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Fix Gorse recommendation engine failing to connect to ClickHouse Cloud with EOF errors. Use when: (1) Gorse pods crash with "failed to connect data database" and "EOF" error, (2) Using ClickHouse Cloud (*.clickhouse.cloud) as Gorse data store, (3) TLS connection works (verified with openssl) but Gorse still fails, (4) Tried secure=true, different ports (8443, 9440), timeout params without success. The Gorse ClickHouse driver is incompatible with ClickHouse Cloud's native protocol.

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Fix Dart/Flutter services where calling start() after stop() is a silent no-op because stop() sets a _disposed (or similar) flag that start()'s guard short-circuits on. Use when: (1) A repository/service/controller has startListening/stopListening, subscribe/unsubscribe, open/close, or similar lifecycle methods, (2) Re-opening the service after closing it appears to do nothing — no subscription, no events, no error, (3) Unit tests that only exercise a single mount/open/start cycle pass while the real app breaks on the second visit to a screen, (4) A boolean flag is used both for "in the middle of tearing down this instance forever" AND for "currently stopped, can be re-started". Common in Riverpod/Bloc-driven screens that wire startListening() in initState and stopListening() in dispose — the second time the user visits the screen, nothing happens.

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Build efficient Blender repetition, Array-driven stairs/modules/openings, instancing, scattering, Bezier/NURBS curve-profile geometry, Geometry Nodes fields, repeat zones, simulation zones, particle-like systems, and collective motion. Use for stairs, railings, trim, pipes, cables, repeated windows/panels, vegetation, debris, crowds, rain, snow, modular structures, or any request involving dependent elements or reusable parameters.

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Select, build, and validate Blender deformation and motion-control systems including Simple Deform, Curve, Lattice, Mesh Deform, Shrinkwrap, Surface Deform, Armature, shape keys, hooks, constraints, and drivers. Use when an object must bend, twist, taper, follow a path, conform to a surface, deform smoothly, articulate, or move through controlled mechanical relationships.

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Build and refine production Blender geometry through semantic vertex/edge/face editing, BMesh, subdivision control cages, Boolean construction, Displace relief, remesh, sculpt preparation, retopology, and deliberate surface connections. Use after blockout for hard-surface, product, organic, architectural openings, terrain/relief, or reference-driven assets when shape quality, topology, continuity, or realistic part integration matters.

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Turn one line ("a turbofan jet engine", "a mechanical watch", "an LVAD heart pump") into a photoreal, physics based, interactive 3D simulation of that machine in a single HTML file, plus a 20 to 30 second film to post it. Real proportions in metres, real materials (PBR metals, paints, polymers, glass), studio lighting with soft shadows, moving parts driven by a small solver that computes real numbers (thrust, torque, rpm, flow, pressure, efficiency), and modes to explode it, cut it open with clean section caps, x-ray it, slow it down, label it and break it. Use this whenever someone asks to build, simulate, visualise, explain or "show how X works" for any machine, engine, mechanism, device, instrument or piece of hardware, wants an interactive 3D model or a cutaway, or wants a video of a machine for a post, even if they never say "simulation" or "three.js".

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Generate implementation code that passes existing unit tests. Use when the user provides test files (Python pytest/unittest, Java JUnit/TestNG) and asks Claude to implement the code to make those tests pass. Supports full TDD workflow - analyzing tests, generating implementation, running tests, debugging failures, and iterating until all tests pass.

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。

Generate implementation code and tests from written specifications. Use when the user provides specifications (natural language descriptions, formal specs, requirements documents, API specs) and asks Claude to implement the described functionality. Supports data structures, algorithms, classes, functions, and includes automatic test generation to validate implementation against specification.

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Generate targeted test inputs to reach specific code paths and hard-to-reach behaviors in Python code. Use when: (1) Targeting uncovered branches or specific execution paths, (2) Need coverage-guided test generation, (3) Want to leverage LLM understanding of code semantics for meaningful test inputs, (4) Testing boundary conditions and edge cases systematically, (5) Combining symbolic reasoning with fuzzing. Provides path analysis, constraint solving, coverage-guided strategies, and LLM-driven semantic generation for comprehensive test input creation.

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Generate integration tests for multiple interacting components in Python. Use when testing interactions between: (1) Multiple services or APIs (REST/GraphQL endpoints, microservices), (2) Database operations with repositories/ORMs (SQLAlchemy, Django ORM), (3) External services (payment gateways, email services, third-party APIs), (4) Message queues and event-driven systems, (5) Full stack workflows (API + database + business logic). Provides test structure templates, fixtures, test data builders, and patterns for pytest-based integration testing.

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 implementing any feature or bugfix, before writing implementation 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。

TRIGGER THIS when researching market salaries, building compensation strategies, creating salary bands, analyzing pay equity, conducting salary reviews, preparing compensation proposals, or ensuring competitive compensation. Analyzes market data, builds equity-focused salary structures, identifies pay gaps, and creates data-driven compensation recommendations.

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This skill orchestrates autonomous discovery of brand materials across enterprise platforms (Notion, Confluence, Google Drive, Box, SharePoint, Figma, Gong, Granola, Slack). It should be used when the user asks to "discover brand materials", "find brand documents", "search for brand guidelines", "audit brand content", "what brand materials do we have", "find our style guide", "where are our brand docs", "do we have a style guide", "discover brand voice", "brand content audit", or "find brand assets".

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。

ACTIVATE when building executive dashboards, tracking business metrics, creating executive scorecards, monitoring organizational KPIs, setting up health metrics, building dashboards for real-time monitoring, or establishing systems to track and measure business performance. Critical for any executive who needs to monitor company health, make data-driven decisions, or track progress toward strategic goals.

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TRIGGER THIS when designing employee engagement surveys, analyzing employee feedback, creating action plans based on survey results, tracking engagement trends, planning pulse surveys, or improving employee experience. Designs comprehensive surveys that measure engagement, belonging, and satisfaction with actionable insights and accountability-driven improvement plans.

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Turn vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper figure. Also trigger on requests such as 明确科研 idea、启发式梳理 idea、互动追问科研问题、梳理论文创新点、定义研究任务、找对标论文、把研究方向变成论文方案、画方法框架图. Do not use as the primary workflow for running experiments, exhaustive systematic reviews, drafting full manuscript sections, or polishing finished prose.

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。

Execute plans task-by-task with two-stage review.

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。

TDD: enforce RED-GREEN-REFACTOR, tests before 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。

Transforms a rough idea into a detailed design document with implementation plan. Follows Prompt-Driven Development — iterative requirements clarification, research, design, and planning.

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。

Manage persistent coding sessions across Claude Code, Codex, Antigravity (agy), Cursor, and OpenCode engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, autoloop autonomous workspace iteration, ultraapp building deployable web apps from a structured Q&A interview, switching models/tools at runtime, exposing the orchestrator's 69 tools as an MCP server to Hermes Agent / Claude Desktop / Cursor / Cline / Continue / Zed / Windsurf / Goose, or running as an Agent Client Protocol (ACP) agent that Zed / JetBrains / Neovim / Emacs / VS Code / dsh can drive directly. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "autoloop", "ultraapp", "Forge tab", "build a web app", "one-click app", "AppSpec", "autonomous iteration", "iterate until goal", "deep paper review", "auto research", "switch model", "multi-agent", "coding session", "session inbox", "cursor agent", "opencode", "mcp server", "clawo-mcp", "hermes mcp", "model context protocol", "ultracode", "dynamic workflow", "fanout", "fan-out", "best-of-N", "steer turn", "interrupt turn", "fork thread", "rollback turns", "acp", "agent client protocol", "clawo acp", "zed agent", "jetbrains agent", "external agent", "dsh subagent", "deepseek harness".

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。

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response 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。

Use when user asks to create/edit/query Online reports, SQL reports, data reports, or says "创建报表", "生成报表", "新建报表", "查询报表", "online报表", "SQL报表", "数据报表", "统计报表", "create report", "generate report", "data report". Also triggers when user describes report requirements like "做一个销售统计报表", mentions JeecgBoot cgreport/online report, or says "查看现有报表" / "列出所有报表". This skill handles Online 报表 (SQL-driven data display/reports), not Online forms (cgform) or designer forms (desform).

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。

Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.

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Determine whether an app is ready to submit, then drive the current App Store release flow with asc, including validation, staging, review submission, first-time availability, subscriptions, IAP, Game Center, and App Privacy checks.

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。