{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_cleric","slug":"cleric","name":"Cleric","type":"agent","url":"https://cleric.io","page_url":"https://unfragile.ai/cleric","categories":["ai-agents"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_cleric__cap_0","uri":"capability://productivity.intelligent.alert.deduplication","name":"intelligent-alert-deduplication","description":"Automatically groups related alerts from multiple sources into coherent incidents, eliminating duplicate and redundant notifications. Uses correlation logic to identify alerts that represent the same underlying problem across different monitoring systems.","intents":["I want to stop receiving the same alert 50 times from different services","I need to see which alerts are actually related to the same incident","I want to reduce noise in my alerting system without losing visibility"],"best_for":["DevOps teams","SRE teams","infrastructure operators"],"limitations":["Requires alerts to have sufficient metadata for correlation","Effectiveness depends on alert naming and tagging conventions"],"requires":["Multiple alert sources integrated with Cleric","Structured alert data with timestamps and service identifiers"],"input_types":["alert streams","metrics","events"],"output_types":["grouped incidents","deduplicated alert lists"],"categories":["productivity","infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleric__cap_1","uri":"capability://productivity.autonomous.root.cause.analysis","name":"autonomous-root-cause-analysis","description":"Automatically investigates incidents by correlating logs, metrics, and traces across the entire infrastructure stack to identify the underlying cause. Performs causal analysis without requiring manual investigation or domain expertise.","intents":["I need to know what actually caused this incident without spending hours investigating","I want to correlate signals across my entire stack to find the root problem","I need faster incident resolution without waiting for an expert to analyze logs"],"best_for":["SRE teams","DevOps engineers","on-call responders"],"limitations":["Requires comprehensive observability data (logs, metrics, traces)","Garbage-in-garbage-out: poor instrumentation limits effectiveness","May struggle with novel or unprecedented failure modes"],"requires":["Integrated observability platform","Logs, metrics, and traces from all services","Proper instrumentation and correlation IDs"],"input_types":["logs","metrics","traces","events"],"output_types":["root cause analysis","causal chains","affected components"],"categories":["productivity","infrastructure","incident-response"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleric__cap_10","uri":"capability://infrastructure.service.dependency.impact.analysis","name":"service-dependency-impact-analysis","description":"Analyzes how incidents in one service impact dependent services and downstream systems. Maps the blast radius of failures across the infrastructure.","intents":["I need to understand how this database failure affects my microservices","I want to know which customers are impacted by this incident","I need to see the full scope of this failure across my infrastructure"],"best_for":["SRE teams","incident commanders","organizations with microservices"],"limitations":["Requires accurate service dependency mapping","Dynamic dependencies may not be captured","Requires understanding of service criticality"],"requires":["Service topology data","Dependency mapping","Impact metrics"],"input_types":["alerts","service topology","metrics"],"output_types":["impact maps","affected services","blast radius analysis"],"categories":["infrastructure","incident-response"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleric__cap_11","uri":"capability://infrastructure.observability.data.integration","name":"observability-data-integration","description":"Integrates with multiple observability platforms and data sources to create a unified view of infrastructure health. Normalizes and correlates data from different monitoring tools without custom development.","intents":["I want to use data from all my monitoring tools without building custom integrations","I need a unified view across Datadog, Prometheus, ELK, and other tools","I want to correlate signals from different observability platforms"],"best_for":["Organizations with multiple monitoring tools","teams without integration engineering resources"],"limitations":["Requires supported integrations with observability platforms","Data normalization may lose platform-specific details"],"requires":["Integrations with observability platforms","API access to monitoring systems"],"input_types":["observability APIs","logs","metrics","traces"],"output_types":["unified data model","correlated signals"],"categories":["infrastructure","integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleric__cap_2","uri":"capability://infrastructure.cross.stack.signal.correlation","name":"cross-stack-signal-correlation","description":"Correlates signals (logs, metrics, traces, events) across heterogeneous infrastructure components to identify patterns and relationships. Connects data from different monitoring systems, services, and layers of the stack.","intents":["I need to see how a database issue cascaded through my microservices","I want to understand the chain of events that led to this failure","I need to correlate signals from different tools and systems"],"best_for":["Complex infrastructure teams","multi-service environments","organizations with heterogeneous stacks"],"limitations":["Requires all relevant systems to be instrumented","Works best with structured data and correlation IDs","May miss correlations in poorly instrumented systems"],"requires":["Multiple data sources (logs, metrics, traces)","Proper instrumentation and tracing","Integration with observability platforms"],"input_types":["logs","metrics","traces","events","spans"],"output_types":["correlation graphs","causal relationships","signal chains"],"categories":["infrastructure","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleric__cap_3","uri":"capability://productivity.alert.noise.filtering","name":"alert-noise-filtering","description":"Intelligently filters out non-actionable alerts and false positives to reduce alert fatigue. Uses AI to distinguish between critical issues and expected noise patterns.","intents":["I want to ignore alerts that don't require action","I need to suppress known false positives without losing real alerts","I want to focus only on alerts that matter"],"best_for":["Teams with high alert volume","organizations suffering from alert fatigue"],"limitations":["Requires training data or patterns to learn what is noise","May suppress legitimate alerts if not properly configured"],"requires":["Historical alert data","Baseline patterns of normal behavior"],"input_types":["alert streams","metrics"],"output_types":["filtered alerts","noise classification"],"categories":["productivity","infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleric__cap_4","uri":"capability://infrastructure.heterogeneous.environment.analysis","name":"heterogeneous-environment-analysis","description":"Analyzes and correlates data from complex, multi-vendor infrastructure environments without requiring extensive custom configuration. Works across different monitoring tools, cloud providers, and technology stacks.","intents":["I have a mix of on-prem, cloud, and containerized systems and need unified incident analysis","I don't want to build custom integrations for every monitoring tool","I need root cause analysis that works across my entire heterogeneous stack"],"best_for":["Large enterprises","organizations with mixed infrastructure","teams without ML expertise"],"limitations":["Requires integrations with all relevant monitoring systems","Effectiveness depends on data quality from each source"],"requires":["Integrations with multiple observability platforms","Access to logs, metrics, and traces from all systems"],"input_types":["multi-source observability data"],"output_types":["unified incident analysis","cross-platform root causes"],"categories":["infrastructure","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleric__cap_5","uri":"capability://infrastructure.incident.severity.assessment","name":"incident-severity-assessment","description":"Automatically evaluates the severity and impact of incidents based on correlated signals and system state. Prioritizes incidents by business impact rather than alert count.","intents":["I need to know which incidents are actually critical vs. noisy","I want to prioritize incidents by business impact","I need to understand the scope and severity of each incident"],"best_for":["SRE teams","on-call responders","incident commanders"],"limitations":["Requires context about service criticality and dependencies","May not understand business impact without proper configuration"],"requires":["Service dependency maps","SLO/SLA definitions","Impact metrics"],"input_types":["alerts","metrics","service topology"],"output_types":["severity scores","impact assessments","prioritized incidents"],"categories":["infrastructure","incident-response"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleric__cap_6","uri":"capability://productivity.mean.time.to.resolution.optimization","name":"mean-time-to-resolution-optimization","description":"Reduces MTTR by automating triage and root cause analysis, enabling faster incident response. Provides actionable insights that allow on-call engineers to resolve issues more quickly.","intents":["I want to resolve incidents faster without hiring more on-call engineers","I need to reduce the time between alert and resolution","I want to give my team the information they need to fix problems quickly"],"best_for":["SRE teams","DevOps teams","organizations with high incident volume"],"limitations":["MTTR improvement depends on quality of root cause analysis","Doesn't automate remediation, only diagnosis"],"requires":["Comprehensive observability","Well-instrumented services"],"input_types":["alerts","logs","metrics","traces"],"output_types":["root cause analysis","remediation guidance"],"categories":["productivity","infrastructure","incident-response"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleric__cap_7","uri":"capability://infrastructure.incident.context.enrichment","name":"incident-context-enrichment","description":"Automatically enriches alerts and incidents with contextual information from logs, metrics, traces, and service topology. Provides on-call engineers with all relevant information needed to understand and resolve issues.","intents":["I want all the context I need to understand an incident in one place","I need to see logs, metrics, and traces related to an alert automatically","I want to understand the service dependencies affected by this incident"],"best_for":["On-call engineers","incident responders","SRE teams"],"limitations":["Requires comprehensive instrumentation","Context quality depends on data availability"],"requires":["Integrated observability platform","Service topology data","Logs, metrics, and traces"],"input_types":["alerts","logs","metrics","traces","service topology"],"output_types":["enriched incidents","contextual information","related signals"],"categories":["infrastructure","incident-response"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleric__cap_8","uri":"capability://infrastructure.alert.pattern.learning","name":"alert-pattern-learning","description":"Learns patterns from historical alert data to improve future triage and filtering. Adapts to your specific infrastructure patterns and alert characteristics over time.","intents":["I want the system to learn what normal looks like in my environment","I want filtering and triage to improve as the system learns my patterns","I want to reduce false positives based on my specific infrastructure"],"best_for":["Organizations with stable alert patterns","teams with historical alert data"],"limitations":["Requires sufficient historical data to learn patterns","May struggle with novel scenarios","Learning period needed before effectiveness improves"],"requires":["Historical alert data","Time for pattern learning"],"input_types":["alert streams","historical alerts"],"output_types":["learned patterns","improved filtering rules"],"categories":["infrastructure","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleric__cap_9","uri":"capability://infrastructure.incident.timeline.reconstruction","name":"incident-timeline-reconstruction","description":"Automatically reconstructs the timeline of events leading to an incident by correlating logs, metrics, and traces. Shows the sequence of failures and their causal relationships.","intents":["I want to understand the sequence of events that caused this incident","I need to see how one failure cascaded into others","I want a clear timeline of what happened and when"],"best_for":["Incident responders","post-incident review teams","SRE teams"],"limitations":["Requires precise timestamps across systems","Clock skew can affect timeline accuracy","May miss events from unmonitored systems"],"requires":["Synchronized timestamps across services","Comprehensive logging and tracing"],"input_types":["logs","metrics","traces","events"],"output_types":["incident timelines","event sequences","causal chains"],"categories":["infrastructure","incident-response"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"low","permissions":["Multiple alert sources integrated with Cleric","Structured alert data with timestamps and service identifiers","Integrated observability platform","Logs, metrics, and traces from all services","Proper instrumentation and correlation IDs","Service topology data","Dependency mapping","Impact metrics","Integrations with observability platforms","API access to monitoring systems"],"failure_modes":["Requires alerts to have sufficient metadata for correlation","Effectiveness depends on alert naming and tagging conventions","Requires comprehensive observability data (logs, metrics, traces)","Garbage-in-garbage-out: poor instrumentation limits effectiveness","May struggle with novel or unprecedented failure modes","Requires accurate service dependency mapping","Dynamic dependencies may not be captured","Requires understanding of service criticality","Requires supported integrations with observability platforms","Data normalization may lose platform-specific details","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.39999999999999997,"quality":0.82,"ecosystem":0.2,"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":"2026-05-24T12:16:29.717Z","last_scraped_at":"2026-04-05T13:23:42.549Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=cleric","compare_url":"https://unfragile.ai/compare?artifact=cleric"}},"signature":"oztHbmkT5Qo4lg3x3QeWn7twcmkr2rJsYpITtSa0LUVPasm6ejcWgWsVyL+xVb6Qz0I5PSqFfc+fkKfewoRQAw==","signedAt":"2026-06-22T14:39:27.688Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cleric","artifact":"https://unfragile.ai/cleric","verify":"https://unfragile.ai/api/v1/verify?slug=cleric","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"}}