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 deciqAI/knowledge-skills --skill dual-system-thinkinggit clone --depth 1 https://github.com/deciqAI/knowledge-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/deciqai/knowledge-skills/dual-system-thinking)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/dual-system-thinking"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/dual-system-thinking/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/deciqai/knowledge-skills/dual-system-thinking"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/dual-system-thinking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00103 | $0.01832 |
| Opus 5 | $0.00051 | $0.00916 |
| Sonnet 5 | $0.00021 | $0.00366 |
| Haiku 4.5 | $0.00010 | $0.00183 |
Grade A, and why
dual-system-thinking 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 9d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dual-System Thinking (System 1 / System 2)
Overview
Two parallel modes: System 1 — fast, automatic, effortless (pattern recognition, gut feel). System 2 — slow, deliberate, effortful (analysis, computation, critical evaluation). System 1 runs ~95% of decisions by default; System 2 engages only when recruited. Most cognitive biases are System 1 shortcuts misapplied where System 2 should have intervened.
Composes with metacognition, every cognitive-bias skill (anchoring, confirmation-bias, availability-heuristic, etc.), deep-work, and wu-wei.
When to Use
- Decision feels obvious and is high-stakes; unfamiliar domain + fast intuition; time pressure on consequential matter; team converging without stress-test; depleted (tired/hungry/pressured)
- Designing a process or interface that depends on user attention
- Acting on a fast, fluent, confident AI/LLM answer (AI adoption, AI hype, "the AI said so") on a high-stakes call — when to force slow verification of a confident machine response
Not when: routine low-stakes decisions; calibrated expert System 1 in this exact domain; emergency requiring speed.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete decision → run The Process directly.
- Coach mode: user is unfamiliar → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line: when a high-stakes decision feels obvious, that obviousness is a warning — System 1 is firing and may need System 2 to check it.
- Check fit: routine low-stakes decisions are fine for System 1. This framework is for consequential, novel, or statistically-loaded decisions.
- Elicit the specific decision: what's being decided? Does it feel obvious? Are you tired / pressured / in your area of trained expertise?
[WAIT — do not advance until user responds]
- Work through: what's the System 1 answer? what's the System 2 check? what bias might be active? what procedure forces System 2 engagement?
[WAIT — do not advance until user responds]
- Close: name the system used and the procedure applied.
[WAIT — do not advance until user responds]
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 120 lines · 103 tokens per session scan A 44e408bc4c5e
dual-system-thinking is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 103 tokens to every session and 1,832 once invoked, about $0.0005 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 2026-09-03.
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