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S4h Epistemology Epistemic Status

Organization
human-avatar
s4h-epistemology-epistemic-status

Produces an honest, rigorous calibration of what you know vs. believe vs. assume vs. hope across a domain. Assigns explicit epistemic statuses to claims and flags when high-confidence claims rest on lower-confidence foundations. Draws from the rationalist tradition of explicit epistemic labeling. Use when you say 'how certain should I be', 'what do we actually know here', 'I want an honest read of our assumptions', 'separate what we know from what we're guessing', 'give me an epistemic audit', 'I want to stop conflating confident with correct', or when producing analysis where the confidence level matters as much as the content.

Overview

Publisherhuman-avatar
Repositoryskills-for-humanity
Skill names4h-epistemology-epistemic-status
Stars
234
Forks
24
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by human-avatar on GitHub. Read the source before you install it.

Installation

Install the S4h Epistemology Epistemic Status AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-epistemology-epistemic-status
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/human-avatar/skills-for-humanity.git /tmp/skills-for-humanity
mkdir -p .claude/skills
cp -r /tmp/skills-for-humanity/skills/s4h-epistemology-epistemic-status .claude/skills/s4h-epistemology-epistemic-status
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable S4h Epistemology Epistemic Status in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads S4h Epistemology Epistemic Status on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so S4h Epistemology Epistemic Status is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Epistemology: Epistemic Status

Most thinking conflates knowing with believing, believing with assuming, and assuming with hoping. These are not the same thing. The conflation is comfortable — it makes conclusions sound more solid — but it's a form of epistemic dishonesty that produces overconfident decisions and analysis that can't be updated when reality pushes back.

Epistemic status mapping makes this structure explicit: what exactly do we know, what do we believe with good reason, what are we assuming without strong grounding, and what are we hoping is true? The goal is not skepticism — it's honesty that produces better decisions.


Your Process

Step 1: Inventory All Claims in Play List every claim that the argument, plan, analysis, or domain rests on. Be exhaustive. Include:

  • Explicit claims (stated conclusions and premises)
  • Implicit claims (what has to be true for the explicit claims to hold)
  • Framing assumptions (what the question presupposes)
  • Value claims (what is treated as desirable or important)

Resist the urge to prune. The point is to surface everything before classifying anything.

Framing check: Confirm the specific domain being audited before continuing. State what you've identified — the actual argument, plan, or body of claims under examination and its central purpose — in one sentence, then use AskUserQuestion:

  • Question: "I'm reading this as: [your one-sentence framing of the specific content being audited]. 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: Assign Epistemic Status

Use this taxonomy:

StatusMeaningTest
KnownEstablished by strong evidence, replication, or logical necessityWould hold up under adversarial scrutiny from a well-informed skeptic
Reasonably believedWell-supported but not certain; evidence is good but not definitiveRational to act on; would update if strong contrary evidence appeared
AssumedTaken for granted without explicit verification; may or may not be trueCould be wrong; hasn't been checked; often invisible because it seems obvious
HopedBelieved partly because we want it to be trueMotivated reasoning may be distorting confidence; should be treated with extra skepticism
UnknownGenuinely unclear; no basis for confident assignmentThe honest answer is "we don't know"

Assign one status per claim. If you're unsure which status applies, that uncertainty is itself epistemic information — note it.

Step 3: Identify Dependency Chains Map which claims depend on which others. Then flag: where do high-confidence claims rest on lower-confidence foundations?

This is the critical finding. It's common for a conclusion labeled "known" to rest on a chain where one link is "assumed" or "hoped." The conclusion inherits the weakest status in its dependency chain.

Step 4: Audit for Status Inflation Review the inventory for common patterns of epistemic overconfidence:

  • Confidence laundering: an "assumed" claim is cited repeatedly until it feels established
  • Expertise elision: someone with authority asserted X, so X has been treated as "known" rather than "reasonably believed"
  • Motivated inflation: "hoped" claims that have quietly become "assumed" because acting on them is attractive
  • The invisible assumption: claims so deeply embedded they weren't listed in Step 1 — probe by asking "what would have to be true for this whole analysis to hold?"

Step 5: Flag High-Stakes Unknowns Which unknown or hoped claims are most load-bearing? If the thing you most need to be true turns out to be false, what breaks?

Before narrowing: Show the complete set of unknown and hoped claims to the user first. Use AskUserQuestion:

  • Question: "I've identified [N] unknown or hoped claims. Before I select the most load-bearing ones, are there any you'd flag as especially critical, or any I've missed?"
  • Header: "Prioritise"
  • Options:
    • Proceed with your selection — the set looks right
    • Flag one — user will name a specific claim to include
    • Add a missing one — user will describe it

These are the priority items for investigation, verification, or contingency planning.

Step 6: Produce the Map Synthesize into a structured output: the full inventory with statuses, the dependency structure, and the highest-priority epistemic gaps.


Human Check-in

Before proceeding, use the AskUserQuestion tool. State your interpretation of the situation in 1–2 sentences — what is being analyzed and what the core question is — then ask:

  • Question: "My read: [your 1–2 sentence interpretation]. How do you want to proceed?"
  • Header: "Scope"
  • Options:
    • Full audit — Complete inventory, status for every claim, dependency map, status inflation audit
    • Top-level only — Classify only the main claims, skip dependency tracing
    • Assumptions only — Surface what's being taken for granted; skip known/believed claims
    • Refine the domain — Sharpen what we're auditing before starting

Proceed based on their selection. If the user reframes, incorporate the correction before running any analysis.


Output Format

Domain

[What is being audited]

Epistemic Status Map

ClaimStatusNotes
[Claim 1]Known / Reasonably Believed / Assumed / Hoped / Unknown[Why this status; what would change it]
[Claim 2]......
...

Dependency Flags

High-confidence claims resting on lower-confidence foundations:

  • [Confident claim] (status: Known/Believed) rests on [foundational claim] (status: Assumed/Hoped) — [why this matters]
  • [Add more as needed]

Status Inflation Found

  • [Pattern identified, e.g., confidence laundering / expertise elision] — [Specific instance and what to do about it]
  • (None — if absent)

High-Stakes Unknowns

Unknown / Hoped ClaimWhy It's Load-BearingPriority
[Claim][What depends on it]High / Medium / Low

Summary

[One paragraph: what is the honest picture of what's known vs. assumed in this domain, and what are the 1-2 highest priority epistemic gaps to address?]


Notes

This skill maps confidence across a domain — use epistemology-justification to go deep on the structure of a single belief's support chain. Use epistemology-limits when a claim is unknown not due to lack of investigation but due to a fundamental or structural limit on what can be established. Use probability when the goal is to quantify uncertainty numerically rather than categorize it epistemically.


What's Next

After delivering this output, use AskUserQuestion to offer the next move:

  • Question: "Epistemic status assigned. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-epistemology-limits — Map where the knowledge runs out beyond the current status
    • /s4h-probability-confidence-calibration — Calibrate expressed confidence to match epistemic status
    • /s4h-investigation-evidence-audit — Audit the evidence underpinning the lowest-status claims
    • Done — Wrap up and synthesise what we have so far

Frequently asked questions

What does the S4h Epistemology Epistemic Status AI skill do?

Produces an honest, rigorous calibration of what you know vs. believe vs. assume vs. hope across a domain. Assigns explicit epistemic statuses to claims and flags when high-confidence claims rest on lower-confidence foundations. Draws from the rationalist tradition of explicit epistemic labeling. Use when you say 'how certain should I be', 'what do we actually know here', 'I want an honest read of our assumptions', 'separate what we know from what we're guessing', 'give me an epistemic audit', 'I want to stop conflating confident with correct', or when producing analysis where the confidenc...

Why use S4h Epistemology Epistemic Status on TypingMind?

Because you install it once and use it with any model. S4h Epistemology Epistemic Status is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install S4h Epistemology Epistemic Status in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-epistemology-epistemic-status. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use S4h Epistemology Epistemic Status?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with S4h Epistemology Epistemic Status?

As many as you like. As long as a model supports skills, you can use S4h Epistemology Epistemic Status with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the S4h Epistemology Epistemic Status AI skill free?

Yes. It is published on GitHub by human-avatar under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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