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Found 174 Skills
Use when working with error diagnostics smart debug
Form a committee of two high-reasoning agents to step back, do root cause analysis, and produce a plan. Use when stuck, looping, tunnel-visioning, or facing a hard planning problem.
Debug experiment code with structured error analysis. Categorize errors, apply targeted fixes with retry logic, and use reflection to prevent recurring issues. Use when experiment code fails or produces incorrect results.
Follow this sub-process when fixing bugs — turn the verbal description of "problem found" into a closed loop from verification to repair, leaving three documents: problem report, root cause analysis, and repair record. This process adds a buffer between "seeing the problem" and "starting to modify code" to avoid common pitfalls: the problem description in your mind disappears after the fix, you only fix the surface without analyzing the root cause, the scope of repair expands and cannot be traced, and you don't know if the fix is correct without verification after modification. This skill only acts as a router, deciding which of report / analyze / fix to proceed with based on existing artifacts. For simple problems that can be identified at a glance, a fast track will be taken, skipping the two middle steps and only retaining the fix-note.
Systematic debugging frameworks for finding and fixing bugs - includes root cause analysis, defense-in-depth validation, and verification protocols
You are an expert error analysis specialist with deep expertise in debugging distributed systems, analyzing production incidents, and implementing comprehensive observability solutions.
Investigate suspected bugs with git archaeology and root cause analysis. Triggers: "bug", "broken", "doesn't work", "failing", "investigate bug".
Use when encountering bugs or test failures - systematic debugging using debuggers, internet research, and agents to find root cause before fixing
Iterative Five Whys root cause analysis drilling from symptoms to fundamentals
Integrates Kelet into AI applications end-to-end: instruments agentic flows with OTEL tracing, maps session boundaries, adds user feedback signals (VoteFeedback, edit tracking, coded behavioral hooks), generates synthetic signal evaluator deeplinks, and verifies the integration. Kelet is an AI agent that performs Root Cause Analysis on AI app failures — it ingests traces and signals, clusters failure patterns, and suggests fixes. Use when the developer mentions Kelet or asks to integrate, set up, instrument, or add tracing/signals/feedback to their AI app. Triggers on: "integrate Kelet", "set up Kelet", "add Kelet", "instrument my agent", "connect Kelet", "use Kelet".
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework with built-in backward tracing for deep-stack failures, ensuring root-cause understanding before implementation
Diagnose and fix bugs with root-cause analysis and verification. Use when you have a concrete issue report, failing behavior, runtime error, or test regression that should be resolved safely. For ambiguous, high-risk, or broad-scope issues, stop and route to write-plan first.