{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-code-yeongyu--oh-my-openagent","slug":"code-yeongyu--oh-my-openagent","name":"oh-my-openagent","type":"agent","url":"https://ohmyopenagent.com/","page_url":"https://unfragile.ai/code-yeongyu--oh-my-openagent","categories":["ai-agents"],"tags":["ai","ai-agents","amp","anthropic","chatgpt","claude","claude-code","claude-skills","cursor","gemini","ide","openai","opencode","orchestration","tui","typescript"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-code-yeongyu--oh-my-openagent__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.role.specific.task.delegation","name":"multi-agent orchestration with role-specific task delegation","description":"Sisyphus main orchestrator coordinates 11 specialized agents (Hephaestus, Oracle, Librarian, Explore, Atlas, Prometheus, Metis, Momus, Multimodal-Looker, Sisyphus-Junior) with role-specific prompts and tool permission matrices. Each agent is matched to tasks based on capability profiles and model compatibility, with dynamic prompt building that injects agent-specific context. The orchestrator implements a planning workflow that decomposes user intent into subtasks, delegates to appropriate agents, and aggregates results.","intents":["I need to break down a complex coding task across multiple specialized AI agents with different expertise","I want agents to automatically choose the right tool for each subtask without manual intervention","I need to ensure each agent only has access to tools appropriate for its role"],"best_for":["teams building autonomous multi-agent systems for code generation and refactoring","developers who need task decomposition with specialized agent roles","builders implementing agent-based CI/CD pipelines with role-based access control"],"limitations":["Agent-to-task matching relies on static capability profiles; no learned routing based on historical performance","Orchestration overhead adds latency per delegation step; no built-in batching for parallel independent subtasks","Role-specific prompts must be manually maintained; no automatic prompt optimization across agent types"],"requires":["Node.js 18+","API keys for at least one LLM provider (OpenAI, Anthropic, Google Gemini)","oh-my-openagent npm package installed"],"input_types":["natural language task descriptions","code snippets with context","structured task definitions with category tags"],"output_types":["code modifications with hash-anchored edits","task execution results with agent attribution","structured task completion reports"],"categories":["planning-reasoning","tool-use-integration","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_1","uri":"capability://code.generation.editing.hash.anchored.code.editing.with.content.validation","name":"hash-anchored code editing with content validation","description":"The hashline_edit tool implements line-level content hashing (LINE#ID format) that validates code before applying modifications, ensuring zero-error edits by confirming the target content matches expected state. Each editable line is tagged with a hash of its content; edits are rejected if the hash doesn't match, preventing off-by-one errors and stale edit conflicts. This pattern integrates with AST-Grep for structural code navigation and LSP for semantic awareness.","intents":["I need to ensure code edits are applied to the exact intended lines without off-by-one errors","I want to prevent concurrent edit conflicts when multiple agents modify the same file","I need reliable code modification that validates content before applying changes"],"best_for":["multi-agent systems where concurrent edits risk collision","developers building reliable code transformation pipelines","teams needing audit trails of exactly which code was modified"],"limitations":["Hash validation adds ~50-100ms per edit operation for content verification","Requires maintaining hash state across edit sessions; stateless systems cannot use this pattern","Hash collisions theoretically possible (though negligible with SHA-based hashing); no cryptographic guarantee"],"requires":["oh-my-openagent with hashline_edit tool enabled","target files must be readable and writable","hash computation library (built-in to oh-my-openagent)"],"input_types":["code file paths","line numbers with hash identifiers","replacement code content"],"output_types":["modified code files with edit confirmation","hash validation results","edit conflict reports"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_10","uri":"capability://planning.reasoning.planning.workflow.with.task.decomposition","name":"planning workflow with task decomposition","description":"Implements a planning workflow where planning agents (Oracle, Librarian) decompose complex user intents into structured subtasks before delegation to worker agents. Planning agents analyze the task, identify dependencies, and create an execution plan with task ordering and resource requirements. The plan is validated before execution, ensuring feasibility. This two-phase approach (plan then execute) reduces agent errors and enables better resource allocation.","intents":["I need to break down complex tasks into subtasks before execution","I want to identify task dependencies and execution order","I need to validate task plans before committing resources"],"best_for":["developers building complex multi-step agent workflows","teams implementing task decomposition for large projects","builders needing structured task planning before execution"],"limitations":["Planning adds latency; simple tasks may not benefit from planning overhead","Plan validation is heuristic-based; invalid plans may still be generated","No automatic plan optimization; generated plans may be suboptimal"],"requires":["oh-my-openagent with planning agents enabled","planning agents (Oracle, Librarian) configured","task decomposition prompts"],"input_types":["user intent descriptions","task context and constraints"],"output_types":["structured task plans","subtask definitions","execution order and dependencies","plan validation results"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_11","uri":"capability://automation.workflow.ultrawork.mode.for.continuous.autonomous.execution","name":"ultrawork mode for continuous autonomous execution","description":"Implements Ultrawork mode, a continuous execution mode where agents autonomously execute tasks without waiting for user confirmation between steps. Agents monitor task progress, handle errors, and adapt execution based on results. Ultrawork mode includes safeguards (resource limits, timeout enforcement, error thresholds) to prevent runaway execution. Session continuity ensures tasks can be resumed if interrupted.","intents":["I need agents to work autonomously without waiting for my input between steps","I want continuous execution with automatic error handling and adaptation","I need safeguards to prevent runaway autonomous execution"],"best_for":["developers building autonomous code generation pipelines","teams implementing hands-off agent workflows","builders needing continuous execution with safety guardrails"],"limitations":["Ultrawork mode can consume significant resources; no built-in cost tracking","Error recovery is heuristic-based; agents may not recover from all error types","Safeguards (timeouts, resource limits) must be manually configured; no automatic tuning"],"requires":["oh-my-openagent with Ultrawork mode enabled","resource limits and timeout configuration","error handling and recovery strategies"],"input_types":["task definitions","execution constraints (timeout, resource limits)","error recovery strategies"],"output_types":["continuous execution results","error logs and recovery actions","execution progress updates"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_12","uri":"capability://code.generation.editing.deep.work.mode.with.hephaestus.specialized.agent","name":"deep work mode with hephaestus specialized agent","description":"Implements Deep Work mode, a focused execution mode where the Hephaestus agent (specialized in complex code generation and refactoring) works deeply on a single task with extended context and reasoning. Hephaestus has access to advanced tools (AST-Grep, LSP, code analysis) and can maintain longer reasoning chains. Deep Work mode is optimized for complex tasks requiring sustained focus, unlike Ultrawork's breadth-first approach.","intents":["I need an agent to work deeply on a complex coding task with extended reasoning","I want specialized expertise in code generation and refactoring","I need agents to maintain context across long reasoning chains"],"best_for":["developers tackling complex code refactoring or generation tasks","teams implementing deep code analysis and transformation","builders needing specialized expertise for difficult coding problems"],"limitations":["Deep Work mode is slower than Ultrawork; not suitable for simple tasks","Extended reasoning chains consume more tokens; cost per task is higher","Hephaestus is specialized for code; not suitable for non-code tasks"],"requires":["oh-my-openagent with Deep Work mode enabled","Hephaestus agent configured","advanced tools (AST-Grep, LSP) available"],"input_types":["complex code tasks","code context and constraints","reasoning requirements"],"output_types":["deeply analyzed code solutions","refactoring recommendations","extended reasoning traces"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_13","uri":"capability://automation.workflow.non.interactive.and.ci.mode.for.automated.pipelines","name":"non-interactive and ci mode for automated pipelines","description":"Implements non-interactive and CI modes where agents execute without user interaction, suitable for automated CI/CD pipelines and batch processing. In CI mode, agents read input from files or environment variables and write output to files or stdout. Error handling is strict; agents fail fast on errors rather than attempting recovery. CI mode integrates with standard Unix tools (pipes, redirection) for easy pipeline composition.","intents":["I need to run agents in CI/CD pipelines without user interaction","I want to compose agent workflows with Unix pipes and redirection","I need strict error handling and fast failure for pipeline reliability"],"best_for":["teams implementing agent-based CI/CD pipelines","developers building automated code generation workflows","builders needing batch processing with agents"],"limitations":["CI mode lacks interactive debugging; errors are harder to diagnose","No user confirmation for destructive operations; requires careful validation","Unix tool integration is limited to stdin/stdout; no native support for other data sources"],"requires":["oh-my-openagent with CI mode enabled","input files or environment variables for task definition","Unix shell for pipeline composition"],"input_types":["files","environment variables","stdin"],"output_types":["files","stdout","exit codes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_14","uri":"capability://code.generation.editing.debugging.and.consultation.workflow.with.oracle.agent","name":"debugging and consultation workflow with oracle agent","description":"Implements a debugging workflow where the Oracle agent analyzes errors, generates debugging hypotheses, and recommends fixes. Oracle has access to error logs, stack traces, and code context. The workflow supports interactive debugging (user provides feedback) and automated debugging (Oracle generates and tests fixes). Debugging results are logged for future reference.","intents":["I need an agent to analyze errors and suggest fixes","I want to interactively debug issues with agent assistance","I need automated debugging that generates and tests fixes"],"best_for":["developers debugging complex code issues with agent assistance","teams implementing automated error analysis and fixing","builders needing interactive debugging workflows"],"limitations":["Oracle's debugging is heuristic-based; not all bugs can be automatically diagnosed","Automated fix generation may introduce new bugs; requires testing","Debugging logs can be large; no built-in log compression or archival"],"requires":["oh-my-openagent with debugging workflow enabled","Oracle agent configured","error logs and stack traces available"],"input_types":["error messages","stack traces","code context","user feedback (interactive mode)"],"output_types":["debugging hypotheses","fix recommendations","test results","debugging logs"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_15","uri":"capability://automation.workflow.concurrency.and.parallelism.with.task.batching","name":"concurrency and parallelism with task batching","description":"Implements concurrent agent execution with task batching, enabling multiple agents to work in parallel on independent subtasks. The orchestrator analyzes task dependencies and groups independent tasks for parallel execution. Concurrency is managed via a configurable thread pool; parallelism is limited by available resources. Results are aggregated after all parallel tasks complete.","intents":["I need multiple agents to work in parallel on independent tasks","I want to batch independent subtasks for faster execution","I need to manage concurrency without manual thread management"],"best_for":["developers building high-throughput agent systems","teams implementing parallel code generation workflows","builders needing efficient resource utilization"],"limitations":["Dependency analysis is static; dynamic dependencies discovered at runtime cannot be parallelized","Thread pool size must be manually configured; no automatic tuning based on workload","Result aggregation is synchronous; no streaming results from parallel tasks"],"requires":["oh-my-openagent with concurrency enabled","thread pool configuration","task dependency analysis"],"input_types":["task definitions","dependency graphs"],"output_types":["parallel execution results","aggregated results","concurrency metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_16","uri":"capability://tool.use.integration.tmux.subagent.integration.for.terminal.based.workflows","name":"tmux subagent integration for terminal-based workflows","description":"Integrates with Tmux (terminal multiplexer) to enable agents to spawn and manage terminal sessions for interactive workflows. Agents can send commands to Tmux sessions, capture output, and respond to interactive prompts. This enables agents to work with CLI tools, REPLs, and interactive applications. Tmux sessions are persistent across agent invocations, enabling stateful workflows.","intents":["I need agents to interact with CLI tools and terminal applications","I want agents to work with REPLs and interactive shells","I need persistent terminal sessions across multiple agent invocations"],"best_for":["developers building agents that work with CLI tools","teams implementing terminal-based automation workflows","builders needing interactive terminal access from agents"],"limitations":["Tmux integration is Unix-only; no Windows support","Terminal output parsing is fragile; ANSI escape codes and formatting can confuse agents","Interactive prompts require careful handling; agents may timeout waiting for input"],"requires":["oh-my-openagent with Tmux integration enabled","Tmux installed and configured","Unix-like OS (Linux, macOS)"],"input_types":["terminal commands","Tmux session names","interactive prompts"],"output_types":["terminal output","command results","session status"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_17","uri":"capability://automation.workflow.ralph.loop.and.todo.enforcement.for.task.tracking","name":"ralph loop and todo enforcement for task tracking","description":"Implements Ralph Loop, a task tracking pattern where agents maintain a todo list of remaining tasks and enforce completion before marking tasks done. Agents check off completed tasks and update the todo list as work progresses. This pattern prevents agents from skipping tasks or declaring completion prematurely. Todo enforcement is integrated with the planning workflow, ensuring all planned tasks are executed.","intents":["I need agents to track task completion and prevent skipping tasks","I want to enforce that all planned tasks are executed","I need visibility into remaining work and task progress"],"best_for":["developers building reliable multi-step workflows","teams implementing task tracking in agent systems","builders needing task completion enforcement"],"limitations":["Ralph Loop adds overhead; agents must maintain and update todo lists","Todo list accuracy depends on agent honesty; agents could falsely mark tasks complete","No automatic task prioritization; agents execute tasks in planned order"],"requires":["oh-my-openagent with Ralph Loop enabled","planning workflow to generate initial todo list","task completion validation"],"input_types":["task definitions","todo list state"],"output_types":["updated todo list","task completion status","remaining work"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_18","uri":"capability://automation.workflow.model.error.recovery.with.automatic.retry.and.fallback","name":"model error recovery with automatic retry and fallback","description":"Implements automatic error recovery for model failures (rate limits, timeouts, API errors) with configurable retry strategies and fallback models. When a model fails, the system automatically retries with exponential backoff, then falls back to alternative models if retries are exhausted. Error recovery is transparent to agents; they don't need to handle model errors explicitly.","intents":["I need agents to automatically recover from model API failures","I want automatic retry with exponential backoff for transient errors","I need fallback models when primary models are unavailable"],"best_for":["teams building resilient agent systems","developers using multiple LLM providers","builders needing automatic error recovery"],"limitations":["Retry strategies are fixed; no adaptive retry based on error type","Fallback models may have different capabilities; agents may fail with fallback models","Exponential backoff can cause long delays for persistent failures"],"requires":["oh-my-openagent with error recovery enabled","retry and fallback configuration","multiple LLM providers (optional but recommended)"],"input_types":["model requests","error responses"],"output_types":["successful model responses","error recovery logs","fallback model used"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_2","uri":"capability://automation.workflow.52.tier.lifecycle.hook.system.with.session.continuity","name":"52-tier lifecycle hook system with session continuity","description":"Implements a hierarchical hook system organized into 5 tiers (Session, Tool-Guard, Transform, Continuation, Skill) with 52 total hooks that fire at specific points in the agent execution lifecycle. Hooks enable custom behavior injection at pre-execution, post-execution, tool-call, and result-transform stages. Session hooks maintain state across multiple agent invocations, enabling multi-turn workflows and context preservation. Continuation hooks allow resuming interrupted tasks with full state recovery.","intents":["I need to inject custom logic at specific points in the agent execution pipeline","I want to maintain session state across multiple agent calls without losing context","I need to resume long-running tasks from interruption points without restarting"],"best_for":["developers building custom agent workflows with complex state management","teams implementing multi-turn interactive agent sessions","builders needing fine-grained control over agent execution lifecycle"],"limitations":["Hook execution is synchronous; async hooks require wrapping in Promise.all, adding complexity","52 hooks create a large API surface; discovering relevant hooks requires documentation reading","Session state is in-memory by default; requires external persistence layer for durability across process restarts"],"requires":["oh-my-openagent with hook system enabled","TypeScript or JavaScript runtime","understanding of hook tier hierarchy (Session > Tool-Guard > Transform > Continuation > Skill)"],"input_types":["hook registration callbacks","session context objects","agent execution events"],"output_types":["modified execution state","hook execution results","session continuity tokens"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_3","uri":"capability://code.generation.editing.26.built.in.tools.with.lsp.and.ast.grep.integration","name":"26 built-in tools with lsp and ast-grep integration","description":"Provides a curated toolkit of 26 tools including Language Server Protocol (LSP) integration for semantic code analysis, AST-Grep for structural code navigation, task delegation primitives, background execution handlers, and session management utilities. Tools are organized by category (Code Manipulation, Interactive, Background Task) with explicit tool permission matrices per agent. LSP integration enables type-aware refactoring; AST-Grep provides syntax-tree-based code search and transformation without regex brittleness.","intents":["I need semantic code analysis (types, definitions, references) without parsing code myself","I want to search and transform code based on syntax structure, not regex patterns","I need to delegate subtasks to background workers and poll for completion"],"best_for":["developers building code analysis and refactoring agents","teams implementing language-aware code transformation","builders needing background task execution within agent workflows"],"limitations":["LSP integration requires language servers to be installed and running; not all languages have mature LSP implementations","AST-Grep supports 20+ languages but with varying feature completeness; some languages have limited query support","Tool permission matrices are static; no dynamic capability negotiation based on runtime constraints"],"requires":["oh-my-openagent with tools enabled","language servers installed for target languages (e.g., typescript-language-server for TypeScript)","AST-Grep binary in PATH for structural code queries"],"input_types":["file paths","code snippets","LSP query parameters","AST-Grep pattern expressions"],"output_types":["semantic code information (types, definitions, references)","AST-based search results","code transformation results","background task status"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_4","uri":"capability://tool.use.integration.3.tier.mcp.system.with.skill.embedded.servers","name":"3-tier mcp system with skill-embedded servers","description":"Implements a three-tier Model Context Protocol (MCP) system that merges built-in MCP servers, Claude Code MCP servers, and skill-embedded MCP servers into a unified tool namespace. Built-in servers provide core functionality; Claude Code servers extend with IDE-specific capabilities; skill-embedded servers are dynamically loaded from the skills system. MCP configuration is declarative via JSON schema, enabling zero-code MCP server registration and tool discovery.","intents":["I need to extend agent capabilities with external MCP servers without modifying core code","I want to use Claude Code MCP servers alongside custom skill-embedded servers","I need declarative MCP configuration that doesn't require code changes"],"best_for":["developers extending oh-my-openagent with custom MCP servers","teams integrating Claude Code MCP ecosystem","builders implementing skill-based capability extension"],"limitations":["MCP server discovery is static at startup; no hot-reloading of new servers without restart","Tool namespace conflicts between tiers are resolved by tier priority; no explicit conflict resolution UI","MCP OAuth authentication requires manual setup per server; no centralized OAuth token management"],"requires":["oh-my-openagent with MCP system enabled","MCP server binaries or Node.js packages for each server","MCP configuration in oh-my-opencode.schema.json","OAuth credentials for authenticated MCP servers (optional)"],"input_types":["MCP server configuration objects","MCP tool definitions","skill definitions with embedded MCP servers"],"output_types":["unified tool namespace","MCP server status","tool discovery results"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_5","uri":"capability://tool.use.integration.skills.system.with.dynamic.prompt.injection","name":"skills system with dynamic prompt injection","description":"Implements a skills system where reusable capability bundles (skills) can be dynamically loaded and injected into agent prompts at runtime. Skills encapsulate domain-specific knowledge, tools, and MCP servers. Dynamic prompt building injects skill context into agent prompts, enabling agents to discover and use skill capabilities without explicit tool registration. Skills are versioned and can be enabled/disabled per agent or globally.","intents":["I want to package reusable agent capabilities (tools, knowledge, MCPs) as skills","I need agents to automatically discover and use available skills without configuration","I want to enable/disable skills dynamically without restarting agents"],"best_for":["teams building skill libraries for agent reuse","developers implementing domain-specific agent capabilities","builders needing modular agent capability management"],"limitations":["Skill discovery is prompt-based; agents may not discover all available skills if prompt context is limited","Skill versioning is manual; no automatic compatibility checking between skill versions and agent versions","Skill dependencies are not explicitly modeled; circular skill dependencies can cause issues"],"requires":["oh-my-openagent with skills system enabled","skill definitions in TypeScript or JSON format","skill registration in configuration"],"input_types":["skill definitions with tools and MCP servers","skill metadata (name, version, description)","agent configuration with skill enablement"],"output_types":["dynamically modified agent prompts","skill discovery results","skill execution results"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_6","uri":"capability://automation.workflow.background.task.execution.with.polling.and.state.recovery","name":"background task execution with polling and state recovery","description":"Provides background task tools that enable agents to spawn long-running tasks (e.g., builds, tests, deployments) and poll for completion without blocking. Tasks are assigned unique IDs and their state is persisted, enabling recovery if the agent session is interrupted. Background task tools integrate with the session continuity system, allowing agents to resume monitoring tasks across session boundaries.","intents":["I need to run long-running operations (builds, tests) without blocking the agent","I want to poll task status and retrieve results when ready","I need to resume monitoring background tasks if my agent session is interrupted"],"best_for":["developers building CI/CD agent workflows","teams implementing long-running task orchestration","builders needing non-blocking task execution in agent pipelines"],"limitations":["Polling adds latency; no push-based task completion notifications","Task state persistence requires external storage; in-memory state is lost on process restart","No built-in timeout enforcement; long-running tasks can consume resources indefinitely"],"requires":["oh-my-openagent with background task tools enabled","external task execution system (e.g., CI/CD platform, job queue)","persistent storage for task state (optional but recommended)"],"input_types":["task definitions (command, parameters, timeout)","task IDs for polling","session context for state recovery"],"output_types":["task IDs","task status (running, completed, failed)","task results and logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_7","uri":"capability://planning.reasoning.agent.model.matching.with.fallback.resolution","name":"agent-model matching with fallback resolution","description":"Implements dynamic agent-to-model matching that selects the optimal LLM provider and model for each agent based on capability profiles and availability. If the primary model is unavailable or rate-limited, the system automatically falls back to alternative models with compatible capabilities. Model resolution is declarative via configuration, enabling zero-code model switching without agent code changes.","intents":["I need agents to automatically select the best available model based on their task","I want fallback models if my primary provider is rate-limited or unavailable","I need to switch models globally without modifying agent code"],"best_for":["teams using multiple LLM providers (OpenAI, Anthropic, Google)","developers building resilient agent systems with fallback capabilities","builders implementing cost-optimized model selection"],"limitations":["Model capability profiles are static; no learned model selection based on task performance history","Fallback resolution is sequential; no parallel model attempts for faster recovery","Model cost optimization is not built-in; requires manual configuration of model preferences"],"requires":["oh-my-openagent with model resolution enabled","API keys for at least one LLM provider","model configuration in oh-my-opencode.schema.json"],"input_types":["agent capability profiles","model availability status","fallback model preferences"],"output_types":["selected model and provider","fallback model if primary unavailable","model resolution logs"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_8","uri":"capability://planning.reasoning.categories.system.for.task.classification.and.routing","name":"categories system for task classification and routing","description":"Implements a categories system that classifies tasks into semantic categories (e.g., 'refactoring', 'testing', 'documentation'), enabling automatic routing to appropriate agents and tools. Categories are hierarchical and configurable; each category can specify preferred agents, allowed tools, and execution constraints. Task classification is performed by planning agents before delegation, ensuring tasks reach the right agent.","intents":["I need to automatically route tasks to the right agent based on task type","I want to enforce tool permissions per task category","I need to classify tasks semantically without manual labeling"],"best_for":["teams implementing task routing in multi-agent systems","developers building category-aware agent workflows","builders needing fine-grained task classification"],"limitations":["Category classification is prompt-based; misclassification can route tasks to wrong agents","Categories are statically defined; no learned category discovery from task patterns","Hierarchical categories add complexity; flat category systems are simpler but less expressive"],"requires":["oh-my-openagent with categories system enabled","category definitions in configuration","planning agents to perform classification"],"input_types":["task descriptions","category definitions","agent-category mappings"],"output_types":["classified task category","routing decision","category-specific constraints"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-code-yeongyu--oh-my-openagent__cap_9","uri":"capability://automation.workflow.configuration.pipeline.with.schema.validation","name":"configuration pipeline with schema validation","description":"Implements a declarative configuration pipeline using JSON schema (oh-my-opencode.schema.json) that validates all configuration before runtime. Configuration covers agents, models, tools, hooks, skills, categories, and MCP servers. Schema validation catches configuration errors early, preventing runtime failures. Configuration is composable; multiple configuration files can be merged with explicit precedence rules.","intents":["I need to validate my agent configuration before deploying","I want to compose configuration from multiple files without conflicts","I need to understand all available configuration options"],"best_for":["teams managing complex agent configurations","developers building configuration-driven agent systems","builders needing configuration validation and composition"],"limitations":["Schema validation is static; no runtime configuration validation","Configuration composition precedence rules must be manually specified","Large configuration files can be difficult to manage; no built-in configuration splitting utilities"],"requires":["oh-my-openagent with configuration pipeline enabled","JSON schema validator (built-in to oh-my-openagent)","oh-my-opencode.schema.json for validation"],"input_types":["JSON configuration files","configuration objects"],"output_types":["validated configuration","validation errors","merged configuration"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","API keys for at least one LLM provider (OpenAI, Anthropic, Google Gemini)","oh-my-openagent npm package installed","oh-my-openagent with hashline_edit tool enabled","target files must be readable and writable","hash computation library (built-in to oh-my-openagent)","oh-my-openagent with planning agents enabled","planning agents (Oracle, Librarian) configured","task decomposition prompts","oh-my-openagent with Ultrawork mode enabled"],"failure_modes":["Agent-to-task matching relies on static capability profiles; no learned routing based on historical performance","Orchestration overhead adds latency per delegation step; no built-in batching for parallel independent subtasks","Role-specific prompts must be manually maintained; no automatic prompt optimization across agent types","Hash validation adds ~50-100ms per edit operation for content verification","Requires maintaining hash state across edit sessions; stateless systems cannot use this pattern","Hash collisions theoretically possible (though negligible with SHA-based hashing); no cryptographic guarantee","Planning adds latency; simple tasks may not benefit from planning overhead","Plan validation is heuristic-based; invalid plans may still be generated","No automatic plan optimization; generated plans may be suboptimal","Ultrawork mode can consume significant resources; no built-in cost tracking","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8310780580603149,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:06.483Z","last_commit":"2026-05-03T09:43:40Z"},"community":{"stars":55560,"forks":4506,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=code-yeongyu--oh-my-openagent","compare_url":"https://unfragile.ai/compare?artifact=code-yeongyu--oh-my-openagent"}},"signature":"cO4PiHtIfbicFoXxT/2uFThVZwa4R1WjmwPFBR0967wG/77VRThZxRQYfdO+0HIr1i0AXEij9JMc5RWihX1LAQ==","signedAt":"2026-06-20T22:36:29.481Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/code-yeongyu--oh-my-openagent","artifact":"https://unfragile.ai/code-yeongyu--oh-my-openagent","verify":"https://unfragile.ai/api/v1/verify?slug=code-yeongyu--oh-my-openagent","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}