{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-archieindian--openclaw-superpowers","slug":"archieindian--openclaw-superpowers","name":"openclaw-superpowers","type":"skill","url":"https://github.com/ArchieIndian/openclaw-superpowers","page_url":"https://unfragile.ai/archieindian--openclaw-superpowers","categories":["ai-agents","code-review-security"],"tags":["agent-framework","ai-agent","ai-automation","ai-plugins","ai-security","ai-tools","autonomous-agent","cron-scheduling","devtools","knowledge-graph","llm","llm-tools","machine-learning","mcp","model-context-protocol","openclaw","persistent-agent","prompt-injection","self-modifying-agent","skills-framework"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-archieindian--openclaw-superpowers__cap_0","uri":"capability://planning.reasoning.self.modifying.skill.acquisition.during.conversation","name":"self-modifying skill acquisition during conversation","description":"Enables AI agents to dynamically learn and integrate new capabilities mid-conversation without code deployment. The agent analyzes conversation context, generates skill implementations (Python functions), validates them against security guardrails, and registers them into its runtime skill registry for immediate use. Uses introspection and code generation to extend its own behavior based on user requests.","intents":["I want my agent to learn new behaviors from user feedback without redeploying","Enable agents to teach themselves domain-specific skills during interaction","Allow agents to adapt their capabilities based on conversation patterns"],"best_for":["Teams building autonomous agents that need adaptive behavior","Research teams exploring self-improving AI systems","Developers prototyping agents with evolving requirements"],"limitations":["Self-generated code quality depends on LLM reasoning — no static type checking on generated skills","Security guardrails may reject legitimate skill patterns if overly conservative","Skill persistence requires external storage integration — no built-in skill versioning or rollback"],"requires":["Python 3.8+","OpenClaw agent framework","LLM with code generation capability (GPT-4, Claude, etc.)","MCP server running for skill registration"],"input_types":["natural language requests","conversation context","skill specification in English"],"output_types":["Python function code","registered skill in agent runtime","execution results from new skill"],"categories":["planning-reasoning","code-generation-editing","self-modifying-agents"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_1","uri":"capability://automation.workflow.cron.based.scheduled.task.execution.with.agent.autonomy","name":"cron-based scheduled task execution with agent autonomy","description":"Provides declarative cron scheduling for autonomous agent tasks with persistent execution state. Agents define recurring jobs (e.g., 'every 6 hours, analyze logs') that execute independently on schedule, maintain execution history, and report results back to the agent's memory system. Integrates with the agent's planning layer to decompose scheduled tasks into skill invocations.","intents":["Schedule my agent to run periodic monitoring or data collection tasks","Enable agents to maintain background processes without human intervention","Track execution history and failures of autonomous scheduled tasks"],"best_for":["DevOps teams automating infrastructure monitoring with AI agents","Data teams building autonomous ETL pipelines with agent orchestration","Security teams deploying agents for continuous threat analysis"],"limitations":["Cron expressions limited to standard Unix cron syntax — no complex scheduling patterns like 'every 2nd Tuesday'","No built-in distributed scheduling — single-instance execution only, no cluster-aware task distribution","Execution history stored in agent memory — requires external persistence layer for audit compliance"],"requires":["Python 3.8+","APScheduler or equivalent cron scheduler library","OpenClaw agent with persistent memory backend","MCP server for task registration and monitoring"],"input_types":["cron expression string","task description","skill reference or function name"],"output_types":["execution timestamp","task result/output","execution status (success/failure)","error logs if applicable"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_10","uri":"capability://code.generation.editing.skill.testing.and.validation.framework","name":"skill testing and validation framework","description":"Provides a testing framework for validating skill correctness, performance, and safety before deployment. Supports unit tests (skill in isolation), integration tests (skill with dependencies), and end-to-end tests (full agent workflows). Includes test data generation, assertion helpers, and coverage analysis. Automatically runs tests on skill updates and blocks deployment if tests fail or coverage drops below threshold.","intents":["Test skills before deploying them to production","Ensure skill updates don't break existing functionality","Measure test coverage for skills"],"best_for":["Teams with quality assurance requirements","Continuous integration/deployment pipelines for agent skills","Safety-critical applications requiring skill validation"],"limitations":["Testing LLM-based skills is non-deterministic — same input may produce different outputs","Test data generation may not cover edge cases — requires manual test case creation","Coverage analysis doesn't account for skill logic branches — may report false positives","No built-in performance regression detection — requires manual baseline configuration"],"requires":["Python 3.8+","Testing framework (pytest, unittest, or custom)","Test data fixtures and factories","Optional: Coverage analysis tool (coverage.py)"],"input_types":["skill code","test cases and assertions","test data","performance baselines"],"output_types":["test results (pass/fail)","coverage reports","performance metrics","deployment approval/rejection"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_11","uri":"capability://tool.use.integration.skill.marketplace.and.community.sharing","name":"skill marketplace and community sharing","description":"Enables agents to discover, install, and share skills from a community marketplace. Agents can browse skills by category, read reviews and ratings, check compatibility with their version, and install skills with dependency resolution. Supports skill publishing with metadata (description, requirements, performance metrics), version management, and security scanning for malicious code. Integrates with package managers (pip) for easy installation.","intents":["Discover and reuse skills created by the community","Share my custom skills with other agents and teams","Find skills that solve specific problems"],"best_for":["Community-driven projects wanting to build skill ecosystems","Teams wanting to reduce skill development effort through reuse","Organizations building internal skill libraries"],"limitations":["Marketplace quality depends on community curation — no guarantee of skill quality or security","Dependency resolution may conflict with existing skills — requires careful version management","Security scanning is limited — can't detect all malicious patterns in generated code","No built-in licensing or usage tracking — requires external systems for compliance"],"requires":["Python 3.8+","Package registry (PyPI, private registry, or custom)","Security scanning tool (bandit, semgrep, or custom)","Marketplace UI (web or CLI)"],"input_types":["skill code and metadata","skill search queries","installation requests"],"output_types":["skill listings with metadata","installation packages","security scan results","compatibility reports"],"categories":["tool-use-integration","community-ecosystem"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_12","uri":"capability://code.generation.editing.skill.execution.tracing.and.debugging","name":"skill execution tracing and debugging","description":"Provides detailed execution traces for skill invocations, enabling debugging and understanding of agent behavior. Captures skill inputs, outputs, intermediate states, LLM calls, and execution time at each step. Supports interactive debugging with breakpoints, step-through execution, and variable inspection. Traces are exportable for analysis and can be replayed to reproduce issues. Integrates with standard debugging tools (pdb, VS Code debugger).","intents":["Debug why a skill produced unexpected output","Understand the execution flow of complex skill chains","Replay skill execution to reproduce bugs"],"best_for":["Developers debugging complex agent behaviors","Teams troubleshooting production issues","Researchers analyzing agent decision-making"],"limitations":["Tracing adds overhead (~10-20% execution time) due to instrumentation","Trace storage can be large for long-running skills — requires external storage and compression","Interactive debugging requires synchronous execution — incompatible with async/parallel skills","Trace replay may not be deterministic if skills have side effects"],"requires":["Python 3.8+","Tracing library (OpenTelemetry, custom instrumentation)","Trace storage (file system, database, or cloud service)","Optional: Debugging UI or IDE integration"],"input_types":["skill execution requests","breakpoint definitions","trace filters"],"output_types":["execution traces (JSON or structured format)","variable values at each step","LLM call logs","performance metrics"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_13","uri":"capability://safety.moderation.skill.permission.and.access.control.system","name":"skill permission and access control system","description":"Implements fine-grained access control for skills based on user roles, resource types, and execution context. Agents can be granted permissions to execute specific skills (e.g., 'read-only database access', 'no external API calls'), and the framework enforces these permissions at runtime. Supports role-based access control (RBAC), attribute-based access control (ABAC), and context-aware policies (time-based, location-based). Integrates with identity providers (OAuth, LDAP) for user authentication.","intents":["Control which skills different users or agents can execute","Enforce security policies on skill execution","Audit who executed which skills and when"],"best_for":["Multi-tenant agent systems with security requirements","Organizations with compliance requirements (SOC2, HIPAA, PCI-DSS)","Teams managing agents with different privilege levels"],"limitations":["Permission checking adds latency (~20-50ms per skill invocation)","Policy definition and maintenance can be complex — requires careful planning","No built-in policy conflict resolution — overlapping policies may cause unexpected behavior","Audit logging requires external storage — doesn't provide built-in compliance reporting"],"requires":["Python 3.8+","Identity provider (OAuth, LDAP, or custom)","Policy definition language (YAML, JSON, or custom DSL)","Audit logging infrastructure"],"input_types":["user/agent identity","skill execution request","execution context (time, location, resource)","permission policies"],"output_types":["permission decision (allow/deny)","audit log entry","permission denial reason"],"categories":["safety-moderation","security-hardening"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_14","uri":"capability://data.processing.analysis.skill.cost.estimation.and.budget.management","name":"skill cost estimation and budget management","description":"Tracks and estimates costs for skill execution (LLM API calls, compute resources, external services) and enforces budget limits. Provides cost breakdowns by skill, user, or time period, and alerts when spending approaches budget limits. Supports cost optimization strategies (model downgrading, caching, batching) and can automatically disable expensive skills if budget is exceeded. Integrates with cloud provider billing APIs for accurate cost tracking.","intents":["Understand and control LLM costs for my agent","Set budgets and get alerts when approaching limits","Optimize skill execution to reduce costs"],"best_for":["Cost-conscious organizations running agents at scale","Teams with strict budget constraints","Multi-tenant systems needing per-user cost tracking"],"limitations":["Cost estimation is approximate — actual costs may differ from estimates due to pricing changes or usage patterns","Budget enforcement is reactive — can't prevent overspending, only alert and disable skills after threshold","No built-in cost optimization — requires manual implementation of optimization strategies","Cloud provider billing APIs have latency — real-time cost tracking may be delayed"],"requires":["Python 3.8+","LLM provider pricing data (OpenAI, Anthropic, etc.)","Optional: Cloud provider billing API (AWS Cost Explorer, GCP Billing API)","Budget configuration and alerting system"],"input_types":["skill execution requests","cost parameters (model, tokens, API calls)","budget limits and thresholds"],"output_types":["cost estimates","cost breakdowns by skill/user/time","budget alerts","cost optimization recommendations"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_2","uri":"capability://safety.moderation.prompt.injection.detection.and.security.guardrails","name":"prompt injection detection and security guardrails","description":"Implements multi-layer defense against prompt injection attacks using pattern matching, semantic analysis, and execution sandboxing. Analyzes user inputs and generated skill code for injection signatures (e.g., 'ignore previous instructions'), validates skill implementations against a security policy (no file system access, no external network calls without approval), and isolates skill execution in restricted contexts. Guards against both direct injection and indirect injection through self-generated code.","intents":["Prevent malicious users from hijacking my agent's behavior through prompt injection","Ensure self-generated skills don't create security vulnerabilities","Audit and control what external resources agents can access"],"best_for":["Teams deploying agents in multi-tenant or untrusted environments","Security-conscious organizations with compliance requirements (SOC2, ISO27001)","Researchers studying adversarial attacks on self-modifying agents"],"limitations":["Pattern-based detection has false positive/negative rates — sophisticated injection attacks may evade regex-based signatures","Sandboxing adds execution overhead (~50-200ms per skill invocation) due to context isolation","Guardrail policies are static — no dynamic policy adaptation based on threat intelligence","No built-in logging of blocked attempts — requires external SIEM integration for security monitoring"],"requires":["Python 3.8+","Security policy configuration file (YAML or JSON)","Optional: semantic analysis model for advanced injection detection","Execution sandbox (Docker, seccomp, or OS-level isolation)"],"input_types":["user input text","generated Python code","skill execution requests"],"output_types":["injection risk score (0-1)","blocked/allowed decision","security violation report","sanitized/approved code"],"categories":["safety-moderation","security-hardening"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_3","uri":"capability://memory.knowledge.persistent.agent.memory.with.knowledge.graph.integration","name":"persistent agent memory with knowledge graph integration","description":"Maintains long-term agent state across sessions using a hybrid memory architecture: short-term conversation buffer, episodic memory (past interactions), semantic memory (facts and relationships), and a knowledge graph for structured reasoning. Automatically extracts entities and relationships from conversations, builds a graph representation, and enables agents to query historical context and infer new knowledge. Supports multiple backend storage (SQLite, PostgreSQL, vector databases) with configurable retention policies.","intents":["Give my agent long-term memory so it remembers past interactions and learned facts","Enable agents to reason over structured knowledge graphs of domain information","Query agent memory to audit decisions and understand agent reasoning"],"best_for":["Teams building long-running agents (chatbots, research assistants, monitoring systems)","Knowledge-intensive applications requiring structured reasoning over facts","Organizations needing agent decision audit trails and explainability"],"limitations":["Knowledge graph construction relies on LLM entity/relationship extraction — accuracy depends on model quality and may miss domain-specific relationships","Memory growth is unbounded without explicit pruning — requires manual or heuristic-based retention policies","Graph queries add latency (~100-500ms for complex traversals) compared to flat memory buffers","No built-in conflict resolution for contradictory facts in the knowledge graph"],"requires":["Python 3.8+","Storage backend: SQLite (local), PostgreSQL (production), or vector DB (semantic search)","Optional: Neo4j or similar graph database for advanced graph queries","LLM with entity/relationship extraction capability"],"input_types":["conversation text","structured facts (JSON)","knowledge graph queries (Cypher or custom DSL)"],"output_types":["memory retrieval results","knowledge graph nodes/edges","inferred facts","memory statistics and summaries"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_4","uri":"capability://automation.workflow.mcp.health.monitoring.and.skill.registry.management","name":"mcp health monitoring and skill registry management","description":"Provides real-time monitoring of Model Context Protocol (MCP) server health, skill availability, and execution metrics. Tracks server uptime, response latencies, error rates, and automatically deregisters unavailable skills. Implements health check endpoints, circuit breaker patterns for failing servers, and fallback skill chains when primary skills are unavailable. Exposes metrics via Prometheus-compatible endpoints for integration with monitoring stacks.","intents":["Monitor the health of my MCP servers and detect when skills become unavailable","Automatically handle skill failures without crashing the agent","Integrate agent health metrics into my existing monitoring infrastructure"],"best_for":["Production deployments requiring high availability and observability","Teams running distributed agent systems with multiple MCP servers","DevOps teams integrating agents into existing monitoring stacks (Prometheus, Grafana)"],"limitations":["Health checks add overhead (~10-50ms per check interval) — configurable but impacts latency","Circuit breaker patterns may temporarily block valid requests during transient failures","No built-in alerting — requires external monitoring system (Prometheus AlertManager, PagerDuty) for notifications","Fallback skill chains must be manually configured — no automatic fallback discovery"],"requires":["Python 3.8+","MCP server implementation","Optional: Prometheus client library for metrics export","Optional: External monitoring system (Prometheus, Grafana, Datadog)"],"input_types":["MCP server endpoint URLs","health check configuration","fallback skill definitions"],"output_types":["health status (healthy/degraded/unhealthy)","latency metrics (p50, p95, p99)","error rates and error types","Prometheus metrics in text format"],"categories":["automation-workflow","tool-use-integration","monitoring-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_5","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.model.switching","name":"multi-provider llm abstraction with model switching","description":"Provides a unified interface for calling multiple LLM providers (OpenAI, Anthropic, Ollama, local models) with automatic fallback and cost optimization. Abstracts provider-specific APIs (function calling, token counting, streaming) into a common interface, enables dynamic model selection based on task complexity or cost constraints, and tracks token usage across providers. Supports prompt caching and batch processing for cost reduction.","intents":["Use different LLM providers without rewriting agent code","Automatically switch models based on task complexity or cost","Track and optimize LLM spending across multiple providers"],"best_for":["Teams wanting to avoid vendor lock-in with a single LLM provider","Cost-conscious organizations needing to optimize LLM spending","Researchers comparing model performance across providers"],"limitations":["Provider API differences (function calling schemas, token limits) require normalization — some features may be unavailable for certain models","Model switching adds latency for decision logic (~50-100ms) and may cause inconsistent outputs across providers","Token counting is approximate for some providers — actual usage may differ from estimates","No built-in prompt optimization — requires manual tuning per provider"],"requires":["Python 3.8+","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Optional: Ollama or local LLM for on-premises deployment"],"input_types":["prompt text","model selection criteria (cost, latency, capability)","function calling schemas"],"output_types":["LLM response text","token usage statistics","cost estimates","model metadata"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_6","uri":"capability://planning.reasoning.skill.composition.and.chaining.with.dependency.resolution","name":"skill composition and chaining with dependency resolution","description":"Enables agents to compose multiple skills into complex workflows with automatic dependency resolution and execution planning. Agents define skill dependencies (e.g., 'skill B requires output from skill A'), and the framework automatically orders execution, handles data passing between skills, and manages error propagation. Supports conditional execution (if-then-else), loops, and parallel skill execution. Uses a DAG-based execution model with topological sorting for optimal ordering.","intents":["Chain multiple skills together to solve complex problems","Automatically order skill execution based on dependencies","Handle data flow between skills without manual orchestration"],"best_for":["Teams building multi-step autonomous workflows","Agents solving complex problems requiring skill composition","Developers wanting declarative workflow definition"],"limitations":["DAG model doesn't support dynamic loops — loop count must be known at planning time","Dependency resolution assumes deterministic skill outputs — non-deterministic skills may cause ordering issues","Error handling is basic — no built-in retry logic or partial failure recovery","Skill composition adds planning overhead (~100-300ms) before execution"],"requires":["Python 3.8+","Skill definitions with input/output type annotations","DAG execution engine (built-in or external like Airflow)"],"input_types":["skill definitions","dependency specifications","execution parameters"],"output_types":["execution plan (DAG)","skill execution results","execution trace/log"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_7","uri":"capability://search.retrieval.conversational.skill.discovery.and.documentation","name":"conversational skill discovery and documentation","description":"Enables agents to discover available skills through natural language queries and provide contextual documentation. Agents can ask 'what skills do I have for data analysis?' and receive filtered, ranked results with usage examples. Automatically generates skill documentation from docstrings and type hints, and provides interactive skill exploration through conversation. Supports skill categorization and tagging for better discoverability.","intents":["Help agents discover what skills are available without reading code","Provide contextual skill recommendations based on user intent","Generate and maintain skill documentation automatically"],"best_for":["Agents with large skill libraries (50+ skills) needing discovery mechanisms","Teams wanting to reduce skill documentation burden","Interactive agent systems where users need to explore capabilities"],"limitations":["Skill discovery ranking depends on semantic similarity — may not match user intent perfectly","Documentation quality depends on source docstrings — poorly documented skills won't be discoverable","No built-in skill usage analytics — can't recommend skills based on popularity or success rate","Conversational discovery adds latency (~200-500ms) compared to direct skill lookup"],"requires":["Python 3.8+","Skill definitions with docstrings and type hints","Semantic search capability (embeddings or LLM-based)"],"input_types":["natural language skill queries","skill category/tag filters","usage context"],"output_types":["ranked skill recommendations","skill documentation with examples","skill metadata (inputs, outputs, requirements)"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_8","uri":"capability://automation.workflow.skill.versioning.and.rollback.with.a.b.testing","name":"skill versioning and rollback with a/b testing","description":"Manages multiple versions of skills with automatic rollback capabilities and A/B testing support. Agents can deploy new skill versions, gradually roll them out to a percentage of requests, compare performance metrics (latency, error rate, user satisfaction), and automatically rollback if metrics degrade. Maintains version history and enables canary deployments where new skills are tested on a subset of traffic before full rollout.","intents":["Deploy new skill versions safely with gradual rollout","Compare performance of different skill implementations","Automatically rollback failing skill versions"],"best_for":["Production agent systems requiring high reliability","Teams iterating on skill implementations with continuous improvement","Data-driven teams wanting to measure skill performance impact"],"limitations":["A/B testing requires sufficient traffic to achieve statistical significance — low-traffic skills may need long test periods","Rollback decisions are based on configured metrics — may not catch subtle regressions","Version storage adds overhead and complexity — requires external version control system","No built-in cost comparison — can't automatically select cheaper skill versions"],"requires":["Python 3.8+","Version control system (Git, S3, or custom)","Metrics collection infrastructure (Prometheus, CloudWatch, etc.)","Statistical analysis library for A/B test significance"],"input_types":["skill code versions","rollout percentage","performance metrics to track","rollback thresholds"],"output_types":["version deployment status","A/B test results and statistics","rollback decision and logs","performance comparison reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-archieindian--openclaw-superpowers__cap_9","uri":"capability://data.processing.analysis.skill.performance.profiling.and.optimization.recommendations","name":"skill performance profiling and optimization recommendations","description":"Automatically profiles skill execution to identify performance bottlenecks and generates optimization recommendations. Tracks execution time, memory usage, API calls, and token consumption per skill, identifies slow skills, and suggests optimizations (caching, batching, model downsizing). Provides comparative analysis across skill versions and identifies skills with high variance in execution time. Integrates with monitoring systems to surface performance insights.","intents":["Identify which skills are slowing down my agent","Get recommendations for optimizing expensive skills","Track performance trends over time"],"best_for":["Performance-critical agent systems with SLA requirements","Cost-conscious teams optimizing LLM spending","Teams maintaining large skill libraries needing performance visibility"],"limitations":["Profiling adds overhead (~5-10% execution time) due to instrumentation","Optimization recommendations are heuristic-based — may not apply to all use cases","No built-in automatic optimization — recommendations require manual implementation","Profiling data requires external storage for historical analysis"],"requires":["Python 3.8+","Instrumentation library (cProfile, py-spy, or custom)","Metrics storage (Prometheus, InfluxDB, or similar)","Optional: APM tool (DataDog, New Relic) for advanced profiling"],"input_types":["skill execution traces","performance baselines","optimization constraints (latency, cost)"],"output_types":["performance metrics (latency, memory, cost)","bottleneck identification","optimization recommendations","performance comparison reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","OpenClaw agent framework","LLM with code generation capability (GPT-4, Claude, etc.)","MCP server running for skill registration","APScheduler or equivalent cron scheduler library","OpenClaw agent with persistent memory backend","MCP server for task registration and monitoring","Testing framework (pytest, unittest, or custom)","Test data fixtures and factories","Optional: Coverage analysis tool (coverage.py)"],"failure_modes":["Self-generated code quality depends on LLM reasoning — no static type checking on generated skills","Security guardrails may reject legitimate skill patterns if overly conservative","Skill persistence requires external storage integration — no built-in skill versioning or rollback","Cron expressions limited to standard Unix cron syntax — no complex scheduling patterns like 'every 2nd Tuesday'","No built-in distributed scheduling — single-instance execution only, no cluster-aware task distribution","Execution history stored in agent memory — requires external persistence layer for audit compliance","Testing LLM-based skills is non-deterministic — same input may produce different outputs","Test data generation may not cover edge cases — requires manual test case creation","Coverage analysis doesn't account for skill logic branches — may report false positives","No built-in performance regression detection — requires manual baseline configuration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.08697777366822308,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.15,"quality":0.25,"ecosystem":0.1,"match_graph":0.45,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-04-22T08:04:48.710Z","last_commit":"2026-04-17T03:26:14Z"},"community":{"stars":56,"forks":9,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=archieindian--openclaw-superpowers","compare_url":"https://unfragile.ai/compare?artifact=archieindian--openclaw-superpowers"}},"signature":"N/y2e1V0w6LrJIXHKSOhdIRr4qgjGTbyYhOP7Pc69tN56CEse3pahqL8sSav5bn3EDk6JdaoZCpjRwIri+BCBA==","signedAt":"2026-06-20T18:58:53.893Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/archieindian--openclaw-superpowers","artifact":"https://unfragile.ai/archieindian--openclaw-superpowers","verify":"https://unfragile.ai/api/v1/verify?slug=archieindian--openclaw-superpowers","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"}}