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 Owl-Listener/ai-design-skills --skill chain-of-thought-designgit clone --depth 1 https://github.com/Owl-Listener/ai-design-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/owl-listener/ai-design-skills/chain-of-thought-design)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/chain-of-thought-design"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/chain-of-thought-design/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/owl-listener/ai-design-skills/chain-of-thought-design"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/chain-of-thought-design.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 | $0.00015 | $0.00582 |
| Opus 5 | $0.00008 | $0.00291 |
| Sonnet 5 | $0.00003 | $0.00116 |
| Haiku 4.5 | $0.00002 | $0.00058 |
Grade A, and why
chain-of-thought-design 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 12d 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chain-of-Thought Design
Chain-of-thought prompting asks the AI to show its reasoning step by step before arriving at an answer. When designed well, this produces more accurate, more nuanced, and more trustworthy outputs. When designed poorly, it produces verbose justification of bad answers.
When Chain-of-Thought Helps
- Complex reasoning: Multi-step problems where the answer depends on intermediate conclusions
- Ambiguous inputs: When the AI needs to consider multiple interpretations
- High-stakes outputs: When you need to verify the reasoning, not just the answer
- Creative exploration: When generating ideas benefits from building on each step
- Analytical tasks: Comparisons, evaluations, and trade-off analyses
When Chain-of-Thought Hurts
- Simple lookups: "What's the capital of France?" doesn't need step-by-step reasoning
- Speed-critical responses: Reasoning adds latency and token cost
- Pattern-matching tasks: Some tasks are better served by direct response
- When reasoning is wrong: The AI can reason convincingly toward an incorrect conclusion
Designing Reasoning Chains
A reasoning chain has structure. Design it deliberately: 1. Problem decomposition "First, break this problem into its component parts." 2. Evidence gathering "For each part, identify what you know and what you're uncertain about." 3. Analysis "Analyse each component, noting assumptions and limitations." 4. Synthesis "Combine your analysis into an overall assessment." 5. Conclusion "State your conclusion and your confidence level."
Chain Variants
- Linear chain: Step 1 → Step 2 → Step 3 → Answer. Simple and predictable.
- Branching chain: Consider multiple paths, evaluate each, then choose. Better for decisions.
- Iterative chain: Draft an answer, critique it, revise it. Better for quality refinement.
- Debate chain: Argue for and against a position, then synthesise. Better for balanced analysis.
Controlling Chain Quality
- Specify the steps: Don't just say "think step by step." Define what the steps are.
- Limit reasoning depth: Set a maximum number of steps or reasoning length to prevent runaway chains.
- Separate thinking from output: Let the AI reason internally, then produce a clean final output.
- Validate intermediate steps: Check that each step is sound, not just the final answer.
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.
- 12d ago First seen · 46 lines · 15 tokens per session scan A c2da1201a22a
chain-of-thought-design is a skill published in the GitHub repository Owl-Listener/ai-design-skills (173 stars, last pushed 3mo ago), licensed MIT. It adds 15 tokens to every session and 582 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 2026-08-30.
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