my-claude-code-setup is a starter configuration and memory-bank template for Claude Code, providing project guidance and reusable setup for Claude Code sessions. Developers use it to organize Claude Code behavior, memory, commands, skills, subagents, hooks, and settings. The catalogue entries are components of this Claude Code workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/centminmod/my-claude-code-setupWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/centminmod/my-claude-code-setup/apply-thinking-to)<a href="https://agentmods.dev/commands/centminmod/my-claude-code-setup/apply-thinking-to"><img src="https://agentmods.dev/badge/commands/centminmod/my-claude-code-setup/apply-thinking-to/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/centminmod/my-claude-code-setup/apply-thinking-to"><img src="https://agentmods.dev/badge/commands/centminmod/my-claude-code-setup/apply-thinking-to.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.02267 |
| Opus 5.5 | $0.00000 | $0.00907 |
| Sonnet 5.5 | $0.00000 | $0.00453 |
| Haiku 4.5 | $0.00000 | $0.00227 |
Grade A, and why
apply-thinking-to scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert prompt engineering specialist with deep expertise in applying Anthropic's extended thinking patterns to enhance prompt effectiveness. Your role is to systematically transform prompts using advanced reasoning frameworks to dramatically improve their analytical depth, accuracy, and reliability.
ADVANCED PROGRESSIVE ENHANCEMENT APPROACH: Apply a systematic methodology to transform any prompt file using Anthropic's most sophisticated thinking patterns. Begin with open-ended analysis, then systematically apply multiple enhancement frameworks to create enterprise-grade prompts with maximum reasoning effectiveness.
TARGET PROMPT FILE: $ARGUMENTS
SYSTEMATIC PROMPT ENHANCEMENT METHODOLOGY
Phase 1: Current State Analysis & Thinking Pattern Identification
Step 1 - Open-Ended Prompt Analysis:
- What is the primary purpose and intended outcome of this prompt?
- What thinking patterns (if any) are already present?
- What complexity level does this prompt operate at?
- What unique characteristics require specialized enhancement approaches?
Step 2 - Enhancement Opportunity Assessment:
- Where could progressive reasoning (open-ended → systematic) be most beneficial?
- What analytical frameworks would improve the prompt's effectiveness?
- What verification mechanisms would increase accuracy and reliability?
- What thinking budget allocation would optimize performance?
Phase 2: Sequential Enhancement Framework Application
Apply these enhancement frameworks systematically based on prompt type and complexity:
Framework 1: Progressive Reasoning Structure
Implementation Guidelines:
- High-Level Exploration First: Add open-ended thinking invitations before specific instructions
- Systematic Framework Progression: Structure analysis to move from broad exploration to specific methodologies
- Creative Problem-Solving Latitude: Encourage exploration of unconventional approaches before constraining to standard patterns
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 224 lines · 0 tokens per session scan A 6360c8398e9f
apply-thinking-to is a command published in the GitHub repository centminmod/my-claude-code-setup (2,693 stars, last pushed 20d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,267 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
improve-prompt
Analyze and improve an AI prompt for clarity, specificity, and effectiveness.
test-prompt
Test an AI prompt against multiple scenarios to verify consistent, quality output.
dev-ai-integration
Integration of language models (LLM) and AI APIs into applications.
improve-prompt
Improve any prompt — for LLMs/chat/agents or generative image/video/audio models. Model-agnostic; interviews to fill real gaps, then returns a rewritten prompt plus a short rationale.
evaluate
Score the prompt I just wrote and give me one thing to sharpen.
enhance
Intelligently enhances user input by generating a structured task map, refining an ambiguous query, or improving a code snippet.