Skill UI

A comprehensive directory designed for discovering, researching, and comparing autonomous AI agents, frameworks, and tools across diverse ecosystems. This skill helps users map the current AI agent landscape, identify existing solutions, gather best practices, and pinpoint market gaps before initiating custom development or workflow design.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

This pattern addresses the fundamental limitation of context window size by treating the filesystem as a persistent, external memory. Agents can write large outputs, plans, or findings to files, offloading them from the active context. Instead of carrying all information, agents use targeted search and retrieval tools to load only the necessary context on demand, drastically reducing token bloat and improving performance over long, complex tasks.

Evaluates incoming requests, decides if specialized skills are needed, orchestrates optimal combinations, and logs successful pairings in agent-memory while guarding against overusing complex tools on simple tasks.

Documented best practices for building applications with Anthropic Claude via Python and TypeScript SDKs, covering messaging, streaming, tools, vision, reasoning, caching, batching, and Agent workflows to optimize prompts, tokens, and cost.

Guides solo founders through end-to-end GTM decisions, stacking affordable AI-supported tools, designing agent workflows, allocating their most precious time, and knowing when to hire so they can scale go-to-market without adding headcount.

A comprehensive design specification detailing 98 rules for building Command Line Interface (CLI) tools that are safe, reliable, and easily consumable by AI agents. It covers structured JSON output formats, standardized error handling (to stderr), robust input contracts, safety guardrails (e.g., preventing path traversal), and tiered compliance levels (Agent-Friendly, Agent-Ready, Agent-Native) for seamless integration into AI workflows.

A comprehensive toolset for analyzing Cobalt Strike Malleable C2 profiles. It parses advanced C2 communication configurations to extract crucial Indicators of Compromise (IOCs) such as HTTP headers, DNS settings, URI patterns, and user agents. The analysis is vital for threat hunting, developing network detection signatures (Suricata/Snort), and understanding evasion techniques used by advanced persistent threats (APTs).

This guide details comprehensive methods for auditing Terraform infrastructure-as-code (IaC) against security misconfigurations. By leveraging industry-standard tools like Checkov, tfsec, Terrascan, and Open Policy Agent (OPA), users can detect critical vulnerabilities—such as overly permissive IAM policies, public resource exposure, and missing encryption—before cloud deployment. It is essential for integrating robust security gates into CI/CD pipelines and enforcing compliance standards.

This skill provides a methodology to detect data exfiltration attempts hidden within DNS queries. By analyzing Zeek dns.log files, the agent computes Shannon entropy of subdomains, flags unusually long labels, and monitors unique subdomain counts per parent domain. This technique is crucial for identifying malicious tunneling tools like dnscat2 or iodine that bypass traditional security controls.

Use Clarvia AEO (Agent Experience Optimization) to score any MCP server, API, or CLI tool for agent-readiness. It evaluates 15,400+ AI tools across four dimensions—API accessibility, data structuring, agent compatibility, and trust signals—ensuring the components are robust enough for advanced AI agent workflows and dynamic selection processes.

A governance skill designed to secure AI agent tool calls using advanced authorization policies. It implements Cedar policies to control which tools agents can access and under what conditions. Key features include running in 'Shadow mode' for safe observation, 'Enforce mode' for strict blocking, and generating cryptographically signed Ed25519 receipts to provide an immutable, tamper-evident audit trail for every policy decision.