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Search and inspect the installed Blueish Blender Asset Library without mutating it. Use when a user asks for a local Blender asset, node preset, Blueish asset, Asset Browser entry, or named source, or when the Router has selected a node-centric/reusable Geometry Nodes, material, simulation, rigging, compositor, or NPR route with eligible local catalogs; return compatible, attributable candidates for the Router-approved workflow.

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Bun — fast all-in-one JavaScript runtime, bundler, test runner, and package manager. Use when speeding up Node.js projects, using Bun as a drop-in Node replacement, bundling with Bun, or running tests faster. Covers runtime APIs, package management, bundling, and Bun-specific features like Bun.serve, Bun.file, and Bun.sqlite.

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德州扑克策略 (德州扑克策略 (Texas Hold'em) — solver/GTO 时代的无限注德州扑克竞技打法，牌手/职业视角。覆盖: (a) 理论两极 — GTO 博弈论均衡 (不可剥削的基线策略) vs Exploitative 剥削打法 (针对对手漏洞偏离均衡最大化 EV)，以及二者的实战取舍; (b) solver 时代工作流 — PioSOLVER/GTO Wizard 跑解、范围构建、节点锁定 (node-locking) 做剥削、用 HUD+数据库 (Hold'em Manager/PokerTracker/Hand2Note) 复盘找漏洞 (leak finding); (c) 牌型分层 — 现金局 (cash game) vs 锦标赛 (MTT) 的根本差异，后者叠加 ICM (独立筹码模型) 与跳台/泡沫期博弈; (d) 核心概念栈 — 范围 (range)/权益 (equity)/EV/位置 (position)/翻前开池与 3-bet/4-bet/翻后下注理论 (c-bet、极化 vs 浓缩范围、MDF 最小防守频率、阻断牌 blockers、超池下注 overbet)、SPR、底池赔率; (e) 元层 — 资金管理 (bankroll management)、桌位选择 (table selection)、方差与心态 (mental game/tilt 控制)、抽水 (rake) 与场次选择对长期赢率 (winrate, bb/100) 的影响; (f) 智识演进 — 从 Sklansky 古典手牌价值 → Janda/Tipton 博弈论 → solver 普及 → GTO Wizard 民主化求解 → AI 超人 (Libratus/Pluribus) 对人类策略的反哺。学派分歧: GTO 派 vs 剥削派、现金局 vs 锦标赛、理论自上而下 vs 牌局自下而上、solver 纯学院派 vs 实战感觉派。不含: 线下牌场运营/发牌荷官、博彩合规与赌场管理、扑克之外的牌类 (桥牌/斗地主)、纯概率赌博 (老虎机/轮盘)。) Master OS — automated mastery of 德州扑克策略 (Texas Hold'em) — solver/GTO 时代的无限注德州扑克竞技打法，牌手/职业视角。覆盖: (a) 理论两极 — GTO 博弈论均衡 (不可剥削的基线策略) vs Exploitative 剥削打法 (针对对手漏洞偏离均衡最大化 EV)，以及二者的实战取舍; (b) solver 时代工作流 — PioSOLVER/GTO Wizard 跑解、范围构建、节点锁定 (node-locking) 做剥削、用 HUD+数据库 (Hold'em Manager/PokerTracker/Hand2Note) 复盘找漏洞 (leak finding); (c) 牌型分层 — 现金局 (cash game) vs 锦标赛 (MTT) 的根本差异，后者叠加 ICM (独立筹码模型) 与跳台/泡沫期博弈; (d) 核心概念栈 — 范围 (range)/权益 (equity)/EV/位置 (position)/翻前开池与 3-bet/4-bet/翻后下注理论 (c-bet、极化 vs 浓缩范围、MDF 最小防守频率、阻断牌 blockers、超池下注 overbet)、SPR、底池赔率; (e) 元层 — 资金管理 (bankroll management)、桌位选择 (table selection)、方差与心态 (mental game/tilt 控制)、抽水 (rake) 与场次选择对长期赢率 (winrate, bb/100) 的影响; (f) 智识演进 — 从 Sklansky 古典手牌价值 → Janda/Tipton 博弈论 → solver 普及 → GTO Wizard 民主化求解 → AI 超人 (Libratus/Pluribus) 对人类策略的反哺。学派分歧: GTO 派 vs 剥削派、现金局 vs 锦标赛、理论自上而下 vs 牌局自下而上、solver 纯学院派 vs 实战感觉派。不含: 线下牌场运营/发牌荷官、博彩合规与赌场管理、扑克之外的牌类 (桥牌/斗地主)、纯概率赌博 (老虎机/轮盘)。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 德州扑克策略 (Texas Hold'em) — solver/GTO 时代的无限注德州扑克竞技打法，牌手/职业视角。覆盖: (a) 理论两极 — GTO 博弈论均衡 (不可剥削的基线策略) vs Exploitative 剥削打法 (针对对手漏洞偏离均衡最大化 EV)，以及二者的实战取舍; (b) solver 时代工作流 — PioSOLVER/GTO Wizard 跑解、范围构建、节点锁定 (node-locking) 做剥削、用 HUD+数据库 (Hold'em Manager/PokerTracker/Hand2Note) 复盘找漏洞 (leak finding); (c) 牌型分层 — 现金局 (cash game) vs 锦标赛 (MTT) 的根本差异，后者叠加 ICM (独立筹码模型) 与跳台/泡沫期博弈; (d) 核心概念栈 — 范围 (range)/权益 (equity)/EV/位置 (position)/翻前开池与 3-bet/4-bet/翻后下注理论 (c-bet、极化 vs 浓缩范围、MDF 最小防守频率、阻断牌 blockers、超池下注 overbet)、SPR、底池赔率; (e) 元层 — 资金管理 (bankroll management)、桌位选择 (table selection)、方差与心态 (mental game/tilt 控制)、抽水 (rake) 与场次选择对长期赢率 (winrate, bb/100) 的影响; (f) 智识演进 — 从 Sklansky 古典手牌价值 → Janda/Tipton 博弈论 → solver 普及 → GTO Wizard 民主化求解 → AI 超人 (Libratus/Pluribus) 对人类策略的反哺。学派分歧: GTO 派 vs 剥削派、现金局 vs 锦标赛、理论自上而下 vs 牌局自下而上、solver 纯学院派 vs 实战感觉派。不含: 线下牌场运营/发牌荷官、博彩合规与赌场管理、扑克之外的牌类 (桥牌/斗地主)、纯概率赌博 (老虎机/轮盘)。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「德州扑克」「德扑」「Texas Hold'em」「poker strategy」「扑克策略」

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Design, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with constraints and indexes, or assessing an existing model against graph modeling best practices. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle Spring Data Neo4j entity mapping — use neo4j-spring-data-skill. Does NOT handle GraphQL type definitions — use neo4j-graphql-skill. Does NOT handle data import — use neo4j-import-skill.

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Neo4j Visualization Library (NVL) — framework-agnostic graph rendering for the browser. Covers @neo4j-nvl/base (NVL class, nodes/relationships, Canvas vs WebGL renderer), @neo4j-nvl/interaction-handlers (ZoomInteraction, PanInteraction, DragNodeInteraction, ClickInteraction, HoverInteraction, BoxSelectInteraction, LassoInteraction, KeyboardInteraction), and @neo4j-nvl/react (InteractiveNvlWrapper, BasicNvlWrapper, StaticPictureWrapper). Use when rendering a Neo4j graph in a browser, feeding driver results through nvlResultTransformer, choosing Canvas vs WebGL, wiring node/relationship click/hover/drag handlers, or embedding NVL in React, Vite, or vanilla JS apps. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle driver lifecycle, sessions, or executeQuery setup — use neo4j-driver-javascript-skill. Does NOT handle GraphVisualization/Needle default embed — use @neo4j-ndl/react.

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Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity function and embedding provider dimensions, and batch-update embeddings. Use when tasks involve CREATE VECTOR INDEX, vector.dimensions, cosine/euclidean search, embedding ingestion pipelines, semantic or structural nearest-neighbor lookup, or hybrid search (vector + fulltext, multiple vector sources, or graph-derived scores). Does NOT handle GraphRAG retrieval_query graph traversal — use neo4j-graphrag-skill. Does NOT handle fulltext-only/keyword-only search — use neo4j-cypher-skill. Does NOT compute GDS graph embeddings (FastRP, Node2Vec) — use neo4j-gds-skill.

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Use Nexscope market statistics capability to output a market statistics dashboard by category node, including top listing average rating, average price, BSR, sales, seller count, and new product related metrics, suitable for quickly assessing the market quality and competitive landscape of a category. Trigger when the user mentions category market statistics, market selection dashboard, market fundamentals assessment, node market quality, top product statistics, Nexscope market statistics, category statistics. Even if the user does not explicitly mention "Nexscope", if the need is to view aggregated statistical results by category node, this skill should also be triggered.

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Implements React Flow node-based UIs correctly using @xyflow/react. Use when building flow charts, diagrams, visual editors, or node-based applications with React. Covers nodes, edges, handles, custom components, state management, and viewport control.

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React Flow (@xyflow/react) for workflow visualization with custom nodes and edges. Use when building graph visualizations, creating custom workflow nodes, implementing edge labels, or controlling viewport. Triggers on ReactFlow, @xyflow/react, Handle, NodeProps, EdgeProps, useReactFlow, fitView.

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Generate MaaFramework Pipeline nodes and recognition snippets from screenshots or observed UI state. Use for OCR node generation, ROI sweep, choosing TemplateMatch/OCR/ColorMatch/CustomRecognition, preserving target-file schema style, and designing `next`/`[JumpBack]` links before merging generated nodes into pipeline JSON.

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Node.js & TypeScript development intelligence. 12 domains, 12 stacks, 300+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check Node code. Domains: pattern, package, error, perf, test, async, arch, idiom, anti, tooling, module, typescript. Stacks: web-express, web-fastify, web-hono, web-nestjs, web-nextjs, web-remix, orm-prisma, orm-drizzle, auth, database, realtime, graphql. Integrations: arch_system generator.

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Conversational profile builder. Produces validated JSON config files for any rootnode Skill that consumes profile-shaped configuration (handoff trigger check, mode router, critic gate, future Skills). Reads a target JSON Schema (draft-07), conducts a progressive-depth interview using plain-language questions, validates answers, and writes the resulting JSON to a destination path. Schema-agnostic — works with any profile schema conforming to draft-07. Use when a user needs to create, clone, or revise a profile and you do not want them hand-editing JSON. Trigger on: "build me a profile," "create a profile," "I need a profile for X," "set up my profile," "edit my profile," "clone the X profile," "walk me through making a profile." Do NOT use when the user already has a working profile and only wants to invoke the consuming Skill, when the schema cannot be located, or for non-profile JSON config work. Always validate before writing.

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Evaluates and scores Claude prompts and system prompts using a six-dimension Scorecard with anchored 1-5 rubrics. Trigger on: "review my prompt," "score this prompt," "rate my prompt," "evaluate this prompt," "improve my prompt," "what's wrong with my prompt." Also trigger on: "why isn't my prompt working," "my prompt produces bad output," "help me fix this prompt," "is this prompt any good." Scorecard covers Objective Clarity, Context Specificity, Reasoning Fit, Output Precision, Behavioral Calibration, and Architectural Efficiency, plus a five-question Output Evaluation Rubric for assessing actual output. Also use when the user pastes a system prompt and asks for feedback. Activate whenever structured quality assessment of a single prompt is the primary need. Do NOT use for project-level audits involving Custom Instructions architecture, knowledge file organization, or multi-file Project structure (use rootnode-project-audit if available).

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Specialized agentic and context engineering prompt methodology for Claude. Use when designing AI agents — system prompts, tool definitions, context window architectures, failure mode planning, multi-agent coordination, agent evaluation. Trigger on: "design an agent," "build a system prompt for an agent," "architect an agent's context," "design tool definitions," "agent system prompt," "context window architecture," "tool interface design," "evaluate agent behavior," "multi-agent coordination," "context engineering for agents," "ACI design." Provides 12 tested approaches for agent architecture (designing agents, not using them). Do NOT use for evaluating or scoring individual non-agent prompts (use rootnode-prompt-validation if available). Do NOT use for general software architecture that hosts agents (use standard technical reasoning for that).

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Specialized software engineering prompt methodology for Claude. Use when building prompts for system design, code review, incident response, security analysis, API design, architecture decisions, RFCs, ADRs, runbooks, or technical leadership. Trigger on: "build a prompt for code review," "system design prompt," "SRE prompt," "security review prompt," "incident response prompt," "API design prompt," "architecture decision prompt," "RFC prompt," "runbook prompt." Provides 11 tested approaches across identity, reasoning, and output for engineering analysis. Do NOT use for general coding help, writing code, or debugging — this builds prompts that shape engineering analysis, not code itself. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available).

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Guides selection of identity, reasoning, and output approaches for Claude prompts based on task characteristics. Trigger on: "help me choose an approach," "which approach fits this task," "recommend a prompt pattern," "compare reasoning methods," "map this task to the right approach," "what combination of approaches," "which identity fits," "which reasoning method." Also trigger on symptom-phrased: "my prompt feels generic," "I don't know which approach to use," "my output lacks domain depth." Covers decision-tree logic across 8 identity approaches, 18 reasoning variants, and 10 output formats. After selection, use the relevant catalog skill if available to retrieve full templates. Activate whenever approach-selection across multiple categories is the primary decision. Do NOT use when the user already names a specific approach to retrieve (use the relevant catalog skill directly if available) or for evaluating existing prompts (use rootnode-prompt-validation if available).

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Independent re-derivation gate for proposed changes during autonomous execution. Evaluates a change (code diff, config edit, engine evolution, schema change) against the work's authority matrix and a 4-check protocol: invariant compliance, scope authorization, detection narrowness, and regression risk. Returns structured JSON with pass/fail per check, an overall verdict (APPROVE / REQUEST_CHANGES / REJECT), and blockers. Profile-driven thresholds (strict for unattended runs, lenient for desk supervision). Use when an autonomous agent proposes an engine change, when reviewing a Claude Code-authored modification before merge, or when an unattended profile requires independent re-derivation. Trigger on: "review/approve this proposed change," "critic-gate this," "is this change safe," "is this safe to merge," "should I let this land," "run the critic." Do NOT use for handoff-readiness checks (use rootnode-handoff-trigger-check), general code review, or design-time correctness. Authority matrix required.

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Produces structured markdown session continuation documents for multi-session workflows. Captures active work streams, decisions with rationale, knowledge-file deltas learned during the session, the exact files to load next conversation, ingested content, artifacts, and open items into an ingestion-optimized handoff. Carries pending KF updates and unresolved items forward across handoff chains so context never drops. Generates a closeout checklist and echoes the starter prompt to chat. Use when approaching context limits, wrapping up a session, or managing work across conversations. Trigger on: "create a handoff," "session closeout," "wrap up this session," "build session handoff," "BSH," "continue next session," "we're running out of context," "context is getting long." Do NOT use for Memory optimization, context budget analysis, project audits, deciding whether work is ready for autonomous/Claude Code execution (use rootnode-handoff-trigger-check), or summaries with no continuation intent.

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Diagnoses and fixes Claude behavioral issues in system prompts and Projects using tested countermeasure templates. Use when "Claude is too verbose," "keeps hedging," "agrees with everything," "claims it did something it didn't," "won't use a tool," "fix Claude's output," "tune Claude's behavior," "Claude ignores my preferences," "adds unsolicited disclaimers," "uses too many lists." Covers ten tendencies: agreeableness (output-content + persistent-preference), hedging, verbosity, list overuse, fabricated precision, over-exploration, tool miscalibration (over- and under-triggering), LaTeX defaulting, editorial drift, self-referential fabrication. Also use when auditing a system prompt for behavioral calibration or recalibrating a pre-4.7 prompt, and when users describe recurring output problems without naming a tendency. Do NOT use for scoring a prompt's overall quality — use rootnode-prompt-validation if available.

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Redact rendered visual artifacts — Mermaid diagrams, SVGs, and HTML dashboards — before public disclosure, using an ordered longest-first mapping table (specific label, then namespace label, then catch-all placeholder) that swaps sensitive identifiers for descriptive stand-ins while preserving structure, node ids, and teaching value. Covers whole-word identifier replacement, node-label-vs-node-id discrimination, re-rendering the derived image from the redacted source, and verifying the result through the redaction gate. Use when a diagram or dashboard built from internal findings must ship to a public mirror without leaking the names behind it.

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Decide what to PERSIST in your data structures (graph nodes, DB tables, cache layouts) based on the QUERIES you must answer, not the DEFINITIONS that justify the data. Definitions tell you logical identity; storage strategy is independent. Materializing every definitional component leads to O(N×M) blow-ups that masquerade as memory leaks. Use when: (1) designing a new graph-node type, edge type, DB table, or cache layout; (2) about to name a `subgraph` / `comprises` / `members` / `contains` collection on a parent entity; (3) memory growth proportional to (entities × per-entity-elements); (4) "memory leak" debugging that survives multiple targeted patches — the leak may be storage shape, not retention; (5) pre-implementation sketch includes "for each X, store all reached nodes"; (6) research/spec doc says "X = ⟨A, B, C⟩" and you're tempted to make it three storage edges per X.

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Fix RFDB V2 graph data silently not persisting to disk after analysis. Use when: (1) rfdb-server reports "0 nodes, 0 edges" on restart despite successful analysis, (2) segment directories exist but are empty (no .bin files), (3) manifest_index.json shows total_nodes: 0 despite analysis logging 70k+ nodes, (4) Docker builds produce empty graph databases, (5) --clear flag used before analyze command. Root cause: GraphEngineV2::clear() replaces the store with MultiShardStore::ephemeral() which has path: None, causing all flush operations to write to in-memory buffers only.

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Write idiomatic application code with the ClickHouse Node.js client (`@clickhouse/client`). Use this skill whenever a user is *building* against the Node.js client - configuring the client, pinging, inserting rows in JSON or raw formats, selecting and parsing results, binding query parameters, managing sessions and temporary tables, working with data types or customizing JSON parsing. Do NOT use for browser/Web client code.

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"Calculate network centrality metrics to identify important nodes in graphs. Use this skill when the user needs to find key influencers, critical infrastructure nodes, or central actors in a network — even if they say 'who is most important in this network', 'key nodes', or 'network influence measurement'.".

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