{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-getpaseo--paseo","slug":"getpaseo--paseo","name":"paseo","type":"agent","url":"https://paseo.sh","page_url":"https://unfragile.ai/getpaseo--paseo","categories":["ai-agents"],"tags":["ade","agents","claude-code","codex","copilot","developer-tools","gemini","mobile","opencode","orchestration","pi"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-getpaseo--paseo__cap_0","uri":"capability://tool.use.integration.remote.agent.orchestration.via.cli","name":"remote-agent-orchestration-via-cli","description":"Orchestrates coding agents (Claude, Gemini, Copilot) from a CLI interface by establishing a command-line control plane that routes agent instructions to remote execution environments. Uses a client-server architecture where the CLI acts as a control interface, serializing agent tasks and receiving structured execution results back, enabling developers to trigger multi-step coding workflows without leaving the terminal.","intents":["I want to run Claude Code or similar agents from my terminal without opening a web UI","I need to orchestrate multiple coding agents in sequence from a single command","I want to automate agent-driven code generation as part of my CI/CD pipeline"],"best_for":["developers who prefer CLI-first workflows","teams building agent-driven automation pipelines","infrastructure engineers integrating agents into existing toolchains"],"limitations":["Requires network connectivity to remote agent services; no offline execution mode","Agent response latency depends on remote service availability and queue depth","Limited to agents with public APIs or supported integrations (Claude, Gemini, Copilot)"],"requires":["Node.js 16+ (TypeScript runtime)","API credentials for at least one supported agent provider","Network access to remote agent services"],"input_types":["code snippets","file paths","natural language instructions","structured task definitions"],"output_types":["generated code","execution logs","structured results","error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getpaseo--paseo__cap_1","uri":"capability://tool.use.integration.mobile.agent.control.interface","name":"mobile-agent-control-interface","description":"Provides a mobile-optimized interface (iOS/Android) for controlling remote coding agents, allowing developers to trigger agent tasks, monitor execution, and retrieve results from their phone. Implements a lightweight mobile client that communicates with the orchestration backend via REST or WebSocket APIs, with optimized UI for touch interaction and low-bandwidth scenarios.","intents":["I want to trigger code generation tasks from my phone while away from my desk","I need to monitor long-running agent tasks and receive notifications on completion","I want to review and approve agent-generated code changes from mobile"],"best_for":["on-call developers managing production systems","remote teams needing asynchronous agent task management","developers who want to start coding tasks during commute or breaks"],"limitations":["Mobile UI may not support complex code review workflows; better for task initiation than detailed inspection","Network latency on mobile networks can delay agent response feedback","Touch-based interaction limits ability to write complex multi-line code instructions"],"requires":["iOS 13+ or Android 8+","Network connectivity (WiFi or cellular)","Account credentials for orchestration backend"],"input_types":["natural language task descriptions","predefined task templates","voice input (if supported)"],"output_types":["task status updates","code snippets (read-only)","execution logs","push notifications"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getpaseo--paseo__cap_10","uri":"capability://automation.workflow.agent.task.scheduling.and.batch.execution","name":"agent-task-scheduling-and-batch-execution","description":"Schedules agent tasks for execution at specified times or on recurring schedules, and batches multiple tasks for efficient execution. Implements a task queue with scheduling support (cron-like syntax), batch processing to reduce API calls, and execution monitoring.","intents":["I want to schedule agent tasks to run at off-peak hours to reduce costs","I need to run the same agent task on multiple files/inputs in a batch","I want to set up recurring agent tasks (e.g., daily code review) without manual triggering"],"best_for":["teams running agent tasks on a schedule","batch processing workflows with many similar tasks","cost-optimization scenarios where off-peak execution is cheaper"],"limitations":["Scheduling adds complexity to debugging; scheduled task failures may go unnoticed","Batch execution requires careful error handling to prevent cascading failures","Task queue persistence requires external storage (Redis, database)"],"requires":["Task queue backend (Redis, RabbitMQ, or database)","Scheduler service (cron-like or job scheduler)","Monitoring and alerting for scheduled tasks"],"input_types":["task definitions","schedule specifications (cron syntax)","batch input lists"],"output_types":["scheduled task IDs","batch execution results","execution logs"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getpaseo--paseo__cap_11","uri":"capability://planning.reasoning.agent.collaboration.and.multi.agent.workflows","name":"agent-collaboration-and-multi-agent-workflows","description":"Orchestrates multi-agent workflows where multiple agents collaborate on a task, passing results between agents and coordinating execution. Implements agent communication patterns (sequential, parallel, branching) and result aggregation for complex tasks requiring multiple agents.","intents":["I want to have one agent write code and another agent review it","I need to run multiple agents in parallel on different parts of a task and combine results","I want to create a workflow where agents hand off work to each other based on intermediate results"],"best_for":["complex coding tasks requiring multiple perspectives","teams wanting to combine strengths of different agents","workflows with natural handoff points between agents"],"limitations":["Multi-agent workflows add latency due to sequential execution and inter-agent communication","Coordinating multiple agents increases complexity and failure modes","Result aggregation from different agents may require custom logic"],"requires":["Multiple agent provider credentials","Workflow definition format (DAG or state machine)","Result aggregation and validation logic"],"input_types":["workflow definitions","initial task description","agent configurations"],"output_types":["aggregated results","execution traces","inter-agent communication logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getpaseo--paseo__cap_2","uri":"capability://tool.use.integration.multi.provider.agent.abstraction","name":"multi-provider-agent-abstraction","description":"Abstracts over multiple coding agent providers (Claude, Gemini, Copilot, OpenCode) through a unified task interface, allowing users to switch providers or run tasks against multiple agents without changing client code. Implements a provider adapter pattern where each agent's API (function calling, streaming, response format) is normalized into a common task execution model with capability negotiation.","intents":["I want to compare outputs from different agents (Claude vs Gemini) for the same task","I need to switch from one agent provider to another without rewriting my orchestration logic","I want to use the cheapest or fastest available agent for each task type"],"best_for":["teams evaluating multiple agent providers","cost-conscious developers optimizing for price/performance","organizations with multi-vendor cloud strategies"],"limitations":["Abstraction layer adds latency (~50-100ms per request) due to normalization overhead","Not all agent capabilities map cleanly across providers; some provider-specific features may be unavailable","Requires maintaining adapter code for each new provider integration"],"requires":["API keys for at least one supported provider","Configuration file mapping provider credentials","TypeScript or JavaScript runtime"],"input_types":["code snippets","natural language instructions","structured task objects"],"output_types":["normalized agent responses","provider metadata","execution metrics"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getpaseo--paseo__cap_3","uri":"capability://tool.use.integration.streaming.agent.execution.with.real.time.feedback","name":"streaming-agent-execution-with-real-time-feedback","description":"Streams agent execution results in real-time using Server-Sent Events (SSE) or WebSocket, allowing clients to receive partial results, intermediate steps, and progress updates as the agent executes rather than waiting for completion. Implements a streaming response handler that buffers and forwards agent output chunks to connected clients with minimal latency.","intents":["I want to see code being generated line-by-line as the agent writes it","I need real-time feedback on long-running agent tasks to know they're still working","I want to cancel or interrupt an agent task mid-execution based on intermediate results"],"best_for":["interactive development workflows where immediate feedback is critical","mobile clients with limited bandwidth wanting to show progress early","teams building agent-driven UIs that need responsive feedback"],"limitations":["Streaming adds complexity to error handling; partial results may be incomplete if connection drops","Not all agent providers support streaming responses; fallback to polling may be required","Client must handle out-of-order or duplicate chunks in unreliable network conditions"],"requires":["HTTP/1.1 or HTTP/2 with SSE support, or WebSocket capability","Agent provider with streaming API support (Claude, Gemini support this)","Client-side streaming response handler"],"input_types":["code generation tasks","multi-step agent workflows"],"output_types":["streamed text chunks","progress metadata","intermediate execution state"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getpaseo--paseo__cap_4","uri":"capability://memory.knowledge.codebase.context.injection.for.agents","name":"codebase-context-injection-for-agents","description":"Automatically injects local codebase context (file structure, relevant code snippets, dependencies) into agent prompts before execution, enabling agents to generate code that's aware of existing patterns, APIs, and project structure. Implements a context extraction pipeline that parses the local codebase, identifies relevant files based on task description, and formats them for inclusion in the agent's input context window.","intents":["I want the agent to understand my project structure and generate code that follows my existing patterns","I need the agent to reference specific functions or classes from my codebase when generating new code","I want to avoid the agent generating code that conflicts with or duplicates existing functionality"],"best_for":["developers working on large codebases where context is critical","teams with established code patterns and conventions","projects where agent-generated code must integrate seamlessly with existing code"],"limitations":["Context extraction is language-specific; not all languages have equally good AST parsing support","Large codebases may exceed agent context window limits; requires intelligent file selection","Injecting too much context can reduce agent reasoning quality; requires tuning context size"],"requires":["Local filesystem access to codebase","Language-specific parser or AST library (tree-sitter, Babel, etc.)","Configuration specifying which files/directories to include"],"input_types":["local file paths","directory structures","dependency manifests"],"output_types":["augmented agent prompts","context metadata","file relevance scores"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getpaseo--paseo__cap_5","uri":"capability://automation.workflow.agent.task.history.and.audit.logging","name":"agent-task-history-and-audit-logging","description":"Maintains a persistent log of all agent task executions with full input/output history, execution metadata (duration, provider, cost), and audit trails for compliance. Stores task records in a queryable database with support for filtering, searching, and replaying past executions, enabling debugging and accountability.","intents":["I need to audit which agent tasks were run and by whom for compliance reasons","I want to debug a failed agent task by reviewing its exact input and output","I need to track agent usage costs and optimize spending across providers"],"best_for":["enterprises with compliance requirements (SOC2, HIPAA)","teams managing shared agent infrastructure","organizations tracking AI spending and ROI"],"limitations":["Storing full task history can consume significant disk space for high-volume usage","Querying large task histories may be slow without proper indexing","Sensitive data in task inputs/outputs requires encryption at rest"],"requires":["Persistent storage backend (PostgreSQL, MongoDB, etc.)","Logging middleware in orchestration layer","Query interface (REST API or CLI)"],"input_types":["task execution events","agent responses","execution metadata"],"output_types":["task records","audit logs","usage analytics","cost reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getpaseo--paseo__cap_6","uri":"capability://automation.workflow.agent.task.templating.and.reuse","name":"agent-task-templating-and-reuse","description":"Allows users to define reusable task templates with parameterized instructions, variable substitution, and conditional logic, enabling rapid task creation without rewriting orchestration code. Templates are stored as YAML/JSON definitions with support for Handlebars or similar templating syntax for dynamic content generation.","intents":["I want to create a template for common agent tasks (e.g., 'add unit tests to this file') and reuse it across projects","I need to parameterize agent instructions so I can run the same task with different inputs","I want to define complex multi-step agent workflows as reusable templates"],"best_for":["teams running repetitive agent tasks","organizations standardizing on agent-driven development practices","developers building agent-powered automation platforms"],"limitations":["Template syntax adds learning curve; requires documentation and examples","Complex conditional logic in templates can become hard to maintain","Template versioning and migration can be challenging as templates evolve"],"requires":["Template storage (filesystem, database, or Git)","Templating engine (Handlebars, Jinja2, etc.)","Template validation and schema definition"],"input_types":["template definitions (YAML/JSON)","parameter values","context objects"],"output_types":["rendered task definitions","agent instructions","execution plans"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getpaseo--paseo__cap_7","uri":"capability://code.generation.editing.agent.output.validation.and.schema.enforcement","name":"agent-output-validation-and-schema-enforcement","description":"Validates agent-generated code and outputs against user-defined schemas, linters, and quality gates before accepting results. Implements a post-processing pipeline that runs static analysis, type checking, and custom validation rules on agent output, with automatic rejection or correction of non-conforming results.","intents":["I want to ensure agent-generated code passes my project's linter and style checks","I need to validate that generated code has proper type annotations","I want to reject agent outputs that don't meet my quality standards without manual review"],"best_for":["teams with strict code quality standards","projects requiring type safety or linting compliance","organizations automating code generation at scale"],"limitations":["Validation rules are language-specific; requires separate configuration per language","Overly strict validation may reject valid but unconventional code","Validation adds latency (~100-500ms) to agent task completion"],"requires":["Linter/type-checker binaries (ESLint, TypeScript, Pylint, etc.)","Schema definitions for expected output format","Custom validation rule definitions"],"input_types":["agent-generated code","validation schemas","linter configurations"],"output_types":["validation results","error reports","corrected code (if auto-fix enabled)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getpaseo--paseo__cap_8","uri":"capability://planning.reasoning.agent.cost.optimization.and.provider.selection","name":"agent-cost-optimization-and-provider-selection","description":"Automatically selects the most cost-effective agent provider for each task based on estimated complexity, latency requirements, and provider pricing. Implements a cost model that predicts task complexity from input size and selects providers that minimize cost while meeting performance SLAs.","intents":["I want to minimize my agent API spending by using cheaper providers for simple tasks","I need to route complex tasks to more capable (but expensive) agents while using cheaper alternatives for simple tasks","I want to understand the cost breakdown of my agent usage and optimize spending"],"best_for":["cost-conscious teams with high agent task volume","organizations using multiple agent providers","startups optimizing for unit economics"],"limitations":["Cost model requires historical data to train; initial predictions may be inaccurate","Provider pricing changes require model retraining","Some tasks may have provider-specific requirements that override cost optimization"],"requires":["Historical task execution data","Provider pricing information","Task complexity estimation model"],"input_types":["task descriptions","input size metrics","latency requirements"],"output_types":["provider recommendations","cost estimates","optimization reports"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getpaseo--paseo__cap_9","uri":"capability://automation.workflow.agent.error.recovery.and.retry.logic","name":"agent-error-recovery-and-retry-logic","description":"Implements automatic retry logic with exponential backoff, fallback providers, and error recovery strategies for failed agent tasks. Detects transient failures (rate limits, timeouts) vs permanent failures (invalid input, unsupported task) and applies appropriate recovery strategies.","intents":["I want agent tasks to automatically retry on transient failures without manual intervention","I need to fall back to an alternative provider if the primary provider fails","I want detailed error reports to understand why agent tasks failed"],"best_for":["production systems requiring high reliability","teams running agent tasks at scale","applications where agent task failure is costly"],"limitations":["Retry logic can mask underlying issues; requires monitoring to detect systematic failures","Exponential backoff may delay task completion for flaky services","Fallback providers may produce different results, requiring output normalization"],"requires":["Retry configuration (max attempts, backoff strategy)","Fallback provider list","Error classification logic"],"input_types":["failed task definitions","error messages","execution context"],"output_types":["retry results","error reports","fallback provider results"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ (TypeScript runtime)","API credentials for at least one supported agent provider","Network access to remote agent services","iOS 13+ or Android 8+","Network connectivity (WiFi or cellular)","Account credentials for orchestration backend","Task queue backend (Redis, RabbitMQ, or database)","Scheduler service (cron-like or job scheduler)","Monitoring and alerting for scheduled tasks","Multiple agent provider credentials"],"failure_modes":["Requires network connectivity to remote agent services; no offline execution mode","Agent response latency depends on remote service availability and queue depth","Limited to agents with public APIs or supported integrations (Claude, Gemini, Copilot)","Mobile UI may not support complex code review workflows; better for task initiation than detailed inspection","Network latency on mobile networks can delay agent response feedback","Touch-based interaction limits ability to write complex multi-line code instructions","Scheduling adds complexity to debugging; scheduled task failures may go unnoticed","Batch execution requires careful error handling to prevent cascading failures","Task queue persistence requires external storage (Redis, database)","Multi-agent workflows add latency due to sequential execution and inter-agent communication","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5902768383852005,"quality":0.34,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:39.623Z","last_commit":"2026-05-03T13:50:33Z"},"community":{"stars":5271,"forks":460,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=getpaseo--paseo","compare_url":"https://unfragile.ai/compare?artifact=getpaseo--paseo"}},"signature":"vnvKXs2fGBhef05fjaQ3/Gt8GWSvZa+hhHhohhRbkdqnX6TVsiXA0ZwtDYVOliHMTDp3ubbXjjWOlGPmOXcVCQ==","signedAt":"2026-06-20T20:00:30.612Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/getpaseo--paseo","artifact":"https://unfragile.ai/getpaseo--paseo","verify":"https://unfragile.ai/api/v1/verify?slug=getpaseo--paseo","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"}}