{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-usestrix--strix","slug":"usestrix--strix","name":"strix","type":"repo","url":"https://strix.ai","page_url":"https://unfragile.ai/usestrix--strix","categories":["code-review-security"],"tags":["agents","artificial-intelligence","cybersecurity","generative-ai","llm","penetration-testing"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-usestrix--strix__cap_0","uri":"capability://planning.reasoning.llm.controlled.multi.agent.penetration.testing.orchestration","name":"llm-controlled multi-agent penetration testing orchestration","description":"Coordinates multiple specialized LLM-powered agents operating in isolated Docker containers to execute dynamic security tests. Each agent receives system prompts that define its security testing role, maintains state across execution steps, and communicates findings through a centralized vulnerability deduplication system. Agents operate in a feedback loop where LLM reasoning drives tool selection and execution, with results fed back into the agent's context for iterative testing.","intents":["I want to automate penetration testing without hiring security consultants","I need to validate vulnerabilities through proof-of-concept exploits rather than static analysis","I want agents to specialize in different attack vectors and coordinate their findings"],"best_for":["DevSecOps teams automating security testing in CI/CD pipelines","Security consultants needing to scale penetration testing capacity","Bug bounty hunters automating vulnerability research workflows"],"limitations":["Agent reasoning adds latency per step — each tool call requires LLM inference, making real-time interactive testing slower than manual tools","Vulnerability deduplication relies on LLM-based comparison which may miss subtle variants or false negatives in complex attack chains","Multi-agent coordination overhead increases with number of agents; no built-in load balancing across concurrent scans","Requires Docker runtime with network access to target; cannot test in fully air-gapped environments"],"requires":["Python 3.9+","Docker daemon running with sufficient isolation capabilities","API key for at least one LLM provider (OpenAI, Anthropic, or compatible)","Target application accessible from Docker container network"],"input_types":["application URL or network target","scan configuration (YAML/JSON)","target application source code (optional, for context)"],"output_types":["structured vulnerability report (JSON/YAML)","proof-of-concept exploit code","remediation recommendations","compliance report with statistics"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_1","uri":"capability://tool.use.integration.docker.sandboxed.tool.execution.with.security.tool.integration","name":"docker-sandboxed tool execution with security tool integration","description":"Executes security testing tools (nmap, sqlmap, burp, etc.) within isolated Docker containers managed by a runtime abstraction layer. The tool execution architecture marshals LLM tool calls into container commands, captures output, and streams results back to agents. Sandbox initialization creates ephemeral containers with pre-configured security tool environments, preventing tool execution from affecting the host system or other concurrent scans.","intents":["I want to run security tools without installing them on my system","I need to isolate tool execution so concurrent scans don't interfere with each other","I want to safely execute untrusted or experimental security tools"],"best_for":["Teams running security scans in shared CI/CD infrastructure","Researchers testing new security tools without host contamination","Organizations requiring audit trails of tool execution in isolated environments"],"limitations":["Container startup overhead adds 2-5 seconds per sandbox initialization, impacting scan latency","Network isolation between containers and host requires explicit port mapping; some tools may not work with restricted network access","Container image size grows with each additional security tool; no built-in image caching across scans","Docker daemon resource limits (CPU, memory) constrain concurrent tool execution; unbounded parallelism will exhaust host resources"],"requires":["Docker daemon with API access (typically /var/run/docker.sock on Linux)","Sufficient disk space for container images and ephemeral layers","Network connectivity from containers to target application","Linux kernel with cgroup and namespace support"],"input_types":["tool name and arguments (marshaled from LLM tool calls)","target host/port/URL","tool-specific configuration files"],"output_types":["raw tool output (stdout/stderr)","structured tool results (parsed from output)","execution logs with timing and resource usage"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_10","uri":"capability://planning.reasoning.agent.state.management.and.execution.loop.control","name":"agent state management and execution loop control","description":"Manages agent lifecycle through a state machine that tracks agent initialization, execution steps, tool invocation, result processing, and termination. Each agent maintains mutable state (current findings, tools attempted, reasoning history) that persists across execution steps, enabling agents to learn from previous attempts and avoid redundant tool calls. The execution loop implements step-by-step reasoning with configurable termination conditions (max steps, timeout, vulnerability threshold reached).","intents":["I want agents to remember what they've already tried and avoid redundant work","I need to control how long agents spend on a single task","I want agents to stop when they've found enough vulnerabilities"],"best_for":["Teams wanting efficient agent execution without wasted effort","Organizations with strict time budgets for security testing","Researchers studying agent behavior and decision-making patterns"],"limitations":["State management adds memory overhead — large execution histories consume significant RAM","State persistence is in-memory only; agent state is lost if process crashes (no built-in checkpointing)","Termination conditions are static per scan; cannot dynamically adjust based on findings quality","State machine complexity makes debugging difficult; no built-in state visualization tools"],"requires":["Agent state definition (strix.agents.state)","Execution loop implementation (strix.agents.base_agent)","Sufficient memory for state persistence across execution steps"],"input_types":["agent initialization parameters","execution step configuration (max steps, timeout, termination conditions)"],"output_types":["agent state snapshots","execution trace (step-by-step decisions)","final agent findings"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_11","uri":"capability://tool.use.integration.tool.call.formatting.and.provider.specific.function.calling","name":"tool call formatting and provider-specific function calling","description":"Abstracts differences in function calling APIs across LLM providers through a unified tool call marshaling layer. The system converts agent tool requests into provider-specific formats (OpenAI function calling, Anthropic tool use, etc.), handles response parsing, and manages tool execution errors. Supports parallel tool calls where providers enable it, and implements retry logic for transient tool execution failures.","intents":["I want agents to call tools without worrying about provider-specific formats","I need reliable tool execution with automatic retry on failures","I want to support multiple LLM providers with different function calling APIs"],"best_for":["Teams using multiple LLM providers with different function calling formats","Organizations wanting resilient tool execution with automatic retries","Developers building custom tools that need to work across providers"],"limitations":["Tool call marshaling adds latency (~50ms per call) due to format conversion","Parallel tool calls are not supported by all providers; fallback to sequential execution adds latency","Retry logic is generic; no support for provider-specific error handling strategies","Tool definitions must be compatible with all providers; cannot use provider-specific features"],"requires":["Tool definitions in a provider-agnostic format","LLM provider with function calling support","Timeout configuration for tool execution"],"input_types":["tool definitions (name, description, parameters)","agent tool requests"],"output_types":["provider-specific function call requests","parsed tool execution results","error handling and retry metadata"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_12","uri":"capability://memory.knowledge.memory.compression.for.long.running.scans","name":"memory compression for long-running scans","description":"Optimizes LLM context windows for extended penetration tests by compressing agent reasoning history, tool output, and findings into summarized representations. The system identifies and removes redundant information, summarizes verbose tool output, and maintains only the most relevant context for ongoing reasoning. Compression is applied incrementally as scans progress, preventing context window overflow while preserving critical information needed for vulnerability discovery.","intents":["I want agents to run long scans without hitting LLM context limits","I need to preserve important findings while compressing verbose tool output","I want to reduce LLM token usage and costs for extended scans"],"best_for":["Teams running comprehensive penetration tests that exceed LLM context windows","Organizations optimizing LLM costs for long-running scans","Security teams needing to scan large applications with extensive attack surfaces"],"limitations":["Memory compression is lossy — important context may be discarded, leading to missed vulnerabilities","Compression logic is heuristic-based; no guarantee that critical information is preserved","Compression adds latency per step; scans with aggressive compression may be slower","No built-in mechanism to detect when compression has lost critical context"],"requires":["LLM with large context window (8k+ tokens)","Compression configuration (thresholds, strategies)","Sufficient processing time for compression operations"],"input_types":["agent reasoning history","tool output and findings","compression configuration"],"output_types":["compressed context representation","compression statistics (tokens saved, information loss estimate)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_2","uri":"capability://planning.reasoning.vulnerability.discovery.through.dynamic.proof.of.concept.exploitation","name":"vulnerability discovery through dynamic proof-of-concept exploitation","description":"Executes actual exploit code against target applications to validate vulnerabilities rather than relying on pattern matching or static signatures. Agents generate or select proof-of-concept payloads, execute them through sandboxed tools, and analyze results to confirm vulnerability existence. The system deduplicates findings across multiple agents and testing attempts, reducing false positives by requiring successful exploitation as evidence.","intents":["I want to confirm vulnerabilities are actually exploitable, not just theoretically possible","I need to generate proof-of-concept code that demonstrates impact","I want to reduce false positives from static analysis tools"],"best_for":["Security teams validating findings before remediation","Bug bounty hunters generating PoC code for submissions","Compliance auditors requiring evidence of actual vulnerability exploitation"],"limitations":["Exploitation may trigger security alerts or WAF blocks, causing false negatives if target has defensive measures","Some vulnerability classes (e.g., race conditions, timing attacks) are difficult to reliably exploit in automated PoC format","Exploitation requires network access to target; cannot test vulnerabilities in disconnected or air-gapped systems","PoC generation for complex vulnerabilities (e.g., logic flaws, business logic bypass) requires extensive agent reasoning and may timeout"],"requires":["Target application running and accessible from Docker containers","Sufficient permissions to execute test payloads (may require test accounts)","LLM with strong code generation capabilities for PoC creation","Timeout configuration allowing sufficient time for exploitation attempts"],"input_types":["vulnerability type or attack vector","target application endpoint or parameter","optional: known vulnerability details or CVE information"],"output_types":["proof-of-concept exploit code (Python, bash, HTTP requests)","exploitation results (success/failure with evidence)","impact assessment (data exfiltrated, system state changed, etc.)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_3","uri":"capability://planning.reasoning.agent.specialization.and.skill.based.task.decomposition","name":"agent specialization and skill-based task decomposition","description":"Defines specialized agent roles through system prompts that encode domain expertise for specific attack vectors (e.g., web application testing, API security, infrastructure scanning). Agents decompose complex penetration testing tasks into sub-tasks aligned with their specialization, selecting appropriate tools and techniques. The system routes findings between agents for cross-validation and enables agents to request assistance from specialized peers when encountering unfamiliar vulnerability types.","intents":["I want agents to focus on specific attack vectors rather than trying everything","I need agents to collaborate and validate each other's findings","I want to leverage domain expertise encoded in agent prompts"],"best_for":["Teams with diverse security testing needs (web, API, infrastructure, cloud)","Organizations wanting to encode internal security expertise into agents","Researchers studying how agent specialization affects vulnerability discovery rates"],"limitations":["Agent specialization is static — defined at initialization and not dynamically adjusted based on target characteristics","Inter-agent communication adds latency; agents must wait for peer responses before proceeding","Skill definitions are encoded in system prompts, making them difficult to update without redeploying agents","No built-in mechanism to detect when an agent is operating outside its specialization, potentially leading to poor decisions"],"requires":["Well-defined system prompts for each agent specialization","LLM capable of following complex role-based instructions","Inter-agent communication protocol (implementation details in strix.agents)","Target application characteristics known in advance to route to appropriate agents"],"input_types":["agent specialization definition (system prompt)","target application type and characteristics","task description or vulnerability class"],"output_types":["task decomposition plan","specialized findings from each agent","cross-validated vulnerability reports"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_4","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.provider.support","name":"llm provider abstraction with multi-provider support","description":"Abstracts LLM interactions behind a provider-agnostic client interface that supports OpenAI, Anthropic, and compatible APIs. The system handles provider-specific differences in function calling formats, token limits, and reasoning capabilities through a unified tool call formatting and parsing layer. Memory compression techniques optimize context windows for long-running scans, and the system automatically falls back to alternative providers if one becomes unavailable.","intents":["I want to switch between LLM providers without rewriting agent code","I need to optimize costs by using cheaper models for simple tasks and expensive models for complex reasoning","I want to avoid vendor lock-in to a single LLM provider"],"best_for":["Organizations with multi-cloud or multi-provider strategies","Teams wanting to experiment with different LLM models without code changes","Cost-conscious teams optimizing LLM spend across different task complexities"],"limitations":["Provider abstraction adds ~50-100ms latency per LLM call due to request marshaling and response parsing","Function calling format differences mean some advanced features (e.g., parallel tool calls) may not work across all providers","Token limit handling is provider-specific; memory compression may lose context relevant to vulnerability discovery","No built-in cost tracking or optimization — teams must manually monitor spend across providers"],"requires":["API key for at least one supported LLM provider (OpenAI, Anthropic, or compatible)","Provider configuration in environment variables or config file","Network connectivity to LLM provider API endpoints"],"input_types":["provider name and API key","model identifier (e.g., 'gpt-4', 'claude-3-opus')","agent prompts and tool definitions"],"output_types":["LLM responses (text, tool calls)","token usage statistics","provider-specific metadata"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_5","uri":"capability://data.processing.analysis.centralized.vulnerability.deduplication.and.correlation","name":"centralized vulnerability deduplication and correlation","description":"Aggregates findings from multiple agents and tool executions, deduplicating vulnerabilities based on LLM-powered semantic comparison rather than exact string matching. The system correlates related findings (e.g., multiple SQL injection points in the same parameter) and merges them into consolidated vulnerability reports. Deduplication logic handles variations in vulnerability descriptions, different exploitation paths to the same root cause, and false positive filtering.","intents":["I want to avoid duplicate vulnerability reports from multiple agents","I need to correlate related findings into a single consolidated issue","I want to filter out false positives before generating the final report"],"best_for":["Teams running multiple agents in parallel and needing consolidated reports","Organizations with strict vulnerability management processes requiring deduplication","Security teams wanting to reduce noise in vulnerability reports"],"limitations":["LLM-based deduplication is probabilistic — may miss subtle duplicates or incorrectly merge distinct vulnerabilities","Deduplication latency scales with number of findings; large scans (1000+ vulnerabilities) may timeout during deduplication","No built-in support for custom deduplication rules; teams cannot define domain-specific correlation logic","Deduplication decisions are not explainable — no audit trail showing why vulnerabilities were merged"],"requires":["LLM with semantic understanding capabilities","Vulnerability findings in structured format (JSON/YAML)","Sufficient context window to compare multiple findings simultaneously"],"input_types":["raw vulnerability findings from agents (title, description, impact, PoC)","optional: custom deduplication rules or similarity thresholds"],"output_types":["deduplicated vulnerability list","correlation metadata (which findings were merged)","consolidated remediation recommendations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_6","uri":"capability://automation.workflow.interactive.tui.and.non.interactive.cli.scan.modes","name":"interactive tui and non-interactive cli scan modes","description":"Provides dual user interfaces for different operational contexts: an interactive Terminal User Interface (TUI) for real-time monitoring and manual intervention during scans, and a non-interactive CLI mode for CI/CD integration and headless execution. Both modes support the same underlying scan engine but differ in feedback mechanisms — TUI streams live agent reasoning and tool output, while CLI mode buffers results for final report generation. The system abstracts UI concerns from core scanning logic through a unified interface layer.","intents":["I want to monitor scan progress in real-time and intervene if needed","I need to integrate Strix into CI/CD pipelines without interactive prompts","I want to see agent reasoning and tool execution as it happens"],"best_for":["Security analysts running interactive penetration tests","DevOps teams integrating Strix into automated CI/CD pipelines","Researchers studying agent behavior and decision-making"],"limitations":["TUI mode requires terminal support and cannot run in headless environments (e.g., containerized CI/CD without TTY)","Interactive mode adds latency for user input processing; scans cannot proceed until user responds to prompts","CLI mode buffers all output, potentially consuming significant memory for large scans with verbose logging","No built-in support for remote TUI access; interactive monitoring requires local terminal connection"],"requires":["For TUI: terminal with ANSI color support and sufficient width (80+ columns)","For CLI: standard stdout/stderr for result output","Python 3.9+ with curses library (for TUI mode)"],"input_types":["scan configuration (YAML/JSON)","target application URL or network target","optional: user input for interactive prompts (TUI mode only)"],"output_types":["TUI mode: real-time agent reasoning, tool output, vulnerability discoveries","CLI mode: final vulnerability report (JSON/YAML), statistics, remediation recommendations"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_7","uri":"capability://automation.workflow.configurable.scan.modes.with.reasoning.effort.levels","name":"configurable scan modes with reasoning effort levels","description":"Supports multiple scan modes (quick, standard, thorough) that adjust agent reasoning depth, tool coverage, and time budgets. Each mode configures the LLM's reasoning effort (e.g., chain-of-thought depth), number of agents deployed, tool selection strategy, and timeout thresholds. The system balances thoroughness against scan duration — quick scans use fast heuristics and limited tool sets, while thorough scans enable extended reasoning and comprehensive tool coverage.","intents":["I want fast vulnerability scans for rapid feedback in development","I need thorough penetration tests for compliance audits","I want to control the trade-off between scan speed and coverage"],"best_for":["Development teams wanting fast feedback during development cycles","Security teams running comprehensive scans for compliance","Organizations with varying security budgets and time constraints"],"limitations":["Quick mode may miss subtle vulnerabilities that require extended reasoning","Thorough mode can take hours or days, making it impractical for rapid feedback","Reasoning effort configuration is static per scan; cannot dynamically adjust based on findings","No built-in cost estimation — teams cannot predict LLM spend before running a scan"],"requires":["Scan mode configuration in config file or CLI arguments","LLM with configurable reasoning capabilities (e.g., OpenAI o1 with extended thinking)","Sufficient time budget for selected mode (quick: minutes, thorough: hours)"],"input_types":["scan mode selection (quick/standard/thorough)","optional: custom reasoning effort and timeout settings"],"output_types":["vulnerability report with coverage metrics","scan statistics (duration, tools used, agents deployed)","reasoning depth indicators"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_8","uri":"capability://automation.workflow.github.actions.ci.cd.integration.with.automated.vulnerability.blocking","name":"github actions ci/cd integration with automated vulnerability blocking","description":"Provides native GitHub Actions integration that runs Strix scans as part of CI/CD workflows and blocks deployments if vulnerabilities exceed configured severity thresholds. The integration marshals scan results into GitHub's native security features (code scanning alerts, security advisories), enabling developers to see vulnerabilities directly in pull requests and commit views. Supports configurable failure policies (fail on critical, warn on high, etc.) and integrates with GitHub's branch protection rules.","intents":["I want to block vulnerable code from being merged into main branches","I need security findings to appear in pull request reviews","I want to enforce security policies as part of CI/CD without manual review"],"best_for":["Development teams using GitHub and wanting shift-left security","Organizations enforcing security policies through CI/CD automation","DevSecOps teams integrating security testing into deployment pipelines"],"limitations":["GitHub Actions integration is GitHub-specific; teams using GitLab, Bitbucket, or other platforms must implement custom integration","Scan results are reported to GitHub but not automatically remediated; developers must manually fix vulnerabilities","Branch protection rules require GitHub Enterprise or specific plan tiers; not available on free tier","Scan latency in CI/CD can slow down development velocity if scans take >10 minutes"],"requires":["GitHub repository with Actions enabled","GitHub Actions workflow file (.github/workflows/strix.yml)","API key for LLM provider (stored as GitHub secret)","Docker daemon available in GitHub Actions runner (default on ubuntu-latest)"],"input_types":["GitHub Actions workflow configuration","scan mode and severity thresholds","optional: custom remediation instructions"],"output_types":["GitHub code scanning alerts","pull request comments with vulnerability details","workflow status (pass/fail based on severity thresholds)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usestrix--strix__cap_9","uri":"capability://data.processing.analysis.observability.and.structured.vulnerability.reporting","name":"observability and structured vulnerability reporting","description":"Implements a global tracer that instruments agent execution, tool calls, and vulnerability discoveries with structured logging and metrics. The system generates comprehensive vulnerability reports in multiple formats (JSON, YAML, HTML) with detailed remediation guidance, CVSS scores, and compliance mappings. Telemetry can be exported to remote systems for centralized security monitoring, and the system tracks scan statistics (duration, tools used, agents deployed, false positive rate).","intents":["I want to understand what agents did and why they made decisions","I need detailed vulnerability reports for compliance and remediation","I want to track security metrics and trends over time"],"best_for":["Security teams needing audit trails of security testing","Organizations with compliance requirements (SOC 2, ISO 27001, etc.)","Teams wanting to measure and improve security testing effectiveness"],"limitations":["Structured logging adds overhead — tracing can increase scan duration by 10-20%","Telemetry export requires network connectivity to remote systems; cannot function in air-gapped environments","Report generation is synchronous; large scans (1000+ vulnerabilities) may timeout during report creation","No built-in support for custom report formats; teams must post-process JSON/YAML for specialized needs"],"requires":["Logging configuration (file path or remote endpoint)","Optional: telemetry backend (e.g., Datadog, New Relic) for remote export","Sufficient disk space for detailed logs"],"input_types":["scan execution trace (agent decisions, tool calls, results)","vulnerability findings with metadata"],"output_types":["structured logs (JSON format)","vulnerability reports (JSON, YAML, HTML)","scan statistics and metrics","telemetry events for remote export"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Docker daemon running with sufficient isolation capabilities","API key for at least one LLM provider (OpenAI, Anthropic, or compatible)","Target application accessible from Docker container network","Docker daemon with API access (typically /var/run/docker.sock on Linux)","Sufficient disk space for container images and ephemeral layers","Network connectivity from containers to target application","Linux kernel with cgroup and namespace support","Agent state definition (strix.agents.state)","Execution loop implementation (strix.agents.base_agent)"],"failure_modes":["Agent reasoning adds latency per step — each tool call requires LLM inference, making real-time interactive testing slower than manual tools","Vulnerability deduplication relies on LLM-based comparison which may miss subtle variants or false negatives in complex attack chains","Multi-agent coordination overhead increases with number of agents; no built-in load balancing across concurrent scans","Requires Docker runtime with network access to target; cannot test in fully air-gapped environments","Container startup overhead adds 2-5 seconds per sandbox initialization, impacting scan latency","Network isolation between containers and host requires explicit port mapping; some tools may not work with restricted network access","Container image size grows with each additional security tool; no built-in image caching across scans","Docker daemon resource limits (CPU, memory) constrain concurrent tool execution; unbounded parallelism will exhaust host resources","State management adds memory overhead — large execution histories consume significant RAM","State persistence is in-memory only; agent state is lost if process crashes (no built-in checkpointing)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7579960131284658,"quality":0.35,"ecosystem":0.5800000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"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":"2026-05-03T13:58:21.997Z","last_commit":"2026-04-26T19:10:02Z"},"community":{"stars":24789,"forks":2754,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=usestrix--strix","compare_url":"https://unfragile.ai/compare?artifact=usestrix--strix"}},"signature":"i/UNKmImsBuyKo0+KQamYs5efgV7GeQFLIDDuiSK2kobxSTBBqZBmjJo2+RNjen3u4P52TnSDVTPdJXJ+N5WDw==","signedAt":"2026-06-21T02:30:12.133Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/usestrix--strix","artifact":"https://unfragile.ai/usestrix--strix","verify":"https://unfragile.ai/api/v1/verify?slug=usestrix--strix","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"}}