{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-a5c-ai--babysitter","slug":"a5c-ai--babysitter","name":"babysitter","type":"agent","url":"https://a5c.ai","page_url":"https://unfragile.ai/a5c-ai--babysitter","categories":["ai-agents"],"tags":["agent-orchestration","agent-skills","agentic-ai","agentic-workflow","ai-agents","ai-automation","babysitter","claude-code","claude-code-skills","claude-skills","codex-plugin","codex-skills","trustworthy-ai","vibe-coding"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-a5c-ai--babysitter__cap_0","uri":"capability://automation.workflow.event.sourced.deterministic.orchestration.with.immutable.journal","name":"event-sourced deterministic orchestration with immutable journal","description":"Babysitter implements event sourcing to record every orchestration decision, task execution, and state transition in an immutable journal, enabling deterministic replay where identical inputs always produce identical outputs. The system appends events via a5c_append_event.py orchestrator script and reconstructs workflow state by replaying the event log, eliminating non-determinism from LLM-based decision-making. This architecture guarantees reproducibility across sessions and enables forensic analysis of agent behavior.","intents":["I need to debug why an agent made a specific decision by replaying the exact sequence of events that led to it","I want to ensure that running the same workflow twice with the same inputs produces identical results, not random variations","I need an audit trail of every step an agent took for compliance and accountability purposes","I want to resume a workflow mid-execution without losing context or repeating completed work"],"best_for":["teams building production AI agents that require deterministic behavior and auditability","developers implementing test-driven development workflows with AI harnesses","organizations with compliance requirements for AI decision logging"],"limitations":["Event log grows linearly with workflow complexity; no built-in log compaction or archival strategy documented","Determinism only applies to orchestration layer—underlying LLM outputs may still vary if temperature/seed not controlled","Journal replay adds latency proportional to event count; no incremental state snapshots mentioned"],"requires":["Claude Code or compatible AI harness with plugin support","Node.js 18+ for SDK execution","Writable filesystem for journal storage in run directory"],"input_types":["workflow definitions (code)","task specifications (JSON/structured)","LLM responses (text)"],"output_types":["immutable event log (JSON events)","reconstructed workflow state","execution trace for debugging"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_1","uri":"capability://planning.reasoning.quality.convergence.with.iterative.refinement.loops","name":"quality convergence with iterative refinement loops","description":"Babysitter implements a quality convergence system that automatically iterates on task outputs until they meet defined quality gates before allowing workflow progression. The system evaluates outputs against quality criteria, triggers refinement loops when gates fail, and tracks convergence metrics across iterations. This is integrated into the orchestration loop via quality-gate evaluation hooks that block advancement until thresholds are met, enabling self-improving agentic workflows without manual intervention.","intents":["I want my agent to automatically retry and improve code generation until it passes my test suite","I need to enforce quality standards (e.g., code coverage, performance benchmarks) before accepting agent outputs","I want to track how many iterations it took for an agent to produce acceptable work and optimize the process","I need to prevent hallucinated or low-quality outputs from propagating downstream in my workflow"],"best_for":["teams using test-driven development with AI agents","organizations requiring quality gates before production deployment","developers building self-improving agent workflows"],"limitations":["Quality gate definitions must be manually specified; no automatic quality metric inference","Convergence loops can be expensive if quality criteria are too strict—no built-in cost optimization or max-iteration caps documented","Quality metrics are task-specific; no cross-task quality aggregation or holistic workflow quality scoring"],"requires":["Defined quality gate criteria (code, configuration, or hooks)","Evaluation logic that can assess outputs against criteria","Claude Code or compatible harness with hook system support"],"input_types":["task output (code, text, structured data)","quality criteria (rules, test suites, scoring functions)","evaluation results (pass/fail, scores)"],"output_types":["refined task output meeting quality gates","convergence metrics (iteration count, quality scores)","refinement history"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_10","uri":"capability://automation.workflow.cli.and.programmatic.orchestration.with.headless.execution.support","name":"cli and programmatic orchestration with headless execution support","description":"Babysitter provides both a CLI interface and a programmatic SDK for orchestrating workflows, enabling both interactive development and headless execution in CI/CD pipelines. The CLI supports commands for running workflows, inspecting run directories, and managing processes, while the SDK provides a Node.js API for embedding Babysitter in applications. The system supports headless execution via an internal harness that doesn't require an IDE, enabling workflows to run in automated environments. Both CLI and SDK maintain the same orchestration semantics (determinism, event sourcing, quality convergence).","intents":["I want to run Babysitter workflows from the command line for CI/CD integration","I need to embed Babysitter orchestration in my Node.js application","I want to execute workflows in headless environments without an IDE","I need to programmatically control workflow execution, pause, and resumption"],"best_for":["teams integrating Babysitter into CI/CD pipelines","developers embedding Babysitter in Node.js applications","organizations running workflows in headless or containerized environments"],"limitations":["CLI reference is documented but command details are sparse; unclear what options and flags are available","SDK API is referenced but not fully documented; unclear what methods and classes are available","Headless execution via internal harness is mentioned but implementation details are not provided"],"requires":["Node.js 18+ for CLI and SDK","Process definitions for workflows to execute","API key for Claude or other LLM provider if using external harness"],"input_types":["CLI arguments and flags","SDK method calls with parameters","process definitions and configurations"],"output_types":["CLI output (logs, results, status)","SDK return values (execution results, state)","run directory with execution artifacts"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_11","uri":"capability://automation.workflow.observer.dashboard.with.real.time.workflow.visualization.and.monitoring","name":"observer dashboard with real-time workflow visualization and monitoring","description":"Babysitter includes an Observer Dashboard component that provides real-time visualization of workflow execution, task progress, quality metrics, and orchestration state. The dashboard connects to running workflows and displays live updates of task execution, quality convergence iterations, and human-in-the-loop breakpoints. It enables monitoring of multiple concurrent workflows and provides drill-down capabilities to inspect individual task execution details. The dashboard integrates with the run directory and event journal to provide accurate, up-to-date execution visibility.","intents":["I want to monitor the real-time progress of my workflow execution","I need to see quality convergence iterations and understand why a task is being refined","I want to respond to human-in-the-loop breakpoints from a visual interface","I need to monitor multiple concurrent workflows and identify bottlenecks or failures"],"best_for":["teams running long-duration workflows that need real-time visibility","developers debugging complex orchestration issues","organizations monitoring production agent deployments"],"limitations":["Observer Dashboard implementation details are not documented; unclear what visualization capabilities are provided","No information on dashboard scalability or support for monitoring large numbers of concurrent workflows","Integration with external monitoring systems (Prometheus, Datadog, etc.) is not documented"],"requires":["Running Babysitter workflow with event journal accessible","Web browser for dashboard access","Network connectivity to dashboard service"],"input_types":["event journal from running workflow","real-time execution updates","workflow metadata and configuration"],"output_types":["visual workflow execution timeline","task progress indicators","quality metrics and convergence graphs","breakpoint notifications and approval interface"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_12","uri":"capability://tool.use.integration.mcp.server.integration.for.standardized.tool.protocol.support","name":"mcp server integration for standardized tool protocol support","description":"Babysitter includes an MCP (Model Context Protocol) server component that exposes Babysitter capabilities through the standardized MCP protocol, enabling integration with any MCP-compatible client. The MCP server allows external tools and applications to invoke Babysitter workflows, query execution state, and receive notifications about workflow progress. This enables Babysitter to be used as a backend service for orchestration, with clients communicating via the standard MCP protocol rather than direct SDK calls.","intents":["I want to invoke Babysitter workflows from MCP-compatible clients","I need to integrate Babysitter with other tools that support the MCP protocol","I want to expose Babysitter as a service that multiple clients can interact with","I need to query workflow execution state and receive notifications via MCP"],"best_for":["teams building MCP-compatible tools that need orchestration capabilities","organizations standardizing on MCP for tool integration","developers building multi-tool workflows that include Babysitter"],"limitations":["MCP server implementation details are not documented; unclear what MCP resources and tools are exposed","No information on MCP server deployment, scaling, or high-availability setup","Integration with MCP clients is not documented; unclear how to configure clients to use Babysitter MCP server"],"requires":["MCP-compatible client","Running Babysitter MCP server","Network connectivity between client and server"],"input_types":["MCP protocol messages","workflow invocation requests","state query requests"],"output_types":["MCP protocol responses","workflow execution results","execution state and notifications"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_13","uri":"capability://automation.workflow.task.types.reference.with.standardized.task.definitions","name":"task types reference with standardized task definitions","description":"Babysitter provides a comprehensive task types reference that defines the standard task types supported by the orchestration system (e.g., code generation, testing, refinement, approval). Each task type has a standardized definition including inputs, outputs, quality criteria, and orchestration behavior. Task types are composable and can be extended with custom implementations. The task types reference serves as the contract between orchestration logic and task implementations, ensuring consistency across workflows.","intents":["I want to understand what task types are available and how to use them in my workflows","I need to define custom task types that fit my domain-specific requirements","I want to ensure that my tasks conform to the standard task type contract","I need to understand the inputs, outputs, and quality criteria for each task type"],"best_for":["developers building workflows using Babysitter task types","teams defining custom task types for domain-specific workflows","organizations standardizing on task type definitions across teams"],"limitations":["Task types reference is documented but details on each task type are sparse","Custom task type extension mechanism is not fully documented","No information on task type versioning or backward compatibility"],"requires":["Understanding of task type contract and interface","Process definitions that use task types","Task implementations that conform to task type definitions"],"input_types":["task type definitions","task inputs and parameters","quality criteria specifications"],"output_types":["task execution results","quality evaluation results","task metadata and execution trace"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_14","uri":"capability://safety.moderation.security.best.practices.and.multi.harness.isolation","name":"security best practices and multi-harness isolation","description":"Babysitter implements security best practices for agentic workflows including multi-harness isolation, credential management, and sandboxing of task execution. The system supports running workflows in isolated harness instances to prevent cross-workflow interference, manages credentials securely without exposing them in logs or event journals, and provides guidance on secure deployment patterns. Security considerations are integrated into the orchestration architecture rather than added as an afterthought.","intents":["I want to run multiple workflows in isolated harness instances to prevent interference","I need to manage API keys and credentials securely without exposing them in logs","I want to ensure that my workflows don't have unintended access to other workflows' state","I need to follow security best practices for deploying agents in production"],"best_for":["teams deploying agents in production with security requirements","organizations running multi-tenant workflows with isolation requirements","developers building secure agent systems with credential management"],"limitations":["Security best practices are documented but implementation details are sparse","No built-in credential management system; relies on external secret stores","Harness isolation mechanism is not fully documented; unclear how isolation is enforced"],"requires":["External secret management system (e.g., environment variables, secret vaults)","Isolated harness instances for multi-tenant deployments","Understanding of security best practices for agent systems"],"input_types":["workflow definitions with security requirements","credential specifications","isolation configuration"],"output_types":["securely executed workflows","audit logs without exposed credentials","isolation enforcement confirmation"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_2","uri":"capability://automation.workflow.human.in.the.loop.breakpoints.with.approval.gates","name":"human-in-the-loop breakpoints with approval gates","description":"Babysitter provides a breakpoint system that pauses workflow execution at critical decision points and requires explicit human approval before progression. The system integrates with the stop-hook mechanism (babysitter-stop-hook.sh) to halt execution, surface decision context to a human reviewer, and resume only after approval is granted. This is implemented as a special hook type in the lifecycle system that blocks the orchestration loop until human signal is received, enabling safe deployment of agentic workflows in production environments.","intents":["I need to review and approve major decisions (e.g., code deployment, data deletion) before my agent executes them","I want to inject human judgment at specific workflow stages without stopping the entire process","I need to prevent autonomous agents from making irreversible changes without oversight","I want to collect human feedback to improve agent decision-making in future runs"],"best_for":["production deployments requiring human oversight of agent actions","teams with compliance or safety requirements for autonomous systems","developers building high-stakes workflows (financial, infrastructure, data operations)"],"limitations":["Breakpoint handling is synchronous—workflow blocks until human responds, no timeout mechanism documented","No built-in UI for approval; requires integration with external approval systems or manual CLI interaction","Approval context must be explicitly passed to breakpoint; no automatic context injection for decision transparency"],"requires":["Human reviewer availability to respond to breakpoints","Integration with approval mechanism (CLI, API, or external service)","Claude Code plugin or custom harness with stop-hook support"],"input_types":["workflow state at breakpoint","decision context (proposed action, reasoning)","human approval signal (yes/no/modify)"],"output_types":["approval decision (approved/rejected/modified)","human feedback or modifications","workflow resumption signal"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_3","uri":"capability://tool.use.integration.multi.harness.adapter.system.with.plugin.marketplace","name":"multi-harness adapter system with plugin marketplace","description":"Babysitter provides a multi-harness adapter architecture that abstracts away differences between Claude Code, Cursor, and other AI harnesses through a unified SDK interface. The system discovers available harnesses, routes orchestration commands to the appropriate adapter, and manages harness-specific lifecycle hooks. A plugin marketplace system (referenced in .claude-plugin/marketplace.json and .cursor-plugin/marketplace.json) enables distribution of Babysitter as a plugin across multiple IDE and harness ecosystems, with each adapter implementing the same core orchestration contract.","intents":["I want to run the same workflow across Claude Code, Cursor, and other AI harnesses without rewriting orchestration logic","I need to distribute my Babysitter workflows as plugins that work in multiple IDE environments","I want to abstract away harness-specific details so my orchestration code is portable","I need to support teams using different AI harnesses without maintaining separate workflow definitions"],"best_for":["teams using multiple AI harnesses (Claude Code, Cursor, etc.)","plugin developers building harness-agnostic orchestration tools","organizations standardizing on Babysitter across heterogeneous AI tooling"],"limitations":["Adapter coverage depends on harness popularity; less common harnesses may lack adapters","Harness-specific features may not be fully exposed through the unified interface—lowest-common-denominator abstraction","Plugin marketplace system is documented but implementation details are sparse; unclear how plugin discovery and versioning work"],"requires":["Compatible AI harness (Claude Code, Cursor, or custom harness with adapter)","Node.js 18+ for SDK","Plugin manifest (plugin.json) for marketplace distribution"],"input_types":["harness detection signals","orchestration commands (harness-agnostic)","plugin manifests"],"output_types":["harness-specific execution results","adapter routing decisions","plugin marketplace metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_4","uri":"capability://memory.knowledge.skill.discovery.and.context.injection.for.dynamic.capability.loading","name":"skill discovery and context injection for dynamic capability loading","description":"Babysitter implements a skill discovery system that dynamically identifies available skills and processes at runtime, then injects them into the agent's execution context via the Context API. Skills are packaged as reusable process definitions that agents can invoke, and the discovery mechanism scans the process library to populate available capabilities. This enables agents to self-discover what they can do without hardcoded skill lists, and allows workflows to be extended with new skills without modifying orchestration code.","intents":["I want my agent to automatically discover what skills and processes are available without hardcoding a skill list","I need to add new capabilities to my agent by simply adding new skill definitions to a library","I want to inject context about available skills into the agent's prompt so it knows what it can do","I need to support dynamic skill loading where new skills become available without restarting the workflow"],"best_for":["teams building extensible agent systems with pluggable skills","developers creating skill libraries that agents can discover and use","organizations wanting to decouple skill definitions from orchestration logic"],"limitations":["Skill discovery is static at workflow start; no runtime skill registration or hot-loading documented","Context injection adds overhead proportional to skill library size—no pagination or lazy-loading of skill metadata","Skill discovery mechanism is not fully documented; unclear how it identifies and catalogs available skills"],"requires":["Process library with skill definitions","Context API integration in orchestration loop","Skill metadata in standardized format"],"input_types":["process library (skill definitions)","skill metadata (name, description, parameters)","execution context"],"output_types":["discovered skills list","injected context for agent","skill invocation results"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_5","uri":"capability://automation.workflow.session.resumption.with.stop.hook.mechanism.and.state.reconstruction","name":"session resumption with stop-hook mechanism and state reconstruction","description":"Babysitter enables workflows to be paused and resumed across sessions using the stop-hook mechanism, which gracefully halts execution and preserves all state in the run directory. When a workflow is resumed, the orchestration loop replays the event journal to reconstruct the exact state at the pause point, then continues execution from that point without data loss or re-execution of completed work. This is implemented via the babysitter-stop-hook.sh script and the event sourcing architecture, enabling long-running workflows to survive interruptions.","intents":["I want to pause a long-running workflow and resume it later without losing progress or context","I need to handle interruptions (e.g., IDE crashes, network failures) without restarting the entire workflow","I want to continue a workflow on a different machine or in a different session","I need to ensure that resuming a workflow doesn't re-execute already-completed tasks"],"best_for":["teams running long-duration workflows that may be interrupted","developers building resilient agent systems with fault tolerance","organizations needing to migrate workflows between environments"],"limitations":["Resumption requires the run directory to be preserved and accessible; no cloud-based state synchronization documented","Stop-hook mechanism is synchronous; no graceful shutdown timeout or force-kill handling documented","Resumption state is tied to the specific harness instance; unclear how to resume in a different harness or environment"],"requires":["Preserved run directory with event journal and state files","Stop-hook script support in harness","Ability to replay event journal on resumption"],"input_types":["pause signal (manual or automatic)","run directory with preserved state","event journal"],"output_types":["graceful pause confirmation","reconstructed workflow state","resumption signal and continuation"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_6","uri":"capability://automation.workflow.lifecycle.hooks.system.with.custom.orchestrator.support","name":"lifecycle hooks system with custom orchestrator support","description":"Babysitter provides a comprehensive hook system that allows custom code to execute at specific lifecycle points in the orchestration loop (e.g., before task execution, after quality evaluation, on workflow completion). The system supports both native orchestrator hooks and custom orchestrators that implement the entire orchestration strategy. Hooks are registered via configuration and executed at defined points in the orchestration state machine, enabling extensibility without modifying core orchestration logic. The hook system integrates with the event sourcing architecture to ensure hooks are deterministic and replay-safe.","intents":["I want to inject custom logic at specific points in the orchestration loop (e.g., logging, metrics collection)","I need to implement a custom orchestration strategy that differs from the default behavior","I want to trigger side effects (e.g., notifications, API calls) at specific workflow stages","I need to validate or transform data between orchestration steps"],"best_for":["developers building custom orchestration strategies on top of Babysitter","teams needing to integrate Babysitter with external systems (monitoring, notifications, approval services)","organizations with domain-specific orchestration requirements"],"limitations":["Hook execution is synchronous; no async hook support or timeout handling documented","Custom orchestrators must implement the full orchestration contract; no partial override mechanism","Hook ordering and dependencies are not documented; unclear how to manage complex hook interactions"],"requires":["Hook implementation code (JavaScript/TypeScript)","Hook registration in configuration","Understanding of orchestration lifecycle and state machine"],"input_types":["orchestration state at hook point","task context and results","workflow metadata"],"output_types":["hook execution results","modified orchestration state (if applicable)","side effects (logs, API calls, etc.)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_7","uri":"capability://automation.workflow.process.composition.and.reuse.with.modular.workflow.definitions","name":"process composition and reuse with modular workflow definitions","description":"Babysitter enables workflows to be defined as composable processes that can be reused, nested, and packaged as distributable units. Processes are defined in code with a standardized structure, can invoke other processes, and can be packaged for distribution via the plugin marketplace. The system supports process composition patterns (sequential, parallel, conditional) and maintains determinism across composed workflows through the event sourcing architecture. Process definitions are stored in the process library and can be discovered and invoked dynamically.","intents":["I want to break my complex workflow into reusable sub-processes that can be composed together","I need to share workflow definitions across teams or projects without duplicating code","I want to package my workflows as distributable plugins that others can use","I need to support conditional and parallel execution patterns within my workflows"],"best_for":["teams building complex multi-step workflows with reusable components","organizations creating workflow libraries for internal or external distribution","developers implementing workflow composition patterns (DAGs, pipelines)"],"limitations":["Process composition patterns are documented but implementation details are sparse; unclear how parallel execution is coordinated","No built-in process versioning or dependency management; unclear how to handle breaking changes in reused processes","Process library organization and naming conventions are not fully documented"],"requires":["Process definitions in standardized format","Process library for storing and discovering processes","Support for process invocation in orchestration loop"],"input_types":["process definitions (code)","process parameters and inputs","composition specifications"],"output_types":["composed workflow execution results","packaged process distributions","process library metadata"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_8","uri":"capability://automation.workflow.parallel.execution.patterns.with.deterministic.coordination","name":"parallel execution patterns with deterministic coordination","description":"Babysitter supports parallel execution of tasks and processes while maintaining determinism through coordinated event sourcing. The system can execute multiple tasks concurrently, coordinate their results, and ensure that the same parallel execution always produces the same outcome. Parallel patterns are defined in process compositions and coordinated through the orchestration loop, with results aggregated deterministically. This enables efficient execution of independent tasks while preserving the deterministic guarantees of the event sourcing architecture.","intents":["I want to execute multiple independent tasks in parallel to improve workflow throughput","I need to coordinate results from parallel tasks and aggregate them deterministically","I want to ensure that parallel execution always produces the same results, not random variations","I need to handle failures in parallel tasks without losing determinism"],"best_for":["teams building high-throughput workflows with independent parallel tasks","developers implementing fan-out/fan-in patterns in agent orchestration","organizations needing deterministic parallel execution for reproducibility"],"limitations":["Parallel execution coordination details are not fully documented; unclear how task ordering and result aggregation work","No built-in load balancing or resource allocation for parallel tasks","Failure handling in parallel tasks is not documented; unclear how partial failures are handled"],"requires":["Process definitions supporting parallel composition","Orchestration loop with parallel task coordination","Event sourcing for deterministic result aggregation"],"input_types":["parallel task definitions","task inputs and parameters","coordination specifications"],"output_types":["parallel task results","aggregated results","execution trace with parallel ordering"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-a5c-ai--babysitter__cap_9","uri":"capability://automation.workflow.run.directory.structure.with.organized.state.and.artifact.management","name":"run directory structure with organized state and artifact management","description":"Babysitter organizes all workflow state, artifacts, and metadata in a structured run directory that serves as the single source of truth for a workflow execution. The run directory contains the event journal, task outputs, quality metrics, and execution traces, all organized in a predictable structure. This enables easy inspection of workflow execution, debugging of specific tasks, and archival of complete execution records. The run directory structure is designed to be human-readable and machine-parseable, supporting both manual inspection and programmatic access.","intents":["I want to inspect the complete execution history of a workflow including all intermediate outputs","I need to debug a specific task by examining its inputs, outputs, and execution context","I want to archive complete workflow executions for compliance or analysis purposes","I need to programmatically access workflow artifacts and metadata for integration with external systems"],"best_for":["developers debugging complex workflows and needing detailed execution visibility","teams with compliance requirements for workflow execution records","organizations building workflow analytics and monitoring systems"],"limitations":["Run directory structure is not fully documented; unclear what files and directories are created","No built-in run directory cleanup or archival; storage management is manual","Run directory is local to the execution environment; no cloud-based storage integration documented"],"requires":["Writable filesystem for run directory","Understanding of run directory structure for programmatic access","Tools for inspecting and analyzing run directory contents"],"input_types":["workflow execution state","task outputs and artifacts","execution metadata"],"output_types":["organized run directory structure","queryable execution records","archivable execution artifacts"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Claude Code or compatible AI harness with plugin support","Node.js 18+ for SDK execution","Writable filesystem for journal storage in run directory","Defined quality gate criteria (code, configuration, or hooks)","Evaluation logic that can assess outputs against criteria","Claude Code or compatible harness with hook system support","Node.js 18+ for CLI and SDK","Process definitions for workflows to execute","API key for Claude or other LLM provider if using external harness","Running Babysitter workflow with event journal accessible"],"failure_modes":["Event log grows linearly with workflow complexity; no built-in log compaction or archival strategy documented","Determinism only applies to orchestration layer—underlying LLM outputs may still vary if temperature/seed not controlled","Journal replay adds latency proportional to event count; no incremental state snapshots mentioned","Quality gate definitions must be manually specified; no automatic quality metric inference","Convergence loops can be expensive if quality criteria are too strict—no built-in cost optimization or max-iteration caps documented","Quality metrics are task-specific; no cross-task quality aggregation or holistic workflow quality scoring","CLI reference is documented but command details are sparse; unclear what options and flags are available","SDK API is referenced but not fully documented; unclear what methods and classes are available","Headless execution via internal harness is mentioned but implementation details are not provided","Observer Dashboard implementation details are not documented; unclear what visualization capabilities are provided","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.37550172106384616,"quality":0.5,"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":"2026-05-03T13:59:57.743Z","last_commit":"2026-05-03T13:21:45Z"},"community":{"stars":737,"forks":45,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=a5c-ai--babysitter","compare_url":"https://unfragile.ai/compare?artifact=a5c-ai--babysitter"}},"signature":"oYvx1aF75AYJdtP9zuSF89djR1B3BHOkD1rVt31oMlIoNFH/iI/9RD0PqOrLlPlJjcJExts4p/RydNwmTRmmAA==","signedAt":"2026-06-22T11:21:24.730Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/a5c-ai--babysitter","artifact":"https://unfragile.ai/a5c-ai--babysitter","verify":"https://unfragile.ai/api/v1/verify?slug=a5c-ai--babysitter","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"}}