extract-struct
Extract structured metadata (title, authors, year) from paper text using LLM. Use to convert online search results metadata to JSON
Browse curated skills with source links, package snapshots, README assets and install signals in one calm, searchable catalog.
Extract structured metadata (title, authors, year) from paper text using LLM. Use to convert online search results metadata to JSON
Check if text contains a question
Queries the persistent spatial map representing everything the agent currently knows about the environment (from past and present observations). Useful for reasoning, explanation, and planning.
Labels a coordinate with a waypoint name for reasoning about spatial relationships. Use to mark important locations for navigation and planning
Create 600x600 Instagram-style card news series automatically with optional background images. User provides topic, colors, and optional images - Claude generates content and creates multiple cards wi…
Generates article cover images with 5 dimensions (type, palette, rendering, text, mood) combining 9 color palettes and 6 rendering styles. Supports cinematic (2.35:1), widescreen (16:9), and square (1…
Cross-model performance optimization and scaling configurations for autonomous agents
Generate dynamic-object binary masks after global motion compensation, output CSR sparse format.
Semantic filter: evaluate each Collection item against a `predicate` string and return matching items (target, predicate, [mode])
Visible non-air blocks within radius R (default/max 7). Filtered by forward cone (yaw ±60°, pitch -60..+90) and line-of-sight. Does NOT report items.
Generates zoomable HTML visualization of the spatial map showing walkability, hazards, resources, and observation confidence.
Bring the agent into stable adjacency with a solid vertical surface using nav conventions and nav motion tools.
Create 600x600 Instagram-style card news series automatically with optional background images. User provides topic, colors, and optional images - Claude generates content and creates multiple cards wi…
Advanced contextual pattern recognition with project fingerprinting, semantic similarity analysis, and cross-domain pattern matching for enhanced learning capabilities
Enables autonomous pattern recognition, storage, and retrieval at project level with self-learning capabilities for continuous improvement
Estimate camera motion with optical flow + affine/homography, allow multi-label per frame.
Generate new text or code content from scratch using natural language prompt via LLM. Creates content from the LLM's own knowledge — no source documents.
Visible item entities within radius R (default/max 7), filtered by forward cone and line-of-sight.
Enable or disable automatic training data capture (partial observation grid + optional ground truth).
Get the current observation from the OSWorld environment. Returns screenshot (base64 PNG) and accessibility tree (JSON).
Remove Gemini logos, watermarks, or AI-generated image markers using OpenCV inpainting. Use this skill when the user asks to remove Gemini logo, AI watermark, or any logo/watermark from images.
Universal model detection and capability assessment for optimal cross-model compatibility
Use this skill when simulating vehicle motion, calculating safe following distances, time-to-collision, speed/position updates, or implementing vehicle state machines for cruise control modes.
Standardize video sampling and frame indexing so interval instructions and mask frames stay aligned with a valid key/index scheme.