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 hajekim/agentic-design-patterns-extension --skill appendix-reasoning-enginesgit clone --depth 1 https://github.com/hajekim/agentic-design-patterns-extensionWrote 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/hajekim/agentic-design-patterns-extension/appendix-reasoning-engines)<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/appendix-reasoning-engines"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/appendix-reasoning-engines/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/hajekim/agentic-design-patterns-extension/appendix-reasoning-engines"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/appendix-reasoning-engines.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 | $0.00397 | $0.03386 |
| Opus 5 | $0.00198 | $0.01693 |
| Sonnet 5 | $0.00079 | $0.00677 |
| Haiku 4.5 | $0.00040 | $0.00339 |
Grade A, and why
appendix-reasoning-engines 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 8d 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.
This is a copy
100% identical to appendix-reasoning-engines — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 335 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Appendix F — Reasoning Engines: Under the Hood
Overview
Modern LLMs can be divided into two broad categories based on how they generate output:
- Standard Models: Generate tokens directly from the prompt context. Fast and cost-efficient.
- Reasoning Models: Perform extended internal deliberation — generating hidden "thinking" tokens before producing the final answer. Slower and more expensive, but significantly better at complex multi-step problems.
Understanding which type to use, and how they work internally, is critical for designing effective agentic systems.
Core Principle: Match the reasoning depth to the task complexity — reasoning models excel where accuracy matters more than speed; standard models win on throughput and cost.
When This Skill Applies
Activate this skill when:
- A task requires complex multi-step mathematical, logical, or strategic reasoning
- Accuracy is more important than latency (e.g., medical, legal, financial decisions)
- Standard models fail repeatedly on a problem that requires deliberate thinking
- You need to choose between
gemini-2.5-flash,gemini-2.5-flashwith high Thinking Budget, orgemini-2.5-pro - Building agents that need to plan deeply before acting
DEFINE → PLAN → ACTION Workflow
DEFINE
Assess the reasoning requirements:
- How many reasoning steps does the task require?
- Is accuracy or speed the primary constraint?
- What is the token budget and cost tolerance?
- Does the problem require backtracking or hypothesis testing?
PLAN
Select the right reasoning configuration:
- Map tasks to Standard vs. Reasoning model based on complexity
- Design the "thinking budget" — how many thinking tokens to allow
- Identify where to surface reasoning traces for debugging or auditing
- Plan cost controls (thinking tokens cost more than output tokens)
ACTION
Implement the reasoning-optimized agent:
- Use thinking-enabled models for complex planning and reasoning nodes
- Use standard models for simpler steps (retrieval, formatting, routing)
- Expose thinking traces for transparency when required
- Benchmark latency and accuracy trade-offs against your SLAs
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.
- 8d ago First seen · 335 lines · 397 tokens per session scan A e67edda05381
appendix-reasoning-engines is a skill published in the GitHub repository hajekim/agentic-design-patterns-extension (1 stars, last pushed 5mo ago), licensed MIT. It adds 397 tokens to every session and 3,386 once invoked, about $0.0020 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to appendix-reasoning-engines, differing in 3 lines, and is treated as a copy.
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