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 falsifiabilitygit 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/falsifiability)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/falsifiability"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/falsifiability/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/falsifiability"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/falsifiability.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 | $0.00107 | $0.01745 |
| Opus 5 | $0.00053 | $0.00873 |
| Sonnet 5 | $0.00021 | $0.00349 |
| Haiku 4.5 | $0.00011 | $0.00175 |
Grade A, and why
falsifiability 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Falsifiability
Overview
A meaningful empirical claim must specify what observations would refute it. Claims that resist all possible refutation are not science — they are unfalsifiable belief. Formalized by Karl Popper (1934): science progresses not by accumulating confirmations but by surviving rigorous attempts at falsification. More-specific claims are more falsifiable; ad-hoc modifications that explain away failures destroy a claim's scientific status.
Composes with confirmation-bias (falsifiability is the structural counter), abductive-reasoning (generates hypotheses; this skill tests them), bayesian-reasoning, critical-thinking.
When to Use
- Designing OKRs, KPIs, or strategic goals; writing or evaluating investment theses
- Designing experiments (A/B tests, product hypotheses, market entry)
- Evaluating consultant/advisor recommendations or diagnosing vague leadership claims
- Someone says "what would change your mind," "how would you know you're wrong," "Popper"
- Stress-testing an AI/AGI hype claim ("AGI is near," "the model truly understands," "our AI adoption is working") — demand what evidence would disprove the capability or safety claim
Not when: genuinely non-empirical (philosophical, ethical, aesthetic); test cost exceeds its value.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a specific claim → run The Process directly.
- Coach mode: user is new → 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: before acting on a claim, ask what observation would refute it — if "nothing would," it's belief, not knowledge.
- Check fit: if the claim is genuinely metaphysical (ethical, aesthetic), falsifiability doesn't apply.
- Elicit the claim and its current evidential basis.
[WAIT — do not advance until user responds]
- Ask: what specific observation would falsify this? When and how would you observe it?
[WAIT — do not advance until user responds]
- Close: falsifiability conditions specified + monitoring plan + commitment to act on disconfirming evidence.
[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 · 122 lines · 107 tokens per session scan A 111ea0486d56
falsifiability is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 107 tokens to every session and 1,745 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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