llms.txt git:20260424.b363a74A

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# hermes-blind

> Context-compensation scaffold for LLM evaluation prompts. A ~40-token string prepended to a scoring prompt to force disclosure of prior exposure, evidence-gated scoring, and hedging on thin evidence. Backend-agnostic. Complementary to claude --bare.

## What it does

Prepends a versioned language scaffold to the caller's LLM prompt. The scaffold
forces the model to:

1. State any prior exposure to the target or its author in one line
2. Score using only quoted evidence from the target text
3. Hedge on thin or absent evidence rather than confabulate confident scores

The scaffold is a string constant. No API, no model, no network. Pure
standard-library Python.

## Install

    pip install hermes-blind

## Python

    from hermes_blind import wrap, extract_disclosure, VARIANTS

    prompt = wrap("Score this paper 0-10.", variant="v1")
    response = your_model_call(prompt)
    disclosure = extract_disclosure(response)  # or None

## Variants

- null   — 0 tokens; control variant, wrap is a no-op
- micro  — ~8 tokens; minimum viable
- short  — ~18 tokens; tight token budgets
- v1     — ~40 tokens; default, four-mechanism scaffold
- full   — ~80 tokens; adds output-shape discipline

Strict length ordering is a tested invariant.

## When to use

- Any LLM scoring or evaluation pipeline where the scorer may have
  session-context or preference knowledge of the target
- Cross-backend ablation where `--bare` mode is unavailable (Ollama, OpenAI,
  in-session scoring)
- Self-scoring contexts where the scorer authored the target

## When not to use

- Generation debiasing (v0.0.x tested only for scoring)
- Long-target scoring >10k tokens (unvalidated)
- Multi-turn dialogue debiasing
- Non-English prompts
- Cases requiring guaranteed bias elimination (scaffold is statistical)

## Status

v0.0.6 — experimental. The empirical variance-reduction test has **not** been
run. The package is structurally complete (19/19 unit tests green, deterministic)
but the core hypothesis — that the scaffold actually reduces score variance — is
measured by Phase 4 of a staged build. See PLAN-v2.md for the test protocol.

## Related

- hermes-rubric — evidence-first structured scoring tool; hermes-blind plugs
  into the prompt layer below it
- hermes-seal — cryptographic attestation for agent policy artifacts
- claude --bare — stronger isolation primitive for claude-cli specifically;
  complementary, not a substitute

## Repo

https://github.com/hermes-labs-ai/hermes-blind