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Found 709 Skills
Contract testing for API consumers and providers. Pact framework, Spring Cloud Contract, consumer-driven contracts, provider verification, and contract broker setup. USE WHEN: user mentions "contract testing", "Pact", "consumer-driven contract", "Spring Cloud Contract", "API contract", "provider verification" DO NOT USE FOR: OpenAPI validation - use `openapi-contract`; integration testing - use integration test skills
Install official tech brand logos from the Elements registry. Use when user needs logos for tech companies (Clerk, Vercel, GitHub, etc.), AI providers (OpenAI, Anthropic, Claude), social platforms, or any brand assets. Triggers on "logo", "brand", "icon for [company]", "add [company] logo", placeholder logo detection, or when building landing pages, auth UIs, or integrations showcases.
API reference: WidgetKit. Query for widget timelines, entries, providers, home/lock screen widgets.
Points to the BlockchainSpider open-source Python/Scrapy toolkit for collecting on-chain data—transfer subgraphs around an address or tx, EVM and Solana block/transaction ingestion, receipts/logs, and optional label plugins. Use when the user wants to build datasets, offline traces, or research pipelines alongside blockchain-analytics-operations and solana-tracing-specialist—not as a substitute for RPC provider ToS, rate limits, or legal review of sensitive crawls.
Agnostic tunnel management supporting Cloudflare, Tailscale, and other providers. Inspired by ZeroClaw's agnostic tunnel architecture.
Sets up a 3D CAD model viewer in a Dune app using Cognite Reveal via @cognite/dune-industrial-components/reveal. Use this skill whenever the user mentions 3D viewer, 3D visualization, reveal, CAD model, RevealProvider, RevealCanvas, Reveal3DResources, FDM 3D mapping, asset 3D model, loading a 3D model, or wants to display any Cognite 3D content in a Dune application — even if they don't explicitly say 'Reveal' or '3D viewer'. Do NOT manually wire up RevealProvider, RevealCanvas, or model-loading hooks without consulting this skill first.
Automates declarative resource creation and provisioning for data pipelines, supporting BigQuery, Dataform, Dataproc, BigQuery Data Transfer Service (DTS), and other resources. It manages environment-specific configurations (dev, staging, prod) through a deployment.yaml file. Use when: - Modifying or creating deployment.yaml for deployment settings. - Resolving environment-specific variables (e.g., Project IDs, Regions) for deployment. - Provisioning supported infrastructure like BigQuery datasets/tables, Dataform resources, or DTS resources via deployment.yaml. Do not use when: - Resources already exist. - Managing resources not supported by `gcloud beta orchestration-pipelines resource-types list`. - Managing general cloud infrastructure (VMs, networks, Kubernetes, IAM policies), which are better suited for Terraform. - Infrastructure spans multiple cloud providers (AWS, Azure, etc.). - Already uses Terraform for the target resources.
Run fact-grounded image generation batches for short-form video production, especially persona images, first-frame candidates, and light consistency edits. Use this when persona and concept inputs already exist and you need local image assets, prompt records, and reusable model-call metadata. This skill should stay anchored to benchmark-backed persona locks and should save both raw provider responses and normalized local asset manifests.
Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.vector.encode(). Use when writing pure-Cypher GraphRAG, embedding nodes in-graph, generating structured maps from prompts, or calling LLMs inside Cypher queries. Does NOT handle neo4j-graphrag Python library pipelines — use neo4j-graphrag-skill. Does NOT handle vector index creation/search — use neo4j-vector-index-skill.
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, or semantic nearest-neighbor lookup. Does NOT handle GraphRAG retrieval_query graph traversal — use neo4j-graphrag-skill. Does NOT handle fulltext/keyword indexes (FULLTEXT INDEX, db.index.fulltext) — use neo4j-cypher-skill. Does NOT handle GDS graph embeddings (FastRP, Node2Vec) — use neo4j-gds-skill.
Front door for any GTM task on Cargo — sourcing, waterfall enrichment, email/phone/LinkedIn lookup, email verification, scoring, qualification, sequencing, CRM sync, and signal monitoring (job changes, funding, tech-stack/hiring intent). Use when the user states a real-world goal involving prospects, leads, accounts, contacts, ICP lists, or campaign activation. Routes to phase guides (Level 2), recipes (Level 2.5), and per-provider playbooks (Level 3) before any action call.
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AgentControl implementation in five stages: audit the code, wrap the call, move the tools, add tracking, attach evaluators. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini, Strands) to a managed config, or stage a full hardcoded-to-LaunchDarkly migration.