPC API as a tool an LLM agent can call. The load-bearing premise: the *tool surface* is an LM-friendly subset of the *API surface* — one tool per user intent, not one per endpoint. Activates when a coder agent must expose an external HTTP API to a model (function calling, tool_use, MCP, LangChain `@tool`, CrewAI `BaseTool`). Encodes the *what to surface, how to name, how to shape, how to fail* — not any single framework's API. ~80% of agent tools in production are HTTP wrappers; this is the SOP for getting them right.

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Re-ingest-correctness SOP for production RAG. Activate when a calling agent builds, reviews, or debugs an ingestion pipeline that runs more than once over a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule — **ingestion must be idempotent: a document's content hash decides insert/update/skip, so re-running over unchanged docs is a no-op** — plus the docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` + `DocstoreStrategy`), the delete-propagation problem, and cross-framework equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE overlay over [[llamaindex]]: the IngestionPipeline exists in the base skill but the re-ingest-correctness contract is not surfaced.

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Decision protocol for building, debugging, and operating LangGraph-based agent systems. Activates when a coder agent is asked to design a stateful LLM workflow, add human-in-the-loop, choose a multi-agent pattern (supervisor / swarm / hierarchical), pick a checkpoint backend, or migrate a fragile chain into a durable graph. LangGraph is positioned by its maintainers as a "low-level orchestration framework for building, managing, and deploying long-running, stateful agents" — this skill encodes the *when* and *why*, not the API.

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Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

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Decision protocol for the map-reduce / dynamic fan-out pattern in LM pipelines — "given list L, run f(item) for each item in parallel, then combine". Activates when the coder agent is about to process N items with N LM calls (per-doc summarize, per-query retrieve, per-candidate rank, parallel tool fan-out). Encodes the *when*, *how many at once*, *what to do when one fails*, and *how to reduce* — not the API of any single framework. Cross-framework: LangGraph `Send`, CrewAI parallel tasks / Flow, `asyncio.gather`, `ThreadPoolExecutor`, LlamaIndex batch retrieval.

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Security-first SOP for multi-tenant RAG systems. Activate when a calling agent is building, reviewing, or debugging any retrieval pipeline whose vector store is shared across more than one user, organisation, workspace, customer, or permission scope. Encodes the single non-negotiable rule — **filter at the vector store query, never after retrieval / never after rerank** — together with the per-vendor query-time filter APIs (Pinecone namespaces + `$eq`/`$in`, Weaviate `multiTenancyConfig` + tenant handle, Qdrant `is_tenant` payload index + `Filter.must`, Chroma `where`, pgvector RLS), and the cross-framework adapters (LlamaIndex `MetadataFilters`, LangChain `filter=` dict). Frame the work as preventing CVE-2024-41892 / EchoLeak / Slack-AI-class cross-tenant leakage, not as "adding a filter for relevance".

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Cross-framework enhancement overlay for choosing a multi-agent topology BEFORE writing any agent. A binary-question rubric — is single-agent + tools enough? do agents need to know about each other? does the output need one voice? — maps the answer to single-agent / supervisor / swarm / sequential / hierarchical. Activates when a coder agent is tempted to "split the work into roles" or reaches for a multi-agent framework. Encodes the *selection rubric* that the per-framework skills assume but never surface. Search keywords: when to use multi-agent, single vs multi agent, do I need multiple agents, supervisor vs swarm, multi-agent vs single agent, agent team design.

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Enhancement overlay for multi-agent / tool-using coder agents. Encodes the per-agent tool- scoping discipline that role-based frameworks (CrewAI, LangChain) document only as a passing best-practice: which agent gets which tool, and why blanket-sharing every tool to every agent is a correctness and blast-radius risk. Activates when an agent system has tools AND there is more than one agent (or one agent holding many tools). Treat a tool as a capability grant; scope by least-privilege. ENHANCE overlay — read alongside [[crewai]], [[agentsop-http-tool-wrapping]], [[agentsop-llm-tool-idempotency]]. Search keywords: which tools per agent, least-privilege agent, agent tool access, tool permissions, limit agent tools, scope tools to roles.overlay_type: enhancement

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Tool skill for declaring reducers on LangGraph state keys so parallel writes merge instead of crashing. Activates whenever a coder agent designs a StateGraph with parallel branches, fan-out via Send, multi-agent topologies, or whenever a run raises `InvalidUpdateError: At key '<k>': Can receive only one value per step`. Encodes the rule "every state key is single-writer or has a reducer — nothing in between."

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Enhancement-overlay decision protocol for STREAMING the output of long-running LLM / agent runs from the *backend*, not just wiring a typing animation in the UI. Activates when a coder agent must stream final tokens to a chat client, surface intermediate agent steps (which tool, which node, partial reasoning), emit custom tool-progress events, choose a transport (SSE vs WebSocket), or decide what to do when the client disconnects mid-stream. The langchain / langgraph skills mention stream modes but stop at "you can stream"; this skill encodes *what to stream, over what transport, and how to fail safely*.

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Universal discipline for any LM-driven loop — agent retries, plan-act-observe, multi-agent handoffs, optimiser passes, test-fix cycles. Encodes the one rule every framework documents quietly and every team relearns expensively: the LM in the loop is NEVER a reliable terminator. Termination must be provided by an explicit counter + exit predicate + stagnation signal + escalation path that live OUTSIDE the LM's control. This is a tool- level, framework-agnostic skill. It maps onto LangGraph (recursion_limit + state counter + interrupt), CrewAI (max_iter + max_rpm + human_input), Claude / OpenAI SDKs (max_iterations + tool_use_budget), DSPy (declared evaluation budget), Aider (REPL + explicit retry cap), and AutoGen (max_consecutive_auto_reply). Search keywords: infinite loop, recursion limit, recursion_limit, GraphRecursionError, max iterations, max_iter, agent stuck, agent won't stop, runaway agent, ReAct loop not terminating, agent repeating itself.

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 a CLI interface: args, flags, help, output, errors, exit codes, config.

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。

Trigger for: writing, revising, converting, or descoping a federal Statement of Work, Performance Work Statement, SOW, PWS, or SOO; develop executable requirements; define contract scope; create measurable performance standards; or prepare requirements before an IGCE. Produce a contract-file-ready .docx plus separate chat-only staffing and Section B handoffs. Never place FTEs, SOC codes, staffing estimates, IGCE content, CLINs, or pricing schedules inside the SOW/PWS body. Do NOT use for the IGCE, market research report, acquisition plan, QASP, or clause-selection matrix.

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Quick reference for RESTful API design patterns, HTTP semantics, caching, and rate limiting. Triggers on: rest api, http methods, status codes, api design, endpoint design, api versioning, rate limiting, caching headers.

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。