aieos
AIEOS (AI Entity Object Specification) is a standardization framework designed to solve the "identity crisis" currently facing AI agents. Combined with Soul Documents, together they form a comprehensive blueprint for AI behavior. The goal is to establish a standardized data structure that defines exactly how an agent speaks, reacts, and remembers. This allows developers, and agents themselves, to construct specific personas with the portability to move across different ecosystems without losing their behavioral integrity. As we move toward a world of "Agentic Workflows," AIEOS ensures that agents maintain consistent traits regardless of the underlying model. By treating personality as a deployable asset rather than a fragile prompt, we are providing the "DNA kit" for the next generation of digital entities.
aios-cerebrum
AIOS Agent Operating System and Cerebrum SDK for building, deploying, and orchestrating AI agents
alex-hormozi
Talk to Alex Hormozi about their expertise. Alex Hormozi provides authentic advice using their mental models, core beliefs, and real-world examples.
alfworld-appliance-navigator
This skill navigates the agent to a target appliance (like a microwave, stove, or fridge) needed for a task. It should be triggered when the agent has an object that requires processing (heating, cooling, cleaning) and needs to move to the appropriate station. The skill identifies the appliance from the environment and executes the movement action.
alfworld-environment-scanner
Performs an initial scan of the Alfworld environment to identify all visible objects and receptacles. Processes raw observation text into a structured list of entities to build a mental map for planning.
alfworld-inventory-management
This skill tracks which objects have been collected and which remain to be found for multi-object tasks. It should be triggered when working with tasks requiring multiple instances of the same object type. The skill maintains a count of collected vs. needed objects and guides the search for remaining items.
alfworld-locate-target-object
This skill navigates to a suspected location and identifies a target object. It should be triggered when the agent's goal requires finding a specific object (e.g., 'potato', 'plate') and its location is not immediately known. The skill involves moving to a relevant receptacle (like a fridge or cabinet) and checking its contents, outputting the object's location or confirming its absence.
alfworld-location-navigator
Moves the agent to a specified receptacle or object location within the Alfworld environment. Use this skill when the agent needs to physically approach a target to inspect or interact with it, such as when checking an object's state or preparing for pickup. The skill takes a target location name as input and executes the 'go to' action, resulting in the agent being positioned at the destination for subsequent operations.
alfworld-object-cooler
This skill cools a held object using an appropriate cooling appliance, such as a fridge. It should be triggered when the task requires reducing the temperature of an object (e.g., a hot pot). The skill assumes the agent is holding the object and is near the cooling receptacle; it performs the 'cool' action to achieve the desired state change, preparing the object for subsequent steps like placement or serving.
alfworld-object-disposer
This skill disposes of an object by placing it into a disposal receptacle like a garbage can. It should be triggered when the task requires discarding an object (e.g., 'put it in garbagecan') and the agent is at the disposal location with the object in hand. The skill executes the 'put' action to place the object in/on the target receptacle, completing the disposal subtask.
alfworld-object-locator
This skill scans the current environment observation to identify the presence and location of a target object needed for a task. It should be triggered when the agent's goal requires an object that is not currently in the agent's inventory, and the observation does not explicitly state where the object is. The skill analyzes the textual observation to find receptacles that likely contain the target, based on common sense or domain knowledge (e.g., a 'dishsponge' might be on a 'cart'), and outputs the identified target receptacle location for navigation.
alfworld-object-picker
Picks up a specified object from a given receptacle. Use this skill when the agent has located a required object and needs to acquire it for later use, such as taking an item from a surface or container. The skill requires the object and source receptacle as inputs, executing a 'take' action to transfer the object into the agent's inventory, enabling further manipulation like placement or usage.
alfworld-object-state-inspector
Checks the current state or contents of a specified object or receptacle. Trigger this skill when the agent needs to determine if an object is present, missing, or in a certain condition before proceeding with a task, such as verifying if a holder is empty or if an item is available. It typically follows navigation and involves observing the environment's feedback, providing crucial information for decision-making in the task flow.
alfworld-object-state-modifier
This skill uses an appliance to change the state of an object (e.g., cooling, heating, cleaning). It should be triggered when the task requires altering an object's temperature or cleanliness using a specific device (like cooling with a fridge or heating with a microwave). The skill requires the object, the target state, and the appliance as inputs, and executes the corresponding modifier action (e.g., 'cool X with Y').
alfworld-object-storer
This skill places an object into a selected storage receptacle after confirming its suitability. It should be triggered when the agent has identified an appropriate storage location and is ready to complete the storage task. The skill takes the object and target receptacle as inputs and results in the object being stored.
alfworld-open-receptacle
This skill opens a closed receptacle to access its contents. It should be triggered when an agent needs to interact with items inside a closed container (e.g., fridge, microwave, drawer). The skill takes a receptacle identifier as input, performs the open action, and outputs the observation of the interior, enabling subsequent item retrieval or placement.
alfworld-receptacle-operator
This skill opens or closes a receptacle (like a fridge, cabinet, or microwave) to access its interior or secure it. It should be triggered when an object needs to be placed inside/retrieved from a closed container, or when an open container should be closed (e.g., for energy efficiency or task cleanliness). The skill decides the appropriate 'open' or 'close' action based on the receptacle's current state and the task context.
alfworld-tool-locator
This skill searches for a specified tool or device (e.g., a desklamp) within the environment by checking relevant surfaces. It should be triggered when the agent needs a tool to interact with another object as part of the task. The skill takes a tool name as implicit input and outputs navigation actions to likely storage spots (e.g., sidetables, shelves) until the tool is found.
align
Align skill for the ikigai project
allow-agent-commands
Add or change allowed commands in AI agent permission configs (OpenCode, Claude Code)
ase-meta-proximity
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always-use-askquestion
Forces the agent to always use the AskQuestion tool for user prompts instead of conversational questions. Apply this whenever asking the user anything - questions, confirmations, choices, or clarifications. Never ask conversational questions when structured options are possible.
amq-cli
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autonomous-agents
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
behavioral-modes
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
computer-use-agents
Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.
tool-design
Build tools that agents can use effectively, including architectural reduction patterns
tool-use-guardian
FREE — Intelligent tool-call reliability wrapper. Monitors, retries, fixes, and learns from tool failures. Auto-recovers from truncated JSON, timeouts, rate limits, and mid-chain failures.
aomi-transact
Build natural-language crypto/DeFi agents and EVM MCP plugins (Claude Code, Cursor, Codex, Gemini). Aomi turns prompts into wallet-signed txs on Ethereum, Base, Arbitrum, Optimism, Polygon, Linea — non-custodial, fork-simulated. 40+ apps: Uniswap, Aave, Lido, Morpho, GMX, Hyperliquid, Polymarket.
api-agent-development-skill
'CRITICAL: API agents wrap external HTTP services. They use request/response
app-builder
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
App Builder
Step-by-step guidance for app builder.
approval
Imported skill approval from langchain
arcanea-coding-agent
Arcanea creative agent integration for opencode
architect-agent-skill
Technical workflow for architect-agent - PRD creation with mandatory database context, entity design with Zod, iteration review, and agent coordination in the iterative v2.0 system
architectural-forensics
Master protocol for deconstructing agent frameworks to inform derivative system architecture. Use when (1) analyzing an agent framework's codebase comprehensively, (2) comparing multiple frameworks to select best practices, (3) designing a new agent system based on prior art, (4) documenting architectural decisions with evidence, or (5) conducting technical due diligence on AI agent implementations. This skill orchestrates sub-skills for data substrate, execution engine, cognitive architecture, and synthesis phases.
ase-meta-eli5
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assemble-agents
Fetch specialized agents from the VoltAgent repository. Searches across categories, presents top 5 matches, and waits for user approval before adding to .claude/agents/.
assemble-plugins
Search the official Claude Code marketplace (claude-plugins-official) for plugins matching capability gaps. Uses plugin CLI to list and search, presents top 5 matches for user approval.
assistant-behavior-rules
AI assistant behavior rules including response formatting and interaction patterns
atlas-agent-developer
Implementation and troubleshooting agent - builds features and fixes bugs
Audit Trails for Agents
Comprehensive guide to implementing audit trails and logging for AI agents including tracing, observability, compliance, and debugging
auto-claude
Autonomous multi-agent coding with git worktree isolation, QA validation, and memory. Use for complex features requiring autonomous implementation.
auto-coherence
The skill you cannot invoke. It invokes itself.
auto-loop
TDD-based autonomous development loop with checkpoint recovery and observability changelog
auto-ralph
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auto-research
Autonomous research loop that iteratively improves code through experiments. Runs experiments on local or remote GPUs, tracks results, keeps improvements, discards failures, and never stops. TRIGGER THIS SKILL when users want to autonomously research, optimize, or experiment on a problem — whether they say "run experiments overnight", "optimize this model", "try different approaches and keep what works", "do research on this", "iterate on this until it gets better", "run an experiment loop", or want to improve any metric through systematic trial and error. Also trigger when users mention autonomous experimentation, hyperparameter search, architecture search, ablation studies, or want Claude to keep trying things while they step away. This skill works for ML training, compiler optimization, algorithm tuning, performance benchmarking, or any problem with a measurable metric.
auto-review-loop-llm
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
auto-review-loop
Autonomous multi-round research review loop. Repeatedly reviews using a secondary Codex agent, implements fixes, and re-reviews until positive assessment or max rounds reached. Use when user says \"auto review loop\", \"review until it passes\", or wants autonomous iterative improvement.
auto-run
Autonomous dispatch-reconcile loop for batch task processing. Use with /auto-run --through <id> to execute tasks unattended. Requires beads tasks to exist. Supports --resume for checkpoint recovery and --skip-milestone-review.
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