# How to Eliminate Business Jargon and Find Replacements Using Stop‑Slop

> Eliminate business jargon with Stop-Slop. This tool replaces corporate buzzwords with plain language, enforcing clarity and active voice for better communication.

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

---

**Stop‑Slop is a prompt‑driven skill that scans text for corporate buzzwords and replaces them with plain‑language alternatives defined in [`references/phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/phrases.md), enforcing clarity through active voice and structural rules.**

Business jargon obscures meaning and erodes trust in professional communication. `hardikpandya/stop-slop` provides a lightweight, file‑based "skill" designed to strip AI‑generated writing habits—especially formulaic corporate language—and substitute concise, human‑centered phrasing. By integrating this repository into Claude or other LLM workflows, you can automate eliminating business jargon without writing custom transformation logic.

## What Is the Stop‑Slop Skill?

The Stop‑Slop repository functions as a declarative instruction set for large language models. Rather than relying on hardcoded parsers, the skill uses markdown files to define behavioral rules that the LLM reads and applies during inference.

The architecture consists of four primary components:

- **[`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md)** – The core system prompt containing eight high‑level rules (cut filler, break structures, enforce active voice) that direct the model’s editing behavior.
- **[`references/phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/phrases.md)** – A mapping table that pairs banned corporate buzzwords (*"lean into,"* *"deep dive,"* *"game‑changer"*) with concise alternatives (*"embrace,"* *"analysis,"* *"significant"*). This file also catalogs throat‑clearing openers, adverbial clutter, and meta‑commentary to strip.
- **[`references/structures.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/structures.md)** – A catalog of structural clichés including binary contrasts, negative listings, dramatic fragmentation, and passive‑voice constructions, each paired with rewrite recommendations.
- **[`references/examples.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/examples.md)** – Real‑world before‑and‑after samples demonstrating the skill’s transformation logic.

Because the rules are expressed in plain markdown, the skill is language‑agnostic and compatible with any downstream tool supporting custom system prompts, including LangChain, LlamaIndex, or direct API calls.

## How Jargon Removal Works

### Detection via Phrase Mapping

The skill scans input text for any phrase appearing in the left column of the "Avoid / Use instead" table located in [`references/phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/phrases.md). When the model encounters matches like *"double down"* or *"circle back,"* it flags them for replacement.

### Replacement and Validation

Upon detection, the LLM substitutes the jargon with the corresponding plain‑language suggestion from the right column. The surrounding rules in [`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md) then validate that the replacement maintains active voice, specificity, and natural rhythm, preventing the introduction of new filler or passive constructions.

## Three Ways to Deploy Stop‑Slop

### Claude Project Import

Upload the entire `stop-slop/` folder to a Claude project. Claude automatically loads the reference files on demand, applying the rules to all subsequent prompts within that project context.

### System Prompt Injection

Copy the full contents of [`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md) (and optionally the replacement tables from [`phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/phrases.md)) directly into your system prompt. This method requires no external file dependencies and works with any chat interface allowing custom instructions.

### API Integration

Include [`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md) as the system message when calling the Claude API. The model obeys the rules without additional code, making it ideal for production pipelines where you need eliminating business jargon at scale.

## Implementation Examples

### 1. Claude API System Prompt

```json
{
  "model": "claude-3-opus-20240229",
  "messages": [
    {
      "role": "system",
      "content": "<!-- Insert the full content of SKILL.md here -->\n---\n# Stop Slop\n\nEliminate predictable AI writing patterns from prose.\n\n## Core Rules\n1. **Cut filler phrases.** See references/phrases.md.\n2. **Break formulaic structures.** See references/structures.md.\n…"

    },
    {
      "role": "user",
      "content": "We need to **lean into** the data and **deep dive** into the market trends. The **game‑changer** here is our ability to **double down** on customer insights."
    }
  ]
}

```

**Result:**

> We need to **handle** the data and **examine** the market trends. The **significant** point here is our ability to **increase** focus on customer insights.

### 2. LangChain Integration

```python
from langchain.chat_models import ChatAnthropic
from langchain.prompts import ChatPromptTemplate

# Load SKILL.md into a string

skill_prompt = open("SKILL.md").read()

prompt = ChatPromptTemplate.from_messages([
    ("system", skill_prompt),
    ("human", "{text}")
])

chain = prompt | ChatAnthropic(model="claude-3-sonnet-20240229")
clean = chain.invoke({"text": """
Our next step is to **lean into** the upcoming release and **double down** on our marketing spend. 
This will be a **game‑changer** for the brand.
"""})
print(clean.content)

```

**Output:**

> Our next step is to **embrace** the upcoming release and **increase** our marketing spend. This will be a **significant** development for the brand.

### 3. Manual Jargon Lookup

```python
import csv
from pathlib import Path

# Load the mapping from phrases.md

mapping = {}
with open(Path("references/phrases.md")) as f:
    for line in f:
        if line.startswith("|"):
            parts = [p.strip() for p in line.strip("|\n").split("|")]
            if len(parts) == 2 and parts[0] and parts[1]:
                mapping[parts[0].lower()] = parts[1]

def replace_jargon(text: str) -> str:
    for bad, good in mapping.items():
        text = text.replace(bad, good)
    return text

sample = "We need to lean into the data and deep dive into the market."
print(replace_jargon(sample))

```

**Result:**

> We need to **handle** the data and **examine** the market.

## Core Files Driving the Skill

| File | Purpose |
|------|---------|
| [`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md) | Defines the eight core rules and references external files for detailed guidance. |
| [`references/phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/phrases.md) | Contains the "Avoid / Use instead" table mapping buzzwords to plain alternatives. |
| [`references/structures.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/structures.md) | Lists structural clichés (binary contrasts, passive voice) with rewrite strategies. |
| [`references/examples.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/examples.md) | Provides before/after samples demonstrating the skill’s editing logic. |

## Summary

- **Stop‑Slop** eliminates business jargon through a prompt‑driven architecture rather than hardcoded parsers.
- The **[`references/phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/phrases.md)** file serves as the central dictionary for finding replacements to corporate buzzwords.
- Deployment options include Claude Projects, direct system prompt injection, and API integration with any LLM supporting system messages.
- The skill validates replacements against active voice and specificity rules to ensure natural output.

## Frequently Asked Questions

### What specific types of business jargon does Stop‑Slop target?

The skill targets four categories: corporate buzzwords like *"synergy"* and *"move the needle,"* throat‑clearing openers such as *"It is important to note that,"* adverbial clutter including *"very"* and *"really,"* and meta‑commentary like *"As an AI language model."* It also flags structural clichés defined in [`references/structures.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/structures.md).

### Can I use Stop‑Slop with GPT‑4 or other non‑Claude models?

Yes. Because the skill consists of plain markdown instructions, any LLM that accepts system prompts—including GPT‑4, Gemini, or local models—can load [`SKILL.md`](https://github.com/hardikpandya/stop-slop/blob/main/SKILL.md) and apply the rules. You simply format the request according to the target API’s message structure.

### How do I add custom jargon phrases to the replacement list?

Open [`references/phrases.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/phrases.md) and append new rows to the markdown table following the "| Avoid | Use instead |" format. Save the file, and the skill immediately recognizes the new mappings on the next inference call without requiring code changes or redeployment.

### Does Stop‑Slop modify sentence structure or only vocabulary?

While primarily focused on finding replacements for individual phrases, the skill enforces broader structural improvements. [`references/structures.md`](https://github.com/hardikpandya/stop-slop/blob/main/references/structures.md) directs the LLM to eliminate binary contrasts, negative listings, and passive‑voice constructions, ensuring the final text flows naturally even after jargon removal.