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episteme

The epistemic provenance and audit layer, used to determine the origin and reliability of claims.

Skill by TylerGarlick

IndexedMIT★ 5
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

The epistemic provenance and audit layer, used to determine the origin and reliability of claims.

Use cases: security

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What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.

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What's inside

Skill name
episteme
Description
The epistemic provenance and audit layer, used to determine the origin and reliability of claims.
License
MIT
Instructions
46 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Episteme Skill

Episteme provides tools for epistemic tracing and auditing. It allows the system to distinguish between direct memory, retrieved knowledge, derived reasoning, and training-set artifacts, ensuring that the provenance of any given claim is transparent and verifiable.

## Core Capabilities

The skill enables the "epistemic labeling" of outputs, which is critical for the Janus system to prevent hallucinations and identify confabulations.

### Epistemic Provenance
Every claim can be categorized into one of five origin codes:
- `[DIR]`: Direct (Parametric Memory) - Knowledge inherent to the model's weights.
- `[INF]`: Inferred (Reasoning Chain) - Knowledge derived via logic or step-by-step inference.
- `[RET]`: Retrieval (Sovereign Vault) - Knowledge retrieved from the system's secure memory.
- `[ART]`: Artifact (Training Pattern) - Responses triggered by common LLM training-set tropes.
- `[CONF]`: Confabulated (No Grounding) - Claims with no verifiable origin.

## Commands

### `episteme_trace`
Analyzes a claim and its context to determine its origin.
- **Arguments**: `claim` (required).
- **Behavior**: 
    1. Checks the Sovereign Vault for matching fragments (`[RET]`).
    2. Checks the Epistemic Ledger for recorded entries (`[DIR]` or `[INF]`).
    3. Checks for known LLM artifact patterns (`[ART]`).
    4. Defaults to parametric memory (`[DIR]`).

### `episteme_audit`
Analyzes session logs to detect noise and epistemic drift.
- **Arguments**: `session_logs` (required).
- **Behavior**: 
    - Counts occurrences of "AI language model" artifacts.
    - Detects "Epistemic Drift" (transitions from Retrieval to Inference without a bridge).
    - Reports stability status (`Stable` vs `High Noise`).

## Implementation Details

- **Architecture**: Two-tier Python implementation (FastMCP $\rightarrow$ EpistemeLogic).
- **Backend**: Reads from `sovereign_vault.json` and `epistemic-ledger.json`.
- **Pattern Matching**: Uses regular expressions for artifact and drift detection.

## MCP tools

Available MCP tools: `episteme_trace`, `episteme_audit`.

From TylerGarlick/abraxas, MIT.

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Last commit (this folder)
Oct 22, 2026 (7 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

episteme skill. Agent Mart. https://agentmart.dev/skills/tylergarlick-episteme (last updated 2026-10-24)