# How Is the Effectiveness of No‑AI‑Slop Measured? A Technical Deep Dive

> Discover how no-ai-slop effectiveness is measured using a pass/fail checklist that tests meaning preservation, voice fidelity, and cutting efficiency against boolean criteria.

- Repository: [Peter Yang/no-ai-slop](https://github.com/petergyang/no-ai-slop)
- Tags: deep-dive
- Published: 2026-09-11

---

**The No‑AI‑Slop skill evaluates effectiveness through a rigorous pass/fail checklist defined in [`eval.md`](https://github.com/petergyang/no-ai-slop/blob/main/eval.md), testing every output against three core dimensions—preservation of meaning, voice fidelity, and efficiency of cutting—until all boolean criteria pass.**

The open‑source **No‑AI‑Slop** skill, maintained in the `petergyang/no-ai-slop` repository, provides a systematic approach to identifying and removing AI‑generated filler from human writing. Unlike heuristic style checkers, this tool measures its own success through an internal validation loop defined in concrete source files, ensuring every edit or detection meets strict, codified standards.

## The Three Core Dimensions of Measurement

The measurement framework evaluates every operation across three distinct quality dimensions defined in the skill’s architecture.

### Preservation of Meaning

Checks whether the edit retains the original point without injecting new claims, examples, or opinions. This boolean test ensures the writer’s intent remains intact throughout the editing process.

### Voice Fidelity

Validates that the output preserves the writer’s distinctive vocabulary, cadence, humor, uncertainty, and overall polish. This prevents the "flattening" effect typical of AI‑generated text.

### Efficiency of Cutting

Verifies that the amount of cutting remains proportional to actual slop patterns, ensuring strong human sentences remain untouched. The test confirms the edit removes only targeted AI‑slop patterns and nothing more.

## The Evaluation Checklist in [`eval.md`](https://github.com/petergyang/no-ai-slop/blob/main/eval.md)

The concrete implementation of these dimensions resides in [[`skills/no-ai-slop/eval.md`](https://github.com/petergyang/no-ai-slop/blob/main/skills/no-ai-slop/eval.md)](https://github.com/petergyang/no-ai-slop/blob/main/skills/no-ai-slop/eval.md). This file enumerates **11 concrete editing principles**—such as "Use active voice," "Make every sentence earn its place," and "Apply the portability test"—alongside specific pattern checks for binary contrasts, throat‑clearing openers, and faux‑insight setups.

Each principle operates as a **boolean test**. The skill reports a **"pass"** if the edited draft satisfies the rule and a **"fail"** otherwise. When any check fails, the skill automatically revises the draft and re‑runs the full suite until **all checks pass**, creating a closed feedback loop that serves as the primary metric of effectiveness.

## Operational Modes and Validation Workflows

The skill supports two distinct execution modes, each with specific measurement applications.

### Detection Mode (Evaluation Only)

When running a detection request, the skill applies pass/fail logic only to detection criteria without rewriting the text.

Example command:

```text
/no-ai-slop is this slop?
[User’s draft]

```

The skill returns a list of detected patterns, each quoting the offending line and suggesting a short fix. Effectiveness is measured by the accuracy of these identifications against the [`eval.md`](https://github.com/petergyang/no-ai-slop/blob/main/eval.md) pattern definitions.

### Edit Mode (Full Validation)

In edit mode, the skill performs the rewrite and then executes the complete [`eval.md`](https://github.com/petergyang/no-ai-slop/blob/main/eval.md) checklist iteratively.

Example command:

```text
/no-ai-slop
[User’s draft]

```

After editing, the skill validates the output against all criteria. If checks fail, it iterates until compliance is achieved, then outputs:

```markdown
[Edited draft]

**What changed**
- Removed binary contrast: "It’s not X. It’s Y."
- Cut throat-clearing opener: "Here’s the thing,"
- …

```

The **"What changed"** section serves as a secondary, human‑readable measure of success, documenting specific transformations applied.

## Key Files That Define the Measurement System

Several source files work together to implement the effectiveness measurement system:

- **[`SKILL.md`](https://github.com/petergyang/no-ai-slop/blob/main/SKILL.md)**: Declares the skill’s purpose, defines the two job modes (edit versus detect), and documents the full set of editing principles that form the measurement baseline.
- **[`eval.md`](https://github.com/petergyang/no-ai-slop/blob/main/eval.md)**: Contains the concrete pass/fail checklist that quantifies effectiveness after each run, implementing the three core dimensions as boolean tests.
- **[`plugin.json`](https://github.com/petergyang/no-ai-slop/blob/main/plugin.json)**: Provides metadata required for ChatGPT/Codex to expose the `/no-ai-slop` command, ensuring the evaluation logic is accessible through the plugin interface.
- **[`build_plugin.py`](https://github.com/petergyang/no-ai-slop/blob/main/build_plugin.py)**: Builds and validates the plugin package, ensuring the evaluation logic from [`eval.md`](https://github.com/petergyang/no-ai-slop/blob/main/eval.md) is correctly bundled and executable.

## Summary

- **No‑AI‑Slop** measures effectiveness through a systematic, rule‑based validation loop defined in [`eval.md`](https://github.com/petergyang/no-ai-slop/blob/main/eval.md).
- Three core dimensions—**preservation of meaning**, **voice fidelity**, and **efficiency of cutting**—provide the qualitative framework.
- **11 concrete editing principles** are implemented as boolean pass/fail tests that must all pass before output is accepted.
- The skill operates in two modes: **detection** (analysis only) and **edit** (full validation with iterative revision).
- Success requires 100% pass rate on the checklist, with the **"What changed"** section providing transparent documentation of modifications.

## Frequently Asked Questions

### What is the primary metric for no-ai-slop effectiveness?

The primary metric is the **pass/fail rate** against the comprehensive checklist in [`eval.md`](https://github.com/petergyang/no-ai-slop/blob/main/eval.md). A successful run requires every boolean test to pass, ensuring the output adheres to all 11 editing principles and three core dimensions before delivery.

### How does the skill handle failed checks during editing?

When any check fails, the skill automatically triggers a revision cycle. It rewrites the draft and re‑runs the full [`eval.md`](https://github.com/petergyang/no-ai-slop/blob/main/eval.md) checklist iteratively until **all checks pass**, creating a closed feedback loop that guarantees compliance with the measurement standards.

### What is the difference between detection and edit mode in terms of measurement?

In **detection mode**, the skill applies pass/fail logic only to identify slop patterns without modifying text, measuring effectiveness by identification accuracy. In **edit mode**, the skill must satisfy the complete validation suite including preservation of meaning and voice fidelity, measuring effectiveness by the quality of the final rewritten output.

### Where are the evaluation rules defined in the repository?

The evaluation rules reside in [[`skills/no-ai-slop/eval.md`](https://github.com/petergyang/no-ai-slop/blob/main/skills/no-ai-slop/eval.md)](https://github.com/petergyang/no-ai-slop/blob/main/skills/no-ai-slop/eval.md), while the high‑level skill definition and principle documentation are located in [[`skills/no-ai-slop/SKILL.md`](https://github.com/petergyang/no-ai-slop/blob/main/skills/no-ai-slop/SKILL.md)](https://github.com/petergyang/no-ai-slop/blob/main/skills/no-ai-slop/SKILL.md).