How does it prevent AI hallucinations during debugging?

The skill utilizes Strawberry MCP tools like audit_trace_budget to verify that every claim about a root cause or fix is supported by actual evidence found in the repository, logs, or test results.

What is an Evidence Pack?

An Evidence Pack is a collection of specific code snippets, log excerpts, and test outputs (spans) that the agent uses to prove its claims and justify the proposed fix.

What is the RCA Fix Agent?

It is a specialized Claude Code skill that automates the process of finding, verifying, and fixing software bugs using an evidence-first approach and a rigorous iterative workflow.

Does it automatically run tests?

Yes, the agent identifies a reproduction command, runs it to establish a baseline, implements the fix, and then iterates until the original failure is resolved and no new regressions are found.

RCA Fix Agent

Name: RCA Fix Agent
Author: leochlon

byleochlon

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1,578

Security & Testing

Automates the root cause analysis and bug fixing process using iterative verification and evidence-based reasoning.

About

The RCA Fix Agent is a high-reliability debugging skill designed to eliminate guesswork in software maintenance. It follows a rigorous iterative workflow that builds an 'Evidence Pack' from logs, code, and test outputs to identify the source of an issue. By integrating with Strawberry MCP tools for hallucination detection, it ensures that every claim about a bug's root cause and every proposed fix is backed by verifiable data before implementation. This skill is ideal for resolving complex production incidents, flaky tests, and regression issues where accuracy and proof are paramount.

Key Features

Automated iterative fix-and-test loop for complex bug resolution
Regression check generation to prevent secondary failure modes
Evidence-driven Root Cause Analysis (RCA) with citation mapping
Hallucination detection via Strawberry MCP to verify debugging claims
Standardized reporting with verified evidence spans and test outputs
1,578 GitHub stars

Use Cases

Debugging flaky test suites or broken builds in CI/CD pipelines
Resolving production incidents with verified evidence and minimal risk
Automating the identification and fixing of complex logic bugs in legacy code

About

Key Features

Automated iterative fix-and-test loop for complex bug resolution
Regression check generation to prevent secondary failure modes
Evidence-driven Root Cause Analysis (RCA) with citation mapping
Hallucination detection via Strawberry MCP to verify debugging claims
Standardized reporting with verified evidence spans and test outputs
1,578 GitHub stars

Use Cases

Debugging flaky test suites or broken builds in CI/CD pipelines
Resolving production incidents with verified evidence and minimal risk
Automating the identification and fixing of complex logic bugs in legacy code