Automate structured multi-step reasoning for open-ended analytical questions.
npx skills add https://github.com/akiselev/qt-llm-template --skill deepthink-akiselev
Please help me install this Agent Skill. Skill: deepthink Source: https://github.com/akiselev/qt-llm-template/tree/main/.claude/skills/deepthink Command: npx skills add https://github.com/akiselev/qt-llm-template --skill deepthink-akiselev
Structured reasoning for open-ended analytical questions, enabling robust multi-step analysis without manual scaffolding.
Run the think script to start the structured reasoning workflow.
High-intent search queries and answers about installing and using this skill.
You can automate multi-step reasoning by running a Python script that orchestrates planning, sub-agent design, iteration, and formatting. This structured workflow handles taxonomy design and conceptual analysis without manual scaffolding.
Structured reasoning is a deterministic workflow that breaks down open-ended analyses into planned sub-agent tasks and iterative synthesis. You need it for taxonomy design or trade-off exploration when the answer structure is unknown.
You set up sub-agent design and iteration by executing the included Python script, which automates the orchestration across planning, critique, revision, and formatting to advance complex conceptual analyses.
No external dependencies are required. The structured reasoning workflow operates self-contained using a Python script to manage the multi-step planning and sub-agent orchestration.
Yes, you can use quick mode for faster, scoped reasoning. This mode limits the multi-step structured analysis to provide quicker outputs when full iterative synthesis is not required.
The best way to explore trade-offs with an unknown answer structure is using automated sub-agent design and iterative synthesis. This approach systematically plans and revises the analysis to map out complex conceptual spaces.
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