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Found 1,434 Skills
KWCode (天工开物) — a CLI coding agent optimized for local open-source models (8B-30B), featuring deterministic expert pipelines, BM25+AST code location, runtime debugging, and a self-improving flywheel — all running fully offline.
Covers the Neo4j Go Driver v6 — driver lifecycle, ExecuteQuery, managed and explicit transactions, session config, error handling, data type mapping, and connection tuning. Use when writing Go code that connects to Neo4j, setting up NewDriver or ExecuteQuery, debugging sessions/transactions/result handling, or working with neo4j-go-driver v5→v6 migration. Triggers on NewDriver, ExecuteQuery, SessionConfig, ManagedTransaction, neo4j-go-driver. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver version migration steps — use neo4j-migration-skill.
This skill should be used when implementing, consuming, or debugging an Open Responses-compliant API — the open standard for multi-provider LLM interoperability. Covers protocol, items, state machines, streaming events, tools, the agentic loop pattern, and extensions. Triggers on: Open Responses, open-responses, /v1/responses endpoint, multi-provider LLM API, Open Responses compliance.
Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality. Use when building a tracking plan from scratch, auditing existing analytics for gaps or errors, debugging missing events, or setting up GTM. Trigger keywords: GA4 setup, Google Tag Manager, GTM, event tracking, analytics implementation, conversion tracking, tracking plan, event taxonomy, custom dimensions, UTM tracking, analytics audit, missing events, tracking broken. NOT for analyzing marketing campaign data — use campaign-analytics for that. NOT for BI dashboards — use product-analytics for in-product event analysis.
Use this skill when building, debugging, or answering questions about Liveblocks. Liveblocks gives you the building blocks and infrastructure to enable people and AI to work together inside your app, powering realtime collaboration. Liveblocks features include collaboration, rooms, organizations, workspaces, comments, composer, threads, notifications, multiplayer, conflict resolution, realtime presence, avatar stacks, AI collaborators, AI agents, text editors, Tiptap, BlockNote, Lexical, React Flow, Chat SDK. Common components include AiChat, Thread, InboxNotification, Composer, Toolbar (for Lexical Tiptap), FloatingToolbar, FloatingComposer, FloatingThreads, AnchoredThreads. Common hooks include useThreads, useStorage, useMutation, useOthers, useInboxNotifications, useAiChats. Common issues are related to authentication (ID tokens vs access tokens), permissions, room limits, connection errors, user info.
Debug package usage guide. Use when adding debug logging, understanding log namespaces, or implementing debugging features. Triggers on debug logging requests or logging implementation.
Implement structured logging with JSON formats, log levels (DEBUG, INFO, WARN, ERROR), contextual logging, PII handling, and centralized logging. Use for logging, observability, log levels, structured logs, or debugging.
Implement distributed tracing with Jaeger and Zipkin for tracking requests across microservices. Use when debugging distributed systems, tracking request flows, or analyzing service performance.
Recognize, diagnose, and mitigate patterns of context degradation in agent systems. Use when context grows large, agent performance degrades unexpectedly, or debugging agent failures.
Complete Liquid templating language reference including syntax, filters, objects, control flow, loops, and conditionals for Shopify themes. Use when working with .liquid files, creating theme templates, implementing dynamic content, debugging Liquid code, working with sections and snippets, or rendering product/collection/cart data in Shopify stores.
Use when investigating why something happened and need to distinguish correlation from causation, identify root causes vs symptoms, test competing hypotheses, control for confounding variables, or design experiments to validate causal claims. Invoke when debugging systems, analyzing failures, researching health outcomes, evaluating policy impacts, or when user mentions root cause, causal chain, confounding, spurious correlation, or asks "why did this really happen?"
Expert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific Dagster concept.