How does the Execution Chain improve my workflow?

It breaks down actions into numbered steps, allowing you to see exactly which files were read, which commands were run, and which lines were edited in sequence.

Why does this skill discourage words like 'obviously' or 'simply'?

To prevent the AI from glossing over complexity. This ensures that difficult tasks are properly explained and that the logic remains accessible to all developers.

What are Phase Markers in this skill?

Phase Markers are status indicators appended to the end of responses that show whether the AI is currently thinking, executing, verifying, or blocked.

Does this skill help with root cause analysis?

Yes, it provides a specialized Debug Trace Pattern that uses Hypotheses, Tests, and Results to systematically eliminate variables and find bugs.

Transparency & Reasoning Trace

Name: Transparency & Reasoning Trace
Author: duyet

byduyet

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Herramientas para Desarrolladores

Standardizes AI reasoning and execution chains to make every decision and step visible during the development process.

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The Transparency skill for Claude Code introduces structured execution patterns that make the AI's internal reasoning process fully traceable and verifiable. By implementing numbered execution steps, phase markers, and hypothesis-driven debugging traces, it builds trust and allows developers to audit logic at every stage. It eliminates ambiguity in the AI's workflow, replacing vague summaries with explicit evidence and status updates ranging from initial analysis to final verification.

Características Principales

Structured thinking blocks for complex logic analysis
Hypothesis-driven debug trace patterns for systematic troubleshooting
Real-time phase markers to indicate AI status (Thinking, Executing, Verifying)
Communication guidelines that prioritize evidence over dismissive language
Numbered execution chains for step-by-step action visibility
2 GitHub stars

Casos de Uso

Auditing complex code refactors to ensure every logic change is documented
Debugging elusive system bugs through explicit hypothesis-based investigations
Learning best practices by following the AI's step-by-step reasoning process

Acerca de

Características Principales

Structured thinking blocks for complex logic analysis
Hypothesis-driven debug trace patterns for systematic troubleshooting
Real-time phase markers to indicate AI status (Thinking, Executing, Verifying)
Communication guidelines that prioritize evidence over dismissive language
Numbered execution chains for step-by-step action visibility
2 GitHub stars

Casos de Uso

Auditing complex code refactors to ensure every logic change is documented
Debugging elusive system bugs through explicit hypothesis-based investigations
Learning best practices by following the AI's step-by-step reasoning process