{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-stakpak--agent","slug":"stakpak--agent","name":"agent","type":"agent","url":"https://stakpak.dev","page_url":"https://unfragile.ai/stakpak--agent","categories":["ai-agents"],"tags":["agent","ai-agent","autonomous-agent","devops","devops-agents","devtool","generative-ai","hacktoberfest","infrastructure","llm-agent"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-stakpak--agent__cap_0","uri":"capability://automation.workflow.autonomous.agent.execution.with.mcp.tool.orchestration","name":"autonomous-agent-execution-with-mcp-tool-orchestration","description":"Executes DevOps tasks autonomously by routing LLM decisions through a Model Context Protocol (MCP) system that dynamically loads and executes tools. The agent implements a 14-method AgentProvider trait abstraction with two backends: RemoteClient for cloud-hosted inference and LocalClient for offline operation. Tool execution flows through a container system that validates schemas, manages permissions, and handles SSH-based remote operations on target machines.","intents":["I want an AI agent to autonomously handle infrastructure tasks like deployments, health checks, and remediation without manual intervention","I need the agent to execute tools securely on remote machines via SSH with proper secret redaction and audit trails","I want to switch between cloud-based and local LLM backends without changing my tool definitions"],"best_for":["DevOps teams running 24/7 infrastructure monitoring and auto-remediation","Organizations needing air-gapped or on-premise agent execution without cloud API calls","Teams building custom tool ecosystems with MCP-compatible integrations"],"limitations":["MCP tool execution adds latency per decision cycle — no built-in batching for high-frequency operations","LocalClient mode requires LLM inference capability on the host machine; no streaming inference optimization documented","SSH-based remote execution depends on pre-configured credentials and network connectivity; no built-in retry logic for transient failures","AgentProvider abstraction supports 14 async methods but no transaction semantics for multi-step operations"],"requires":["Rust 1.70+ (workspace uses tokio async runtime)","API key for RemoteClient (OpenAI, Anthropic, or compatible endpoint)","SSH access to target machines for remote tool execution","MCP-compatible tool definitions in JSON schema format"],"input_types":["natural language task descriptions","JSON-formatted MCP tool schemas","SSH connection strings and credentials","structured task context (environment variables, previous outputs)"],"output_types":["tool execution results (stdout/stderr)","structured task completion status","audit logs with secret redaction","agent decision traces for debugging"],"categories":["automation-workflow","tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_1","uri":"capability://automation.workflow.interactive.terminal.ui.with.event.driven.state.management","name":"interactive-terminal-ui-with-event-driven-state-management","description":"Provides a full-featured terminal user interface (TUI) built in Rust that runs as a subprocess spawned by the CLI with bidirectional event channels. The TUI implements a core event loop managing state transitions, user input handling (keyboard/mouse), and real-time rendering of agent messages and interactive components. State is managed through immutable snapshots with event-driven updates, enabling responsive interaction while the agent processes tasks asynchronously.","intents":["I want to monitor and interact with the agent in real-time through a terminal interface without context switching","I need to approve or modify agent actions before execution with interactive prompts and confirmations","I want to see agent reasoning, tool outputs, and task progress in a structured, scrollable format"],"best_for":["DevOps engineers running agents on local machines or jump hosts","Teams needing real-time visibility into agent decision-making and tool execution","Users preferring terminal-based workflows over web dashboards"],"limitations":["TUI rendering is single-threaded; high-frequency message updates (>100/sec) may cause frame drops","No built-in session persistence — closing the TUI loses interaction history unless explicitly saved","Terminal size changes require manual re-rendering; no automatic responsive layout adaptation","Interactive components (prompts, confirmations) block agent execution; no non-blocking approval workflows"],"requires":["Terminal emulator with ANSI color support (xterm-256color or equivalent)","Rust 1.70+ for TUI subprocess spawning","Unix-like OS (Linux, macOS) — Windows support via WSL2 only"],"input_types":["keyboard events (arrow keys, enter, escape, vim keybindings)","mouse events (click, scroll)","agent messages from event channels","user confirmations and text input"],"output_types":["rendered terminal UI with colors and formatting","agent message display with syntax highlighting","interactive prompts and input fields","scrollable message history and logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_10","uri":"capability://automation.workflow.configuration.management.with.profile.persistence","name":"configuration-management-with-profile-persistence","description":"Manages agent configuration through a TOML file at ~/.stakpak/config.toml that persists profiles, API keys, context sources, and execution settings. The configuration system supports multiple named profiles, enabling different agents to use different LLM backends and settings. Configuration is loaded at startup and can be reloaded without restarting the agent. The system provides a CLI subcommand for configuration management and validation.","intents":["I want to store API keys and LLM model preferences in a persistent configuration file without hardcoding them","I need to manage multiple profiles for different projects or environments (dev, staging, prod) with different settings","I want to validate my configuration before running agents to catch errors early"],"best_for":["DevOps teams managing multiple projects with different LLM requirements","Organizations needing environment-specific agent configurations","Teams wanting to version control agent settings (minus secrets) in git"],"limitations":["Configuration is file-based; no integration with external config management systems (Consul, etcd)","No built-in config validation schema; invalid configurations may not be caught until runtime","Profile selection is static per session; no dynamic profile switching based on environment variables","Secrets are stored in plaintext in config file; no built-in encryption at rest"],"requires":["TOML configuration file at ~/.stakpak/config.toml","Read/write permissions on config file","API keys for configured LLM providers"],"input_types":["TOML configuration file","profile definitions","API keys and model names"],"output_types":["parsed configuration objects","validation results","profile metadata"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_11","uri":"capability://automation.workflow.account.and.billing.information.viewer","name":"account-and-billing-information-viewer","description":"Provides a CLI subcommand that displays current account information, billing status, and usage metrics for the authenticated user. The system queries account metadata from the remote API (for RemoteClient mode) or displays local account information (for LocalClient mode). Account information includes subscription tier, API usage, and billing details.","intents":["I want to check my current subscription tier and billing status without logging into a web dashboard","I need to monitor API usage and understand my billing charges","I want to verify my account authentication status before running agents"],"best_for":["Individual developers and small teams using Stakpak with cloud-based LLM backends","Organizations needing to track API usage and billing from the CLI","Teams verifying account status as part of agent setup and troubleshooting"],"limitations":["Account viewer is read-only; no ability to update billing information or subscription tier from CLI","Billing information is only available for RemoteClient mode; LocalClient mode has limited account data","No real-time billing updates; usage metrics may be delayed by API sync intervals","No integration with external billing systems; billing data is Stakpak-specific"],"requires":["API key for RemoteClient authentication","Network connectivity to Stakpak API (for RemoteClient mode)"],"input_types":["authentication credentials (API key)"],"output_types":["account information (name, email, subscription tier)","billing details (charges, usage metrics)","authentication status"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_12","uri":"capability://tool.use.integration.agent.client.protocol.server.for.editor.integration","name":"agent-client-protocol-server-for-editor-integration","description":"Implements an Agent Client Protocol (ACP) server that enables editor integration (VS Code, Cursor, JetBrains) by exposing agent capabilities through a standardized protocol. The ACP server handles editor requests for agent execution, tool discovery, and result streaming. The system supports bidirectional communication between editors and the agent, enabling in-editor task execution and result display.","intents":["I want to run agent tasks directly from my editor without switching to a terminal","I need the agent to provide code suggestions and infrastructure recommendations within my IDE","I want to see agent execution results and logs streamed to my editor in real-time"],"best_for":["Developers using VS Code, Cursor, or JetBrains IDEs with Stakpak agent integration","Teams wanting to embed DevOps automation into their development workflows","Organizations building custom editor plugins that need agent integration"],"limitations":["ACP protocol support is limited to editors with ACP client implementations; legacy editors require adapters","Result streaming adds latency compared to direct CLI execution; no built-in buffering for high-frequency updates","Editor integration requires editor-specific plugins; no universal integration across all editors","No built-in editor state synchronization; agent context may be stale if editor state changes during execution"],"requires":["ACP-compatible editor (VS Code extension, Cursor plugin, JetBrains plugin)","Stakpak agent running with ACP server enabled","Network connectivity between editor and ACP server (localhost or remote)"],"input_types":["ACP protocol messages from editor","task descriptions and context from editor","editor state (open files, cursor position)"],"output_types":["ACP protocol responses with task results","streamed execution logs and messages","tool discovery and capability information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_2","uri":"capability://safety.moderation.dynamic.secret.redaction.and.privacy.mode","name":"dynamic-secret-redaction-and-privacy-mode","description":"Implements a secret substitution system that dynamically detects and redacts sensitive data (API keys, passwords, tokens) from agent outputs, logs, and user-facing messages before display or storage. Privacy mode can be enabled to further redact environment variables, file paths, and command arguments. The system uses pattern matching and configurable secret patterns to identify sensitive data across all message types, with audit logging that preserves redacted values in encrypted storage for compliance.","intents":["I want to ensure API keys and credentials never appear in logs, terminal output, or audit trails","I need to comply with data privacy regulations by redacting PII and sensitive infrastructure details from agent outputs","I want to share agent logs with team members without exposing production credentials or internal paths"],"best_for":["Organizations handling production infrastructure with strict security compliance requirements","Teams sharing agent logs across security boundaries (e.g., support, auditing)","DevOps teams running agents on shared machines where log files may be accessible to other users"],"limitations":["Pattern-based redaction may miss novel secret formats not in the pattern database; no ML-based anomaly detection for unknown secret types","Privacy mode redaction is coarse-grained (entire env vars, full paths); no fine-grained field-level redaction","Redacted values stored in encrypted storage require key management; no built-in key rotation or HSM integration","Redaction happens at display time, not at execution time; secrets may still be processed by the agent in memory"],"requires":["Configuration file with secret patterns (~/.stakpak/config.toml)","Encryption key for storing redacted values (managed via Warden guardrails system)","Rust 1.70+ for async secret substitution pipeline"],"input_types":["agent output strings","log messages","environment variable values","command arguments and responses"],"output_types":["redacted message strings with [REDACTED] placeholders","audit logs with encrypted secret references","privacy-compliant log exports"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_3","uri":"capability://memory.knowledge.context.injection.pipeline.with.session.profiles","name":"context-injection-pipeline-with-session-profiles","description":"Manages a context injection pipeline that enriches agent prompts with workspace-specific information (codebase structure, environment variables, git history, previous task outputs) before sending to the LLM. Session profiles stored in ~/.stakpak/config.toml define API keys, model selection, and context sources. The pipeline supports multiple profile selection, enabling different agents to use different LLM backends and context configurations for the same task.","intents":["I want the agent to have access to my codebase structure, git history, and environment without manually providing context each time","I need to run different agents with different LLM models (e.g., fast model for simple tasks, powerful model for complex reasoning) using profiles","I want to isolate agent configurations per project or environment (dev, staging, prod) using named profiles"],"best_for":["Teams managing multiple projects with different LLM requirements and API keys","DevOps engineers needing context-aware agents that understand their infrastructure topology","Organizations using multiple LLM providers (OpenAI, Anthropic, local) and wanting to switch without code changes"],"limitations":["Context injection happens at prompt time; large codebases (>100MB) may exceed token limits without chunking","No built-in caching of context snapshots; re-injecting context for repeated tasks causes redundant processing","Profile selection is static per session; no dynamic profile switching based on task type","Context sources are file-based (git, env, codebase); no integration with external knowledge bases or vector stores"],"requires":["Configuration file at ~/.stakpak/config.toml with profile definitions","API keys for selected LLM provider in config or environment variables","Git repository initialized in workspace (for git history context)","Read access to codebase and environment variables"],"input_types":["TOML configuration file with profile definitions","API keys and model names","workspace file system (codebase, git history)","environment variables"],"output_types":["enriched LLM prompts with injected context","profile metadata (model, API endpoint, context sources)","context snapshots for debugging"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_4","uri":"capability://tool.use.integration.mcp.server.and.proxy.modes.for.tool.distribution","name":"mcp-server-and-proxy-modes-for-tool-distribution","description":"Provides two MCP deployment modes: MCP server mode that exposes the agent's tool registry as a Model Context Protocol server for external clients (editors, IDEs, other agents), and MCP proxy mode that routes tool requests to an upstream MCP server with request/response transformation. Both modes use the same tool container and execution system, enabling tool reuse across different client types and deployment topologies.","intents":["I want to expose my agent's tools to VS Code, Cursor, or other MCP-compatible editors without building custom integrations","I need to proxy tool requests through the agent to an upstream MCP server with custom request transformation or filtering","I want to distribute my tool definitions across multiple MCP servers with a single agent managing execution"],"best_for":["Teams integrating DevOps agents into editor workflows (VS Code, Cursor, JetBrains)","Organizations with multiple MCP servers needing a unified tool interface","Developers building MCP-compatible clients that need access to agent tools"],"limitations":["MCP server mode requires clients to support MCP protocol; legacy tools and integrations need adapters","Proxy mode adds request/response transformation overhead; no built-in caching for repeated tool calls","Tool schema validation happens at server startup; schema changes require server restart","No built-in load balancing or failover for multiple upstream MCP servers"],"requires":["MCP protocol support in client (VS Code extension, Cursor, or custom client)","Rust 1.70+ for async MCP server implementation","Tool definitions in JSON schema format compatible with MCP spec","Network connectivity between MCP client and server (localhost or remote)"],"input_types":["MCP protocol messages (tool_call, tool_result)","JSON-formatted tool schemas","upstream MCP server endpoint (for proxy mode)"],"output_types":["MCP protocol responses with tool results","transformed tool outputs (for proxy mode)","tool execution logs and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_5","uri":"capability://tool.use.integration.local.tool.execution.with.schema.validation","name":"local-tool-execution-with-schema-validation","description":"Executes tools locally on the agent's host machine by validating tool calls against JSON schemas, resolving tool definitions from the MCP tool registry, and executing them through a containerized execution environment. Tools can be shell commands, scripts, or native binaries. The system validates input parameters against schemas before execution, captures stdout/stderr, and returns structured results to the agent. Execution happens synchronously with timeout protection.","intents":["I want the agent to execute shell commands and scripts on the local machine with schema validation to prevent malformed calls","I need to wrap existing tools (scripts, binaries) as MCP-compatible tools without modifying their source code","I want to ensure tool execution respects resource limits and timeouts to prevent runaway processes"],"best_for":["DevOps teams running agents on local machines or jump hosts with direct shell access","Organizations with existing shell scripts and tools that need to be exposed to agents","Teams needing synchronous tool execution with immediate feedback for interactive workflows"],"limitations":["Synchronous execution blocks agent reasoning until tool completes; no async tool execution for long-running operations","Timeout protection is fixed per tool; no dynamic timeout adjustment based on task complexity","Tool output is captured as text; no streaming output for long-running commands","No built-in sandboxing or resource limits (CPU, memory, disk); relies on OS-level process limits"],"requires":["Unix-like shell (bash, sh, zsh) for command execution","Tool definitions in JSON schema format with executable paths","Read/execute permissions on tool binaries and scripts","Rust 1.70+ for async execution environment"],"input_types":["JSON-formatted tool calls with parameters","tool schemas defining input/output types","shell commands and script paths"],"output_types":["tool execution results (stdout, stderr, exit code)","structured JSON responses matching tool schemas","execution metadata (duration, resource usage)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_6","uri":"capability://tool.use.integration.remote.ssh.based.tool.execution.with.credential.management","name":"remote-ssh-based-tool-execution-with-credential-management","description":"Executes tools on remote machines via SSH by establishing connections using credentials stored in the agent's configuration, executing commands on the remote host, and capturing results. The system manages SSH connection pooling, handles authentication (password, key-based), and integrates with the secret redaction system to prevent credential exposure. Remote operations are treated as first-class tools in the MCP system, enabling agents to manage distributed infrastructure.","intents":["I want the agent to execute deployment, health check, and remediation tasks on remote servers via SSH","I need to manage SSH credentials securely without exposing them in logs or configuration files","I want the agent to handle multi-host operations (e.g., rolling deployments) with coordinated SSH execution"],"best_for":["DevOps teams managing distributed infrastructure across multiple servers","Organizations needing agents to perform remote deployments, monitoring, and auto-remediation","Teams with existing SSH-based infrastructure and wanting to automate via agents"],"limitations":["SSH connection pooling is not documented; each remote operation may establish a new connection, adding latency","No built-in retry logic for transient SSH failures (network timeouts, connection resets)","Multi-host operations require sequential execution; no parallel SSH execution for concurrent operations","Credential management relies on local configuration; no integration with external secret stores (Vault, AWS Secrets Manager)"],"requires":["SSH access to target machines (port 22 or custom)","SSH credentials (password or private key) stored in ~/.stakpak/config.toml","Network connectivity from agent host to target machines","Remote shell (bash, sh) on target machines"],"input_types":["SSH connection strings (user@host:port)","remote commands and scripts","SSH credentials (password or key path)"],"output_types":["remote command output (stdout, stderr)","SSH connection status and errors","execution metadata (duration, host, command)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_7","uri":"capability://planning.reasoning.rulebook.management.for.organizational.sops","name":"rulebook-management-for-organizational-sops","description":"Provides a CRUD interface for managing rulebooks that encode organizational standard operating procedures (SOPs) and policies. Rulebooks are stored centrally and can be referenced by agents to guide decision-making and constrain actions. The system supports rulebook versioning, team-based access control, and integration with agent execution to enforce policies during task execution.","intents":["I want to define organizational policies (e.g., 'never deploy to production without approval') that agents must follow","I need to version and audit changes to operational procedures as they evolve","I want to share rulebooks across teams and enforce consistent policies across multiple agents"],"best_for":["Enterprise teams needing policy enforcement across multiple agents and teams","Organizations with compliance requirements that need auditable policy definitions","Teams evolving operational procedures and wanting to track changes over time"],"limitations":["Rulebook enforcement mechanism is not detailed in architecture; unclear how policies are validated during execution","No built-in conflict resolution for overlapping or contradictory rules","Rulebook format and schema are not specified; no standard for expressing complex policies","No integration with external policy engines (OPA, Kyverno); policies are agent-specific"],"requires":["Stakpak agent with rulebook CLI subcommand","Authentication and authorization for rulebook CRUD operations","Rulebook storage backend (local or remote)"],"input_types":["rulebook definitions (format unspecified)","policy constraints and conditions","team and access control metadata"],"output_types":["rulebook CRUD responses","rulebook versions and audit logs","policy enforcement results"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_8","uri":"capability://automation.workflow.async.and.interactive.execution.modes","name":"async-and-interactive-execution-modes","description":"Supports two execution modes: async mode that runs agent logic directly without a TUI for batch/scheduled execution, and interactive mode that spawns a TUI subprocess for real-time interaction. The CLI determines execution mode based on command-line flags and routes to different handlers (mode_async.rs, mode_interactive.rs). Both modes share the same agent logic and tool execution system, differing only in user interaction and output handling.","intents":["I want to run agents in batch mode via cron or CI/CD pipelines without interactive prompts","I need interactive mode for manual task execution with real-time feedback and approval prompts","I want to switch between batch and interactive execution without changing agent configuration"],"best_for":["DevOps teams running agents both interactively (manual tasks) and in batch (scheduled jobs)","Organizations integrating agents into CI/CD pipelines and cron-based automation","Teams needing flexible execution modes without maintaining separate agent implementations"],"limitations":["Mode selection is static per invocation; no dynamic mode switching based on task type","Async mode has no built-in output capture or logging; results must be explicitly logged by the agent","Interactive mode blocks on user input; no timeout for approval prompts","No built-in session persistence across mode switches; state is lost when switching from interactive to async"],"requires":["Rust 1.70+ for async runtime and subprocess spawning","Command-line flags to specify execution mode (--async or default interactive)","Terminal emulator for interactive mode"],"input_types":["command-line flags (--async)","task descriptions and context","user input (interactive mode only)"],"output_types":["task results and logs","exit codes for batch mode","interactive prompts and messages (interactive mode only)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-stakpak--agent__cap_9","uri":"capability://safety.moderation.warden.guardrails.system.for.policy.enforcement","name":"warden-guardrails-system-for-policy-enforcement","description":"Implements a guardrails system called Warden that enforces security and operational policies during agent execution. The system validates agent actions against defined rules before execution, prevents unauthorized operations, and logs policy violations. Warden integrates with the secret redaction system and privacy mode to enforce data protection policies. Policy rules are configurable and can be updated without restarting the agent.","intents":["I want to prevent the agent from executing dangerous operations (e.g., deleting production databases) without explicit approval","I need to enforce data protection policies (e.g., no exfiltration of sensitive data) at the tool execution layer","I want to audit all policy violations and maintain compliance with organizational security standards"],"best_for":["Enterprise teams running agents on production infrastructure with strict security requirements","Organizations with compliance mandates (SOC2, ISO27001) requiring policy enforcement and audit trails","Teams needing to prevent accidental or malicious agent actions through guardrails"],"limitations":["Guardrail rule format and evaluation logic are not detailed; unclear how complex policies are expressed","No built-in integration with external policy engines; policies are Warden-specific","Policy updates require configuration changes; no dynamic policy injection during execution","Guardrail enforcement is synchronous; no async policy evaluation for long-running validations"],"requires":["Warden guardrails configuration in ~/.stakpak/config.toml","Policy rules defined in Warden format (unspecified)","Encryption key for storing policy metadata"],"input_types":["agent actions (tool calls, operations)","policy rules and constraints","execution context (user, host, time)"],"output_types":["policy validation results (allow/deny)","policy violation logs with audit trails","enforcement metadata (rule matched, timestamp)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Rust 1.70+ (workspace uses tokio async runtime)","API key for RemoteClient (OpenAI, Anthropic, or compatible endpoint)","SSH access to target machines for remote tool execution","MCP-compatible tool definitions in JSON schema format","Terminal emulator with ANSI color support (xterm-256color or equivalent)","Rust 1.70+ for TUI subprocess spawning","Unix-like OS (Linux, macOS) — Windows support via WSL2 only","TOML configuration file at ~/.stakpak/config.toml","Read/write permissions on config file","API keys for configured LLM providers"],"failure_modes":["MCP tool execution adds latency per decision cycle — no built-in batching for high-frequency operations","LocalClient mode requires LLM inference capability on the host machine; no streaming inference optimization documented","SSH-based remote execution depends on pre-configured credentials and network connectivity; no built-in retry logic for transient failures","AgentProvider abstraction supports 14 async methods but no transaction semantics for multi-step operations","TUI rendering is single-threaded; high-frequency message updates (>100/sec) may cause frame drops","No built-in session persistence — closing the TUI loses interaction history unless explicitly saved","Terminal size changes require manual re-rendering; no automatic responsive layout adaptation","Interactive components (prompts, confirmations) block agent execution; no non-blocking approval workflows","Configuration is file-based; no integration with external config management systems (Consul, etcd)","No built-in config validation schema; invalid configurations may not be caught until runtime","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4609417371139348,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:57:09.057Z","last_commit":"2026-05-01T14:01:40Z"},"community":{"stars":1468,"forks":139,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=stakpak--agent","compare_url":"https://unfragile.ai/compare?artifact=stakpak--agent"}},"signature":"ChQ7zq/djDLxF8eswUZ4w3rv/RyPq2tGaMTccar6xEzQ+9Bj/Oi2aPl61pjW3qlUHbZvVYc+9+Rkdy34W3MZCw==","signedAt":"2026-06-21T21:02:11.597Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stakpak--agent","artifact":"https://unfragile.ai/stakpak--agent","verify":"https://unfragile.ai/api/v1/verify?slug=stakpak--agent","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"}}