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.
npx skills add alivirgo/Major-AI-Skills --skill ask-ai-to-show-its-reasoninggit clone --depth 1 https://github.com/alivirgo/Major-AI-SkillsWrote 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/skills/alivirgo/major-ai-skills/ask-ai-to-show-its-reasoning)<a href="https://agentmods.dev/skills/alivirgo/major-ai-skills/ask-ai-to-show-its-reasoning"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/ask-ai-to-show-its-reasoning/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/skills/alivirgo/major-ai-skills/ask-ai-to-show-its-reasoning"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/ask-ai-to-show-its-reasoning.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 | $0.00029 | $0.01229 |
| Opus 5 | $0.00015 | $0.00615 |
| Sonnet 5 | $0.00006 | $0.00246 |
| Haiku 4.5 | $0.00003 | $0.00123 |
Grade A, and why
ask-ai-to-show-its-reasoning 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 yesterday.
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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ask AI to Show Its Reasoning (AI Skill)
Overview
When an AI simply proclaims: "I recommend Option B," you have no way to evaluate whether the conclusion is based on deep technical trade-offs or an arbitrary bias in training data.
Forcing the AI to expose its reasoning path before stating its final recommendation achieves two critical outcomes:
- Dramatically higher accuracy: Generating intermediate reasoning tokens allows the model to compute complex dependencies before finalizing the output.
- Auditability: It allows humans to inspect the logical premises, identify flawed assumptions, and make fully informed decisions.
The 4-Step Transparent Reasoning Framework
┌─────────────────────────────────────────────────────────────┐
│ Transparent Decision Flow │
│ │
│ 1. Evaluation Criteria ──► Weight key factors (Cost, Ops) │
│ │ │
│ 2. Analysis of Options ──► Strengths & fatal flaws │
│ │ │
│ 3. Elimination Logic ──► Why alternatives were rejected │
│ │ │
│ 4. Final Recommendation ──► Clear, defended conclusion │
└─────────────────────────────────────────────────────────────┘
Master Reasoning Prompts
Pattern 1: The First-Principles Decision Engine
Use this when choosing between technical architectures, business strategies, or tools:
I need to decide between [OPTION A], [OPTION B], and [OPTION C] for [SPECIFIC USE CASE & CONSTRAINTS].
Structure your analysis as follows:
1. **Decision Criteria**: Define the 3-4 most critical factors that determine success for this scenario.
2. **Side-by-Side Trade-off Table**: Score each option against those criteria (High / Medium / Low or 1-5).
3. **Elimination Analysis**: Explain why each rejected option fails to meet the threshold.
4. **Final Recommendation**: State your chosen option with a 2-sentence executive defense.
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.
- yesterday Changed · -5 tokens per session c369882f1b92
- 7d ago First seen · 119 lines · 34 tokens per session scan A 3c9b0ac1d760
ask-ai-to-show-its-reasoning is a skill published in the GitHub repository alivirgo/Major-AI-Skills (1 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 1,229 once invoked, about $0.0001 per session on Opus 5. 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 2026-09-05.
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