Use when the agent is holding an object and needs to place it into a target receptacle in ALFWorld. This skill checks receptacle suitability, opens closed containers if needed, and executes the `put` command to store the object. It handles both open surfaces (countertops, beds) and closed containers (drawers, cabinets).
Picks up a target object from its current receptacle and moves it to a specified destination receptacle. Use when you have located an object and need to relocate it to complete a task (e.g., moving a laptop to a desk). Takes the object identifier, source receptacle, and destination receptacle as inputs and outputs the action sequence to take, transport, and place the object.
Systematically searches a sequence of likely locations for a target object based on common sense. Use when you need to find a specific object and know which receptacles to check but not which one contains it. Takes a list of candidate receptacles, orchestrates navigation and inspection, and outputs when the target is found or all locations are exhausted.
Re-examines previously visited locations to confirm the absence of a target object or to check for overlooked items. Use when an initial search fails to find enough objects or when double-checking is required before concluding task failure. Systematically revisits receptacles, re-opens closed containers, and re-inspects contents to ensure no viable location was missed.
Executes a 'focus on OBJ' action on a specific object based on the outcome of a prior conditional evaluation. Use when you have a measurement result and task instructions specify focusing on different objects (e.g., colored boxes) depending on whether the result meets a threshold.
This skill makes a determination about a material's property (e.g., conductivity) based on environmental cues or domain knowledge when direct testing fails. Trigger it when experimental actions are invalid or unavailable, requiring a logical inference. It uses observed object properties and common-sense reasoning to classify the material and decide its final disposition.
Moves a tested or examined object into a designated container (e.g., a specific colored box) based on a determined property. Use when you have completed a test or inspection and need to fulfill a classification or sorting subtask. It takes the object and target container as inputs and performs the move action.
This skill selects and focuses on a specific object to signal task intent or prepare it for manipulation. Use when you have identified a target object that meets task criteria (e.g., a living thing) and need to formally select it before performing actions like moving or using the object in ScienceWorld tasks. The skill uses the 'focus on OBJ' action, taking the object name as input.
Searches for a specific target object across multiple rooms by systematically teleporting to likely locations and examining each room. Use this skill when you need to find an object whose exact location is unknown. Iterates through candidate rooms using teleport and look around, checks for the object, and continues until found, returning the object's location.
Use when the agent needs to choose a specific object from multiple candidates in the environment based on task criteria such as object type (non-living thing, electrical component, container), properties, or category. This skill surveys visible objects with "look around", evaluates each against the task requirements, selects the most suitable candidate, and signals intent with "focus on [OBJECT]".
This skill initiates the cooling of a substance by moving it into a cooling appliance like a freezer. Use when a task requires lowering the temperature of a specific material to observe phase changes. The skill takes the substance (often in a container) and the target appliance as inputs, using the 'move OBJ to OBJ' action. It outputs confirmation of the new location.
This skill determines the most likely location for a target object based on domain knowledge and environmental clues. Use when the agent needs to find a specific item (like an animal) but it is not in the current room. It analyzes the environment description and suggests a room to teleport to for further investigation.
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.
CI/CD matrix strategies for multi-platform and multi-language builds
Add a new AI model to the Pipelex inference system. Guides through all required steps: backend TOML configuration (OpenAI, Azure, Anthropic, Google, etc.), kit sync, test profile collections, and fixture regeneration. Use when the user says "add a model", "add GPT-X", "add Claude X", "new model", "register a model", "add Gemini X", "support model X", "add model to backend", or any variation of introducing a new AI model to the inference configuration. Also use when the user mentions a model name that doesn't exist in the backend configs yet and wants to add it.
Test an AI model on a specific backend using the Pipelex inference test infrastructure. Handles test profile creation, fixture regeneration, and running the right test class for the model type (LLM, image gen, extract, search). Use when the user says "test model X", "test gpt-5.4 on openai", "test model on gateway", "run inference test for model", "try model X on backend Y", "verify model X works", or any variation of running inference tests against a specific model on a specific backend. Also use when the user mentions testing a model after adding it, or wants to verify a model works end-to-end with real API calls.
Standard operating procedure for academic figure generation. Four rendering engines: Python (data viz), Mermaid (flowcharts), AI Image via NanoBanana/OpenRouter (complex diagrams), SVG (vector). Includes engine selection decision tree, ReAct self-correction, NanoBanana configuration, environment detection, and academic style rules.
Help users build effective AI applications. Use when someone is building with LLMs, writing prompts, designing AI features, implementing RAG, creating agents, running evals, or trying to improve AI output quality.
Expert Haskell engineer specializing in advanced type systems, pure functional design, and high-reliability software. Use PROACTIVELY for type-level programming, concurrency, and architecture guidance.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use when building AI features, improving agent performance, or crafting system prompts.
