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 human-avatar/skills-for-humanity --skill s4h-epistemology-knowledge-typesgit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-epistemology-knowledge-types)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-epistemology-knowledge-types"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-epistemology-knowledge-types/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/human-avatar/skills-for-humanity/s4h-epistemology-knowledge-types"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-epistemology-knowledge-types.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 | $0.00142 | $0.01864 |
| Opus 5 | $0.00071 | $0.00932 |
| Sonnet 5 | $0.00028 | $0.00373 |
| Haiku 4.5 | $0.00014 | $0.00186 |
Grade A, and why
s4h-epistemology-knowledge-types 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Epistemology: Knowledge Types
Before you can test whether a claim is justified, you need to know what kind of claim it is — because different kinds of knowing have different justification standards, different failure modes, and different evidentiary requirements. Mixing up knowledge types is a recurring source of bad reasoning: treating empirical claims as if they're matters of pure logic, treating intuitions as if they're perceptions, treating testimony as if it's first-hand knowledge.
This skill classifies the kind of knowing in play, then draws out what that classification implies for how the claim can be established or challenged.
Your Process
Step 1: Extract the Claim State the claim being made as precisely as possible. Strip away rhetorical packaging. What exactly is being asserted?
Framing check: Confirm the specific claim before continuing. State what you've identified — the actual claim being analyzed and the context in which it is being made — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific claim and context]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
Step 2: Classify Along the Primary Axis — A Priori vs. A Posteriori
-
A priori — can be known through reason alone, independent of experience. True by definition, logical necessity, or mathematical proof. Examples: "all bachelors are unmarried," "2+2=4," "if A>B and B>C then A>C."
- Test: could this be false if the world were different? If no, it's a priori.
- Failure mode: confusing definitional truths with empirical claims ("free markets are efficient" can be made a priori by definition, but that makes it empty rather than powerful).
-
A posteriori — requires experience and evidence. Could be false; the world could have been otherwise. Examples: "water boils at 100°C at sea level," "this product's NPS is 42."
- Test: would we need to investigate the world to know if it's true? If yes, it's a posteriori.
- Failure mode: treating empirical claims as if they've been established when they've only been assumed.
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 · 141 lines · 142 tokens per session scan A 8ede9e17499c
s4h-epistemology-knowledge-types is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 142 tokens to every session and 1,864 once invoked, about $0.0007 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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