# Removing Emphasis Crutches from AI-Generated Content with Stop-Slop

> Remove AI emphasis crutches from content using Stop Slop. This data-only skill injects markdown rulebooks to enhance your AI writing. Learn more!

- Repository: [Hardik Pandya/stop-slop](https://github.com/hardikpandya/stop-slop)
- Tags: how-to-guide
- Published: 2026-05-26

---

**Stop-Slop is a data-only editorial skill that strips emphasis crutches and AI-writing artifacts by injecting curated markdown rulebooks directly into an LLM’s system prompt or post-processing pipeline.**

Removing emphasis crutches from AI-generated content requires more than simple keyword filtering—it demands structured editorial intelligence. The **hardikpandya/stop-slop** repository provides a portable, code-free "skill" that eliminates predictable LLM prose habits—including throat-clearing openers, business jargon, and filler constructions—by supplying eight core editorial rules and comprehensive reference dictionaries that any model can consume.

## Core Architecture and File Structure

Stop-Slop is organized as a self-contained knowledge package containing zero executable code. Every component is a markdown file describing editorial policy, making it compatible with any LLM platform that supports system-prompt injection or file-based knowledge imports.

### The Master Rule Set ([`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md))

The central instruction set resides in [`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md), which enumerates **eight high-level editorial rules** designed to detect and remove emphasis crutches. The file contains:

- Rules 1-8 covering active voice enforcement, filler phrase elimination, and formulaic structure breaking
- A **quick-check checklist** for real-time evaluation (e.g., "Any adverbs? Kill them.")
- A **scoring rubric** where outputs scoring below **35/50** require revision

### Reference Libraries

Three specialized dictionaries in the `references/` directory provide the lexical and structural inventory for identifying AI slop:

- **[`references/phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/phrases.md)**: A concrete list of emphasis crutches and filler phrases to delete, including entries like `"Here's the thing:"`, `"Full stop."`, and `"lean into"`
- **[`references/structures.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/structures.md)**: Pattern catalogue of prose constructions to avoid, such as binary contrasts (`"Not because X. Because Y."`), dramatic fragmentation, and false agency
- **[`references/examples.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/examples.md)**: Before-and-after transformations illustrating how rules apply to real text

## How the Skill Works in an LLM Pipeline

The cleaning process follows a five-step evaluation cycle:

1. **Load Core Rules**: Inject [`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md) into the system prompt or knowledge base
2. **Reference Consultation**: Direct the LLM to check [`phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/phrases.md) and [`structures.md`](https://github.com/hardikpandya/stop-slop/blob/main/structures.md) when triggered
3. **Quick-Check Execution**: Evaluate each sentence against the checklist during generation
4. **Compliance Scoring**: Run the draft against the scoring table; revise if below 35/50
5. **Iterative Refinement**: Continue rewriting until the threshold is met

Because the repository supplies both **low-level lexical items** (specific crutch words) and **high-level structural patterns** (rhetorical constructions), the LLM performs surgical edits without over-pruning meaning.

## Three Ways to Implement Stop-Slop

### System Prompt Injection

For API-driven workflows, fetch [`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md) at runtime and prepend it to your system prompt:

```python
import requests

# Load the skill dynamically

skill_url = "https://raw.githubusercontent.com/hardikpandya/stop-slop/main/SKILL.md"
skill_content = requests.get(skill_url).text

system_prompt = f"""
You are Claude. Load the Stop-Slop skill:

---BEGIN SKILL---
{skill_content}
---END SKILL---

When you generate text, obey all rules in the skill.
"""

# Send to your LLM client

response = claude_api.generate(
    system_prompt=system_prompt,
    user_prompt="Explain the benefits of micro-frontends."
)

```

### Claude Projects Knowledge Base

For persistent access across multiple conversations:

1. Clone the repository locally
2. Upload [`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md) and the entire `references/` directory to your Claude Project’s **knowledge** section
3. Reference the skill in your system prompt: `Load skill from project: stop-slop`

Claude automatically reads the files and applies the rules during generation, consulting the emphasis crutch lists in [`phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/phrases.md) when drafting responses.

### Offline Post-Processing

For batch cleaning without API costs, apply the lexical rules directly using Python:

```python
import re
import pathlib

def remove_emphasis_crutches(text: str) -> str:
    """Strip known crutches using the phrase inventory."""
    phrases = pathlib.Path("references/phrases.md").read_text()
    
    for line in phrases.splitlines():
        if line.startswith("- "):
            phrase = re.escape(line[2:].strip('"'))
            text = re.sub(r'\b' + phrase + r'\b', '', text, flags=re.I)
    
    # Clean up residual whitespace

    text = re.sub(r'\s{2,}', ' ', text)
    return text.strip()

raw = """Here's the thing: building products is hard. Not because the technology is complex. Because people are complex. Let that sink in."""
clean = remove_emphasis_crutches(raw)

print(clean)

# Output: "building products is hard. technology is complex. people are complex."

```

This method leverages the [`references/phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/phrases.md) inventory to remove emphasis crutches without invoking an LLM, ideal for high-volume content pipelines.

## Summary

- **Stop-Slop** is a data-only editorial skill residing in the `hardikpandya/stop-slop` repository, distributed under the MIT license
- The system relies on **[`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md)** for eight core rules and a 50-point scoring rubric where scores below 35 trigger revision
- **Emphasis crutches** are catalogued in [`references/phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/phrases.md), while structural patterns live in [`references/structures.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/structures.md)
- Implementation requires no code execution—only markdown injection into system prompts or knowledge bases
- Offline processing is possible by regex-matching against the phrase inventories for API-free content cleaning

## Frequently Asked Questions

### What exactly are emphasis crutches in AI writing?

Emphasis crutches are predictable rhetorical tics that LLMs use to simulate conviction or transition, such as `"Here's the thing:"`, `"Full stop."`, or `"Let that sink in."` According to the Stop-Slop source code, these phrases function as filler that signals AI authorship without adding semantic value, and they are catalogued for removal in [`references/phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/phrases.md).

### How does the Stop-Slop scoring rubric work?

The rubric in [`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md) assigns points based on compliance with eight editorial rules, including active voice usage and absence of throat-clearing openers. A score of **35 out of 50** is the minimum threshold; anything below requires iterative revision until the text meets the cleanliness standard.

### Can I use Stop-Slop with models other than Claude?

Yes. Because the repository is **data-only** markdown with no executable dependencies, you can inject [`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md) into any LLM supporting system prompts—including GPT-4, Llama, or Gemini—or use the phrase lists for offline regex processing in Python.

### What is the difference between [`phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/phrases.md) and [`structures.md`](https://github.com/hardikpandya/stop-slop/blob/main/structures.md)?

[`phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/phrases.md) contains **lexical** emphasis crutches—specific words and short phrases to delete—while [`structures.md`](https://github.com/hardikpandya/stop-slop/blob/main/structures.md) catalogs **higher-order prose patterns** like binary contrasts (`"Not because X. Because Y."`) and dramatic fragmentation that require rewriting rather than simple deletion.