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AI Agent Evaluation Suites

sickn33/agentic-awesome-skills

Build automated evaluation suites for AI agents with golden datasets, rubrics, and regression gates. Covers unit, tool-calling, end-to-end, and safety evals to validate prompt changes and gate deployments on quality.

AI Agent Behavior Evaluation

agent-evaluation

sickn33/agentic-awesome-skills

Evaluate AI agent behavior using versioned cases and explicit verifiers. This skill helps compare agent or prompt changes, reproduce failures, and run regression tests. It ensures reliability on declared task distributions without inferring product readiness from generic scores. Includes methods for uncertainty reporting and safety verification.

AI Agent Evaluation Reporting

agent-evaluation-reporting

sickn33/agentic-awesome-skills

Generate decision-ready reports from AI agent evaluation runs. This skill ensures autonomous, assisted, failed, and timed-out outcomes remain distinct and comparable. It defines metrics with correct denominators, handles latency and cost populations honestly, and maps evidence to decision gates. Ideal for benchmarking, regression testing, and production readiness validation of AI agents.

AI SRE Incident Response

ai-sre-incident-response

sickn33/agentic-awesome-skills

Establish SRE practices for AI systems to handle LLM outages, quality regressions, cost spikes, and safety issues. Includes monitoring, alerting, and response playbooks for AI infrastructure reliability.

Multi-Agent Bug Root Cause Analysis

sickn33/agentic-awesome-skills

This skill enables parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, or unexplained failures. It deploys four specialized sub-agents to analyze reproduction, code paths, recent changes, and proof plans simultaneously. The main agent synthesizes findings into ranked hypotheses without editing files. Ideal for diagnosing complex software issues efficiently.

Systematic Software Bug Debugging Guide

sickn33/agentic-awesome-skills

This comprehensive skill guides users through the entire debugging lifecycle, from reproducing elusive bugs to tracing the root cause, implementing permanent fixes, and preventing future regressions. It covers advanced techniques like binary search, time travel debugging, and handling common patterns such as race conditions and null values, ensuring professional and systematic troubleshooting for any production issue.

Debugging Workflow For Complex Bugs

diagnosing-bugs

sickn33/agentic-awesome-skills

This skill outlines a systematic, multi-phase workflow for diagnosing hard bugs and performance regressions in complex codebases. It emphasizes the critical necessity of building a tight, deterministic feedback loop (Phase 1) before moving to hypothesis generation. The process guides users through reproducing the failure, minimizing the scope of the issue, and forming actionable, ranked hypotheses.

End-to-End Testing Patterns

e2e-testing-patterns

sickn33/agentic-awesome-skills

Build reliable and maintainable end-to-end test suites to ensure code quality, catch regressions early, and validate critical user workflows across browsers and environments. Ideal for CI/CD pipelines and automation strategies.

EAS Update Health Insights

eas-update-insights

sickn33/agentic-awesome-skills

This skill allows developers to query the health metrics of published EAS Updates directly from the command line. It provides data on crash rates, install counts, unique users, payload sizes, and the split between embedded and OTA users per channel. Useful for post-publish rollout monitoring and regression detection.

Security Fix Commit Review

sickn33/agentic-awesome-skills

Verify that fix commits properly address audit findings without introducing new bugs or security vulnerabilities. Use it to review security remediation commits, confirm root-cause fixes, check for regressions, and validate completeness.

Laravel Development Workflow

laravel-development-workflow

sickn33/agentic-awesome-skills

This workflow guides developers to build and fix existing Laravel applications effectively. It emphasizes root-cause diagnosis, repository-native implementation, regression coverage, and risk-based verification. Suitable for feature implementation, bug fixing, and maintaining architectural conventions within established codebases.

LLM Prompt Optimization

llm-application-dev-prompt-optimize

sickn33/agentic-awesome-skills

Improve LLM prompts using task-specific evaluation, structured instructions, chain-of-thought reasoning, and regression checks for production-ready AI applications.

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