{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-yicoclaw","slug":"yicoclaw","name":"yicoclaw","type":"agent","url":"https://github.com/yicoclaw/yicoclaw#readme","page_url":"https://unfragile.ai/yicoclaw","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-yicoclaw__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.role.based.task.delegation","name":"multi-agent orchestration with role-based task delegation","description":"Coordinates multiple AI agents with distinct roles and responsibilities, routing tasks to specialized agents based on capability matching and context. Implements a supervisor pattern where a coordinator agent analyzes incoming requests, decomposes them into subtasks, and delegates to worker agents with appropriate system prompts and tool access, aggregating results into coherent outputs.","intents":["I need to break down complex tasks across multiple specialized AI agents without manually managing each one","I want agents with different expertise (code review, testing, documentation) to collaborate on a single project","I need to parallelize agent work while maintaining task dependencies and result ordering"],"best_for":["teams building complex AI workflows requiring specialized agent roles","developers automating multi-step processes that benefit from agent specialization","organizations scaling from single-agent to multi-agent architectures"],"limitations":["No built-in load balancing — all agents run sequentially unless explicitly parallelized","Task decomposition relies on LLM reasoning, which may fail on ambiguous or novel task types","No automatic retry logic for failed agent subtasks — requires explicit error handling in orchestration layer","Context passing between agents can accumulate token overhead with large intermediate results"],"requires":["Node.js 16+","API keys for at least one LLM provider (OpenAI, Anthropic, or compatible)","Understanding of agent role definition and task decomposition patterns"],"input_types":["natural language task descriptions","structured task objects with metadata","code snippets or file paths for context"],"output_types":["aggregated text results from all agents","structured JSON with per-agent outputs","execution logs with agent decision traces"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-yicoclaw__cap_1","uri":"capability://tool.use.integration.tool.use.integration.with.schema.based.function.registry","name":"tool-use integration with schema-based function registry","description":"Provides a declarative function registry system where tools are defined as JSON schemas with execution bindings, enabling agents to discover and invoke external functions with type safety. Supports native integrations with OpenAI and Anthropic function-calling APIs, automatically marshaling arguments and handling response serialization across different LLM provider formats.","intents":["I want agents to call external APIs and functions without hardcoding tool definitions in prompts","I need type-safe tool invocation with automatic argument validation before execution","I want to reuse the same tool definitions across multiple agents and LLM providers"],"best_for":["developers building agent systems that need deterministic tool invocation","teams managing large tool catalogs that must stay in sync across agents","projects requiring audit trails of tool calls with full argument/response logging"],"limitations":["Schema-based approach requires upfront tool definition — dynamic or runtime-generated tools need manual registration","No built-in retry logic for failed tool calls — errors propagate to agent for handling","Tool execution is synchronous — long-running operations block agent processing","Limited to tools that can be expressed as JSON schema — complex stateful tools require wrapper functions"],"requires":["Node.js 16+","Tool definitions as JSON schemas with execution handlers","LLM provider that supports function calling (OpenAI, Anthropic, or compatible)"],"input_types":["JSON schema definitions","JavaScript/TypeScript function implementations","Tool metadata (name, description, parameters)"],"output_types":["structured tool call objects with arguments","tool execution results as JSON","error objects with failure context"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-yicoclaw__cap_10","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.provider.switching","name":"multi-provider llm abstraction with provider switching","description":"Abstracts away provider-specific API differences through a unified interface, allowing agents to switch between LLM providers (OpenAI, Anthropic, Ollama, etc.) without code changes. Handles provider-specific features (function calling formats, streaming, token counting) transparently, with automatic fallback to alternative providers on failure.","intents":["I want to use different LLM providers for different agents based on cost or capability","I need to switch providers without rewriting agent code","I want to implement provider failover for high availability"],"best_for":["teams using multiple LLM providers for cost optimization or capability matching","projects requiring provider independence for vendor lock-in avoidance","organizations implementing high-availability agent systems"],"limitations":["Abstraction layer adds complexity — provider-specific optimizations are harder to implement","API differences between providers may cause subtle behavioral differences","Token counting varies by provider — cost estimates may be inaccurate","Streaming and real-time features may not be uniformly supported across providers"],"requires":["Node.js 16+","API keys for at least one LLM provider","Provider configuration (model names, API endpoints)"],"input_types":["agent requests (messages, tools, parameters)","provider configuration","fallback provider list"],"output_types":["unified LLM responses","provider metadata (model used, tokens consumed)","fallback execution logs"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-yicoclaw__cap_2","uri":"capability://memory.knowledge.context.aware.memory.management.with.sliding.window.and.summarization","name":"context-aware memory management with sliding window and summarization","description":"Manages agent conversation history and working memory using a sliding window approach that preserves recent interactions while summarizing older context to stay within token limits. Implements automatic summarization of conversation segments when memory exceeds thresholds, maintaining semantic continuity while reducing token overhead for long-running agent sessions.","intents":["I need agents to maintain conversation context across multiple turns without hitting token limits","I want to preserve important facts from earlier in the conversation while forgetting irrelevant details","I need to track agent decision history and reasoning chains for debugging and auditing"],"best_for":["long-running agent sessions that span dozens or hundreds of interactions","multi-turn workflows where context accumulation is a bottleneck","teams needing audit trails of agent reasoning and decision-making"],"limitations":["Summarization is lossy — important details may be omitted if not explicitly marked as critical","Automatic summarization adds latency (typically 500ms-2s per summarization event)","No built-in semantic deduplication — redundant context is preserved if not explicitly filtered","Memory persistence requires external storage — in-memory only by default"],"requires":["Node.js 16+","LLM provider for summarization (same provider as agent or separate)","Configuration of memory window size and summarization thresholds"],"input_types":["conversation messages (user, assistant, tool)","metadata tags for importance/retention","memory window configuration"],"output_types":["trimmed conversation history","summarized context segments","memory usage metrics"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-yicoclaw__cap_3","uri":"capability://automation.workflow.agent.state.persistence.and.checkpoint.recovery","name":"agent state persistence and checkpoint recovery","description":"Provides mechanisms to serialize agent execution state (memory, tool results, decision history) to persistent storage and recover from checkpoints, enabling agents to resume work after interruptions or failures. Supports pluggable storage backends (file system, database) and automatic checkpoint creation at configurable intervals or after significant state changes.","intents":["I want agents to survive process crashes and resume work without losing progress","I need to pause long-running agent tasks and resume them later on different infrastructure","I want to debug agent behavior by replaying execution from saved checkpoints"],"best_for":["production agent systems requiring high availability and fault tolerance","long-running batch processing workflows that may span hours or days","teams debugging complex agent behaviors through execution replay"],"limitations":["Checkpoint serialization adds overhead — each checkpoint write may add 100-500ms latency","No built-in versioning — checkpoint format changes require migration logic","State recovery assumes deterministic tool execution — non-deterministic tools may produce different results on replay","Requires external storage infrastructure — no built-in database, requires configuration"],"requires":["Node.js 16+","Persistent storage backend (file system, PostgreSQL, MongoDB, etc.)","Serialization format compatible with agent state (JSON or custom serializer)"],"input_types":["agent execution state objects","checkpoint metadata (timestamp, step count)","storage configuration"],"output_types":["serialized checkpoint files","recovery metadata","execution logs with checkpoint markers"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-yicoclaw__cap_4","uri":"capability://planning.reasoning.agent.behavior.customization.through.system.prompts.and.role.definitions","name":"agent behavior customization through system prompts and role definitions","description":"Allows developers to define agent personalities, constraints, and behavioral guidelines through structured system prompt templates and role definitions. Supports prompt composition where base system prompts are combined with role-specific instructions, tool descriptions, and output format requirements, enabling consistent behavior across agent instances while allowing fine-grained customization.","intents":["I want to create specialized agents (code reviewer, tester, documenter) with distinct personalities and expertise","I need to enforce behavioral constraints (e.g., 'never modify production code without approval')","I want to standardize agent output formats across different agent types"],"best_for":["teams building multiple specialized agents with consistent quality standards","organizations with compliance requirements for agent behavior","projects where agent personality and tone matter (customer-facing agents)"],"limitations":["Prompt-based customization is not guaranteed — LLM may ignore or misinterpret constraints","No built-in testing framework for validating agent behavior against role definitions","Prompt composition can become complex with many layers of inheritance","Changes to system prompts require re-testing all affected agents"],"requires":["Node.js 16+","Understanding of prompt engineering and LLM behavior","LLM provider with consistent instruction-following (GPT-4, Claude 3+)"],"input_types":["system prompt templates (text with variables)","role definitions (JSON with personality traits, constraints)","tool descriptions and output format specifications"],"output_types":["composed system prompts","agent configuration objects","validation reports on prompt coverage"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-yicoclaw__cap_5","uri":"capability://automation.workflow.execution.tracing.and.observability.with.step.by.step.logging","name":"execution tracing and observability with step-by-step logging","description":"Captures detailed execution traces of agent operations including LLM calls, tool invocations, decision points, and state transitions, with structured logging that enables debugging and performance analysis. Provides hooks for custom logging handlers and integrates with observability platforms, recording latency, token usage, and error context at each step.","intents":["I need to debug why an agent made a particular decision or took an unexpected action","I want to monitor agent performance and identify bottlenecks in multi-step workflows","I need to audit agent behavior for compliance and troubleshooting"],"best_for":["production agent systems requiring operational visibility","teams debugging complex agent behaviors in multi-agent systems","organizations with compliance requirements for decision audit trails"],"limitations":["Detailed tracing adds overhead — logging can increase execution time by 10-30%","Trace storage can be expensive at scale — requires log aggregation infrastructure","No built-in trace analysis tools — requires external tools for visualization and querying","Sensitive data (API keys, user inputs) may be captured in traces — requires careful filtering"],"requires":["Node.js 16+","Logging infrastructure (console, file, or observability platform)","Optional: observability platform (DataDog, New Relic, etc.)"],"input_types":["agent execution events","LLM API calls and responses","tool invocation details","state transition metadata"],"output_types":["structured execution logs (JSON)","trace spans with timing information","performance metrics (latency, token usage)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-yicoclaw__cap_6","uri":"capability://automation.workflow.parallel.agent.execution.with.dependency.management","name":"parallel agent execution with dependency management","description":"Enables multiple agents to execute concurrently while respecting task dependencies and data flow constraints. Implements a DAG-based execution model where tasks are defined with explicit dependencies, allowing the framework to parallelize independent tasks while serializing dependent ones, with automatic result aggregation and error propagation.","intents":["I want to speed up multi-agent workflows by running independent tasks in parallel","I need to express complex workflows where some agents depend on outputs from others","I want to maximize resource utilization by parallelizing I/O-bound agent operations"],"best_for":["workflows with multiple independent agents that can run concurrently","teams optimizing agent execution time in latency-sensitive applications","complex multi-step processes with clear task dependencies"],"limitations":["DAG construction requires upfront task definition — dynamic task creation is limited","Parallel execution increases resource consumption (concurrent LLM API calls)","Error in one task may cascade to dependent tasks — requires explicit error handling","Debugging parallel execution is harder than sequential — race conditions are possible"],"requires":["Node.js 16+","Task definitions with explicit dependency declarations","LLM provider with sufficient concurrent request limits"],"input_types":["task definitions with dependency metadata","parallelization configuration (max concurrent tasks)","error handling policies"],"output_types":["aggregated results from all tasks","execution timeline with parallel segments","dependency resolution metadata"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-yicoclaw__cap_7","uri":"capability://tool.use.integration.dynamic.tool.discovery.and.capability.matching","name":"dynamic tool discovery and capability matching","description":"Automatically matches agent capabilities to available tools based on semantic similarity and task requirements, enabling agents to discover and use tools without explicit configuration. Uses embeddings or semantic matching to find relevant tools for a given task, with fallback mechanisms when exact matches aren't available, reducing the need for manual tool registration per agent.","intents":["I want agents to automatically discover relevant tools without manual tool assignment","I need agents to adapt to new tools being added to the system without code changes","I want to reduce the overhead of configuring tool access for each agent"],"best_for":["systems with large tool catalogs where manual assignment is impractical","dynamic environments where tools are frequently added or removed","teams wanting to reduce agent configuration overhead"],"limitations":["Semantic matching is probabilistic — may suggest irrelevant tools or miss relevant ones","Requires tool descriptions to be semantically meaningful — poor descriptions reduce accuracy","Tool discovery adds latency (typically 100-500ms per discovery call)","No built-in tool versioning — multiple versions of the same tool may cause confusion"],"requires":["Node.js 16+","Tool registry with semantic descriptions","Embedding model or semantic matching service"],"input_types":["task descriptions or agent queries","tool catalog with metadata","semantic matching configuration"],"output_types":["ranked list of matching tools","confidence scores for each match","fallback tool suggestions"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-yicoclaw__cap_8","uri":"capability://automation.workflow.error.handling.and.recovery.with.retry.strategies","name":"error handling and recovery with retry strategies","description":"Provides configurable error handling and retry logic for agent operations, tool calls, and LLM API requests, with support for exponential backoff, circuit breakers, and custom recovery strategies. Distinguishes between transient errors (retryable) and permanent errors (fail-fast), with hooks for custom error handlers and recovery logic.","intents":["I want agents to automatically retry failed operations without manual intervention","I need to handle rate limiting and transient API failures gracefully","I want to implement custom recovery strategies for specific error types"],"best_for":["production agent systems requiring resilience to transient failures","workflows with external dependencies (APIs, databases) that may fail temporarily","teams building fault-tolerant agent systems"],"limitations":["Retry logic can mask underlying issues — excessive retries may hide systemic problems","Exponential backoff can increase total execution time significantly for flaky operations","No built-in circuit breaker for cascading failures — requires explicit configuration","Custom recovery logic must be implemented per error type — no one-size-fits-all solution"],"requires":["Node.js 16+","Configuration of retry policies (max attempts, backoff strategy)","Error classification logic (transient vs permanent)"],"input_types":["error objects from failed operations","retry configuration (max attempts, backoff multiplier)","custom error handlers"],"output_types":["retry decisions (retry, fail, custom recovery)","execution logs with retry history","error metrics and failure patterns"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-yicoclaw__cap_9","uri":"capability://automation.workflow.agent.performance.monitoring.and.metrics.collection","name":"agent performance monitoring and metrics collection","description":"Collects and aggregates performance metrics for agent operations including execution time, token usage, API costs, success rates, and tool utilization. Provides real-time dashboards and historical analysis capabilities, with support for custom metrics and integration with monitoring platforms for alerting and trend analysis.","intents":["I want to track how much my agents cost to run and optimize for cost efficiency","I need to identify performance bottlenecks in agent workflows","I want to monitor agent reliability and success rates over time"],"best_for":["teams running production agent systems with cost constraints","organizations optimizing agent performance and efficiency","projects requiring detailed usage analytics and billing"],"limitations":["Metrics collection adds overhead — can increase execution time by 5-15%","Accurate cost tracking requires integration with LLM provider billing APIs","No built-in cost optimization — metrics are informational only","Historical data retention requires external storage — in-memory metrics are lost on restart"],"requires":["Node.js 16+","Metrics collection infrastructure (Prometheus, CloudWatch, etc.)","LLM provider API for token usage and cost data"],"input_types":["agent execution events","LLM API responses with token counts","tool execution timing data","custom metric definitions"],"output_types":["aggregated performance metrics","cost breakdowns by agent/tool/operation","performance trends and anomalies"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+","API keys for at least one LLM provider (OpenAI, Anthropic, or compatible)","Understanding of agent role definition and task decomposition patterns","Tool definitions as JSON schemas with execution handlers","LLM provider that supports function calling (OpenAI, Anthropic, or compatible)","API keys for at least one LLM provider","Provider configuration (model names, API endpoints)","LLM provider for summarization (same provider as agent or separate)","Configuration of memory window size and summarization thresholds","Persistent storage backend (file system, PostgreSQL, MongoDB, etc.)"],"failure_modes":["No built-in load balancing — all agents run sequentially unless explicitly parallelized","Task decomposition relies on LLM reasoning, which may fail on ambiguous or novel task types","No automatic retry logic for failed agent subtasks — requires explicit error handling in orchestration layer","Context passing between agents can accumulate token overhead with large intermediate results","Schema-based approach requires upfront tool definition — dynamic or runtime-generated tools need manual registration","No built-in retry logic for failed tool calls — errors propagate to agent for handling","Tool execution is synchronous — long-running operations block agent processing","Limited to tools that can be expressed as JSON schema — complex stateful tools require wrapper functions","Abstraction layer adds complexity — provider-specific optimizations are harder to implement","API differences between providers may cause subtle behavioral differences","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.1976716704363382,"quality":0.32,"ecosystem":0.39999999999999997,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.651Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":4739,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=yicoclaw","compare_url":"https://unfragile.ai/compare?artifact=yicoclaw"}},"signature":"E9X4l84D7F2AEXQyYDPmdrNHmNbFD1d+fja/vH2zbo/+Vf69AgP3gdSbIanha1YwGhuiOD4dud5VEW29VQPDCg==","signedAt":"2026-06-20T03:04:06.502Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/yicoclaw","artifact":"https://unfragile.ai/yicoclaw","verify":"https://unfragile.ai/api/v1/verify?slug=yicoclaw","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"}}