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Found 554 Skills
Render an ad-hoc interactive map inline in the chat from a deck.gl declarative spec via the CARTO MCP server's view_map tool. Use whenever the user asks to map, visualize, or show the geographic distribution of points, polygons, hexagons, quadbins, clusters, density (heatmaps), or raster — and the map is exploratory or throwaway, not meant to be saved as a permanent CARTO Builder map. Triggers on "show me X on a map", "visualize Y", "make a heatmap of Z", "render the points/clusters/raster of W". Distinct from carto-create-builder-maps (CLI authoring of permanent maps), carto-preview-builder-map (loading an existing saved Builder map), and carto-develop-app (writing a from-scratch deck.gl app in TypeScript / JavaScript).
Use when the user asks for repeated rollouts, marked decision processes, high-dimensional search, stochastic optimization, local-optima exploration, ensemble comparison, or recursive reasoning with a visible evidence trail.
Score a single draft against the rubric. **Output only to the console, no file writing, no prediction**. Trigger phrases: "Score this [path]"/"score this [path]"/"Score this draft"/"Let's score first". It's a lightweight exploratory action before cheat-predict.
Audits AI-implemented work for honest completion. Runs independent-evaluator checks against task artifacts, transcripts, tests, CI evidence, requirement-to-test mapping, status front matter, and quality gates; flags skipped tests, weakened assertions, mock-only confidence, snapshot drift, happy-path-only coverage, flaky retries, and status/evidence mismatches. Use when validating completed Compozy tasks, AI-authored PRs, or codex-loop iterations. Do not use for real-user QA, persona/journey testing, exploratory charters, or product usability sessions; use qa-execution for those.
Inspeccionar un proyecto existente para descubrir decisiones arquitectónicas implícitas y proponer ADRs candidatos. Usar cuando el usuario quiera auditar un repositorio en busca de decisiones no documentadas, pida "descubrir ADRs", "qué decisiones arquitectónicas tiene este proyecto", "busca ADRs en el repo", "analiza la arquitectura del proyecto" o cualquier variante que implique explorar el código/estructura para inferir decisiones relevantes que merezcan un ADR. Activar también cuando el usuario llegue a un proyecto nuevo y quiera entender qué decisiones ya se tomaron, aunque no mencione explícitamente "ADR".
Interact with EVM-compatible blockchains using Foundry's cast tool for querying balances, calling contracts, sending transactions, and blockchain exploration. Use when needing to interact with Ethereum Virtual Machine networks via command-line, including reading contract state, sending funds, executing contract functions, or inspecting blockchain data.
Exploratory discussion pattern for unsolved problems. Replicate the thinking of Staff+ engineers: "When there's no clear answer, expose blind spots by confronting diverse perspectives." True multi-agent discussions where experts directly engage with each other through team-based + messaging architecture.
Uncertainty-aware non-linear reasoning system with recursive subagent orchestration. Triggers for complex reasoning, research, multi-domain synthesis, or when explicit commands `/nlr`, `/reason`, `/think-deep` are used. Integrates think skill (reasoning), agent-core skill (acting), and MCP tools (infranodus, exa, scholar-gateway) in recursive think→act→observe loops. Uses coding sandbox for execution validation and maintains deliberate noisiness via NoisyGraph scaffold. Supports `/compact` mode for abbreviated outputs and `/semantic` mode for rich exploration.
Learn about Moralis and Web3 development. Invoked without a question, gives a friendly platform walkthrough — what's available, what data you can fetch, and how everything fits together. Invoked with a question, answers it directly. Use for "what is Moralis", "can Moralis do X", "what chains are supported", "how do I get started", "which API should I use", pricing, feature comparisons, or any exploratory questions. Routes to the correct technical skill (@moralis-data-api or @moralis-streams-api) after answering.
Intelligent Retrieval Assistant for Cangjie Language Documentation. Supports 4 search modes (Direct Search, PageIndex Intelligent Retrieval, Hybrid Mode, Exploratory Learning). It is used when users need to: (1) Query Cangjie syntax (variable declaration, function definition, generics, etc.), (2) Look up standard library APIs (String, Array, HashMap, etc.), (3) Learn about Cangjie features or get started with the language, (4) Conduct any documentation queries related to Cangjie/cangjie/cj. It uses four MCP tools: cangjie_docs_overview, cangjie_list_docs, cangjie_search, and cangjie_get_doc for intelligent retrieval.
World-class character and art style consistency for AI-generated images and videos - ensures visual coherence across series, maintains character identity, and provides rigorous QA before deliveryUse when "character consistency, art style, same character, consistent character, visual continuity, series, turnaround sheet, character sheet, reference image, character bible, style guide, anime character, consistent look, face consistency, outfit consistency, lora training, ip-adapter, flux kontext, visual qa, art quality, generation review, style drift, character drift, character-consistency, art-style, visual-qa, ai-art, image-generation, video-generation, anime, illustration, lora, ip-adapter, flux, midjourney, stable-diffusion" mentioned.
Unified brand discovery that outputs brand.yaml. Merges brand-builder (voice/audience) + design-exploration (visual direction) into one flow. Run once per project to establish brand identity.