Agent skill

Epistemic Classification

by Abilityai in Abilityai/cornelius

Framework for distinguishing research findings from hypotheses and speculative synthesis.

MITAuto-check passed

Install Epistemic Classification

skills CLI
$ npx skills add Abilityai/cornelius --skill epistemic-classification -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Abilityai/cornelius epistemic-classification --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Abilityai/cornelius.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/epistemic-classification .claude/skills/epistemic-classification && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
epistemic-classification
GitHub stars
109
Token cost
~1.5k tokens
SKILL.md length
629 words
Files
1
Skills in repo
51
Repo updated
First seen
Licence
MIT

At a glance

Framework for distinguishing research findings from hypotheses and speculative synthesis.

  • Works in 5 steps: Confirmed Research Findings → Theoretical Frameworks → Working Hypotheses → …
  • Extracting insights from research
  • SKILL.md covers Classification Levels, Mandatory Labeling for…, Examples and Intellectual Honesty Principle, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Epistemic Classification is an agent skill from Abilityai/cornelius. Framework for distinguishing research findings from hypotheses and speculative synthesis. Use when extracting insights from research, creating notes from external sources, or classifying the epistemic status of claims.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: AI-powered second brain template for Claude Code + Obsidian. The licence is MIT.

When your agent uses it

  • Extracting insights from research
  • Creating notes from external sources
  • Classifying the epistemic status of claims

Example prompts

  • “/epistemic-classification”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Confirmed Research Findings
  2. Theoretical Frameworks
  3. Working Hypotheses
  4. Speculative Synthesis
  5. Research Gaps

What it can do on your machine

Read from SKILL.md and the folder at commit fd5e9a4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Epistemic Classification loads about 1.5k tokens when it runs. Until then it costs about 61 tokens; SKILL.md has 629 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Abilityai/cornelius at commit fd5e9a4, republished under its MIT licence (© Abilityai). 629 words, ~1,507 tokens.

Download SKILL.mdSave it as .claude/skills/epistemic-classification/SKILL.md (or your agent's skills folder).
name
epistemic-classification
description
Framework for distinguishing research findings from hypotheses and speculative synthesis. Use when extracting insights from research, creating notes from external sources, or classifying the epistemic status of claims.
user-invocable
false

Epistemic Classification Framework

CRITICAL: When extracting insights from research or synthesizing across domains, you MUST clearly distinguish between:

Classification Levels

1. Confirmed Research Findings

Empirically validated, peer-reviewed, replicated

  • Tag: #research-finding or #empirical-evidence
  • Language: "Research shows...", "Studies confirm...", "Evidence demonstrates..."
  • Requirement: Source citation with publication year and journal
2. Theoretical Frameworks

Established models with strong theoretical backing

  • Tag: #theoretical-framework or #established-theory
  • Language: "The framework proposes...", "Theory suggests...", "Model predicts..."
  • Note: Level of acceptance in field
3. Working Hypotheses

Testable propositions not yet validated

  • Tag: #hypothesis or #testable-hypothesis
  • Language: "A possible mechanism...", "This suggests...", "One hypothesis..."
  • Mark as: "HYPOTHESIS:" in note title or frontmatter
  • Include: What would validate/falsify this hypothesis
4. Speculative Synthesis

Original connections or interpretations

  • Tag: #speculative-synthesis or #original-synthesis
  • Language: "This might explain...", "A potential connection...", "Speculatively..."
  • State clearly: "This is synthesis/interpretation, not established fact"
  • Confidence level: Low (20-40%), Medium (40-70%), High (70-90%)
5. Research Gaps

Identified missing connections in literature

  • Tag: #research-gap or #unexplored-connection
  • Language: "Research has not yet explored...", "Gap identified..."
  • Note: Why this gap matters

Mandatory Labeling for Hypotheses

When creating notes containing hypotheses or speculative synthesis:

markdown
---
title: [Title] (HYPOTHESIS) or [Title]
type: hypothesis / speculative-synthesis / working-theory
status: untested / under-investigation / partially-supported
confidence: low / medium / high
tags: #hypothesis #topic
---

**STATUS: HYPOTHESIS - NOT CONFIRMED BY RESEARCH**

[Content of hypothesis]

## Testable Predictions
[What would validate this]

## Current Evidence
[Supporting indirect evidence]

## Research Needed
[What studies would test this]

Examples

✅ GOOD
  • "Dopamine May Modulate Interoceptive Precision Weighting (HYPOTHESIS)"
  • Type: speculative-synthesis
  • Status: untested
  • Confidence: medium
  • Clear statement: "This is an original synthesis filling a research gap"
❌ BAD
  • "Dopamine Modulates Interoceptive Precision" (stated as fact)
  • No hypothesis tag
  • No confidence level
  • Presented as established finding

Intellectual Honesty Principle

Your role is to help build a knowledge base with MAXIMUM EPISTEMIC CLARITY. Users must be able to trust the distinction between:

  • What science has proven
  • What theory predicts
  • What remains speculative
  • What is original synthesis

Never present hypotheses as facts. Never obscure the difference between research and speculation. Intellectual rigor requires epistemic humility.


Authorship Provenance (a second, orthogonal axis)

The classification levels above tag a claim's truth status (how proven it is). Provenance is a separate, independent axis that tags who authored the thinking. Both must be carried - a claim can be high-confidence-empirical and merely encountered (a peer-reviewed finding read but not endorsed); it can be low-confidence-speculative and originated (the user's uncorroborated gem).

Set the provenance: frontmatter field on every knowledge note:

ValueMeaning
originatedthe user's own original thinking - the cognitive fingerprint. The asset.
endorsedExternal idea the user explicitly adopted as his own view.
encounteredRead and recorded, not yet endorsed. Default for external-document extractions.
ai-inferredAn agent's own synthesis/inference. Must stay tagged so it never wears the user's voice.
Show full SKILL.md (229 more words)Show less

Two guardrails (non-negotiable):

  1. Never demote an originated insight toward consensus. Uncorroborated + originated = the gem, not a defect. A second brain that regresses its owner's thinking to the consensus mean has destroyed its reason to exist. (This is the inversion that makes this knowledge base not an oracle.)
  2. Measure provenance; never adjudicate originality. Tiering and tagging are human-reviewable signals. Do not silently decide what is "true" or rewrite who authored a thought.

The guarded boundary: nothing crosses encountered -> endorsed without an explicit endorsement act by the user. When new external input contradicts an existing note, create a tension/contrast link - do not overwrite. Contradictions are synthesis fuel for detect-tensions, not errors to correct.


Gate 1: Source Tiering at Ingestion

Before extracting from any external source, tier it and reject slop at the door. Stamp the tier on the note's frontmatter (source-tier:) so trust is recorded, not buried.

The per-domain source diet - which sources to trust, demote, or reject - is the canonical resources/SOURCE-AUTHORITY.md. Consult it when tiering. Keep that file as the single source of truth; do not maintain a competing list here.

Tier values for source-tier: primary (the paper/text/lab itself) | credible-interpreter (a trusted secondary reading it faithfully) | rejected (content-farm / AI-generated / regurgitation - do not extract).

Cross-domain rule (applies to any domain): prefer the primary over anyone summarizing it; reject machine-generated and content-farm material before extraction; record the tier on ingestion.

© Abilityai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/epistemic-classification of Abilityai/cornelius.

Open the folder on GitHubat commit fd5e9a4

Compare with similar skills

Epistemic Classification next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Epistemic Classification compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Epistemic Classification this skillAbilityai/cornelius109—~1.5kAutomated safety check: PassMIT
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Fd Findpenpot/penpot61k—~882Automated safety check: PassMPL-2.0
Speculative Namingsgl-project/sglang37k2 repos~1.6kAutomated safety check: PassApache-2.0
Hypothesesdavepoon/buildwithclaude3.6k—~2kAutomated safety check: PassMIT
Find Matching Tenderssickn33/agentic-awesome-skills47k1 repos~1kAutomated safety check: PassApache-2.0

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Questions about Epistemic Classification

What does Epistemic Classification do?

Framework for distinguishing research findings from hypotheses and speculative synthesis. Epistemic Classification is an agent skill from Abilityai/cornelius. Framework for distinguishing research findings from hypotheses and speculative synthesis.

When should I use Epistemic Classification?

Epistemic Classification fits situations like: extracting insights from research; creating notes from external sources; classifying the epistemic status of claims.

How do I install Epistemic Classification in Claude Code?

Run `npx skills add Abilityai/cornelius --skill epistemic-classification -a claude-code`. Or copy the skill folder (.claude/skills/epistemic-classification in Abilityai/cornelius) into .claude/skills/epistemic-classification in your project. Claude Code loads it when a task matches its description.

How do I install Epistemic Classification in Codex?

Run `npx skills add Abilityai/cornelius --skill epistemic-classification -a codex`. Or copy the skill folder (.claude/skills/epistemic-classification in Abilityai/cornelius) into .agents/skills/epistemic-classification in your project. Codex loads it when a task matches its description.

Can I use Epistemic Classification in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Abilityai/cornelius --skill epistemic-classification -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/epistemic-classification, .gemini/skills/epistemic-classification, .github/skills/epistemic-classification and .opencode/skills/epistemic-classification in your project.

What does Epistemic Classification need to run?

SKILL.md names no scripts, command-line tools or credentials: Epistemic Classification is instructions for the agent only.

Does Epistemic Classification access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Epistemic Classification safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Epistemic Classification use?

Epistemic Classification is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Epistemic Classification use?

About 1.5k tokens (SKILL.md is roughly 6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Epistemic Classification?

Skills that share tags, products or a category with Epistemic Classification: Find Release (flutter/flutter, 179k stars), Fd Find (penpot/penpot, 61k stars), Speculative Naming (sgl-project/sglang, 37k stars) and Hypotheses (davepoon/buildwithclaude, 3.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Epistemic Classification?

Abilityai (a GitHub organization) maintains it in Abilityai/cornelius, which has 109 GitHub stars. The repository holds 51 skills in this directory. The repository was last updated on September 22, 2026.

Source: Abilityai/cornelius on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.