talk-normal-llm-prompt

Original：🇺🇸 English

Translated

System prompt toolkit that removes AI slop and makes any LLM respond like a normal person — concise, direct, no filler.

2installs

Sourcearadotso/trending-skills

Added on2026-04-25

NPX Install

npx skill4agent add aradotso/trending-skills talk-normal-llm-prompt

SKILL.md Content

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talk-normal

Skill by ara.so — Daily 2026 Skills collection.

talk-normal is a system prompt (plus a shell-script helper) that strips AI slop — bullet-point padding, hollow affirmations, corporate filler — from any LLM while preserving all useful information. Tested at ~73% character reduction on GPT-4o-mini and GPT-5.4 with no information loss.

How it works

The project is a single

prompt.md

file (the system prompt) plus optional shell helpers. You copy the prompt text into the "System" field of any LLM interface or API call.

repo layout
├── prompt.md          ← the system prompt (main artifact)
├── CHANGELOG.md       ← rule history
├── CONTRIBUTING.md    ← how to add rules
└── TEST_RESULTS.md    ← before/after comparisons

Installation

1 — Clone the repo

bash

git clone https://github.com/hexiecs/talk-normal.git
cd talk-normal

2 — Read the prompt

bash

cat prompt.md

3 — Copy into your tool

Paste the contents of

prompt.md

into:

ChatGPT → Settings → Customize ChatGPT → Custom Instructions → "How should ChatGPT respond?"
Claude.ai → Project Instructions
Cursor / Windsurf →
```
.cursorrules
```
or global AI rules
API calls →
```
system
```
parameter (see examples below)

Using the prompt via API

OpenAI (Python)

python

import os
from pathlib import Path
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

system_prompt = Path("prompt.md").read_text()

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user",   "content": "What is Python?"},
    ],
)
print(response.choices[0].message.content)

OpenAI (curl)

bash

SYSTEM=$(cat prompt.md | jq -Rs .)

curl https://api.openai.com/v1/chat/completions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d "{
    \"model\": \"gpt-4o-mini\",
    \"messages\": [
      {\"role\": \"system\", \"content\": $SYSTEM},
      {\"role\": \"user\",   \"content\": \"What is Python?\"}
    ]
  }"

Anthropic Claude (Python)

python

import os
from pathlib import Path
import anthropic

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

system_prompt = Path("prompt.md").read_text()

message = client.messages.create(
    model="claude-opus-4-5",
    max_tokens=1024,
    system=system_prompt,
    messages=[{"role": "user", "content": "Explain Docker in one paragraph."}],
)
print(message.content[0].text)

Google Gemini (Python)

python

import os
from pathlib import Path
import google.generativeai as genai

genai.configure(api_key=os.environ["GEMINI_API_KEY"])

system_prompt = Path("prompt.md").read_text()

model = genai.GenerativeModel(
    model_name="gemini-1.5-flash",
    system_instruction=system_prompt,
)

response = model.generate_content("What is a neural network?")
print(response.text)

Ollama (local models)

bash

SYSTEM=$(cat prompt.md)

ollama run llama3 \
  --system "$SYSTEM" \
  "What is a REST API?"

Or via the Ollama Python SDK:

python

import subprocess, json
from pathlib import Path

system_prompt = Path("prompt.md").read_text()

result = subprocess.run(
    ["ollama", "run", "llama3"],
    input=f"SYSTEM: {system_prompt}\nUSER: What is a REST API?",
    capture_output=True, text=True,
)
print(result.stdout)

Shell helper: one-liner wrapper

A reusable shell function that injects the prompt automatically:

bash

# Add to ~/.bashrc or ~/.zshrc
export TALK_NORMAL_PROMPT="$HOME/talk-normal/prompt.md"

asknormal() {
  local question="$*"
  local system
  system=$(cat "$TALK_NORMAL_PROMPT")

  curl -s https://api.openai.com/v1/chat/completions \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -H "Content-Type: application/json" \
    -d "$(jq -n \
      --arg sys "$system" \
      --arg q   "$question" \
      '{model:"gpt-4o-mini",messages:[{role:"system",content:$sys},{role:"user",content:$q}]}'
    )" | jq -r '.choices[0].message.content'
}

Usage:

bash

source ~/.bashrc
asknormal "What is the CAP theorem?"

Embedding in a project's AI config

Cursor (

.cursorrules

)

bash

# Prepend talk-normal to your existing rules
cat talk-normal/prompt.md > .cursorrules
echo "" >> .cursorrules
echo "# Project-specific rules below" >> .cursorrules
cat your-existing-rules.md >> .cursorrules

OpenAI Assistants API

python

import os
from pathlib import Path
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
system_prompt = Path("talk-normal/prompt.md").read_text()

assistant = client.beta.assistants.create(
    name="Normal Assistant",
    instructions=system_prompt,
    model="gpt-4o-mini",
)
print(f"Assistant ID: {assistant.id}")

Combining with your own system prompt

talk-normal rules are additive — prepend them before your domain instructions:

python

from pathlib import Path

talk_normal = Path("talk-normal/prompt.md").read_text()

your_rules = """
You are a senior backend engineer. Answer questions about Python, Go, and distributed systems.
"""

combined_system = f"{talk_normal}\n\n---\n\n{your_rules}"

Common patterns

Pattern 1: Measure verbosity reduction

python

def verbosity_ratio(before: str, after: str) -> float:
    """Returns fraction of original length kept (lower = more concise)."""
    return len(after) / len(before)

before = "Python is a high-level, interpreted programming language known for its readability..."  # 1583 chars
after  = "Python is a high-level, interpreted language known for readability..."                  #  513 chars
print(f"{verbosity_ratio(before, after):.0%} of original length")  # → 32%

Pattern 2: A/B test with and without the prompt

python

import os
from pathlib import Path
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
system_prompt = Path("talk-normal/prompt.md").read_text()

question = "What is Kubernetes?"

def ask(system: str | None, user: str) -> str:
    messages = []
    if system:
        messages.append({"role": "system", "content": system})
    messages.append({"role": "user", "content": user})
    resp = client.chat.completions.create(model="gpt-4o-mini", messages=messages)
    return resp.choices[0].message.content

without = ask(None, question)
with_prompt = ask(system_prompt, question)

print(f"Without: {len(without)} chars")
print(f"With:    {len(with_prompt)} chars")
print(f"Reduction: {(1 - len(with_prompt)/len(without)):.0%}")

Pattern 3: Keep the prompt up to date

bash

# Pull latest rules from upstream
cd talk-normal
git pull origin main

# Check what changed
git log --oneline -10
cat CHANGELOG.md | head -50

Contributing a new rule

Fork the repo and create a branch:
```
git checkout -b rule/no-em-dashes
```
Edit
```
prompt.md
```
— add your rule in plain imperative English
Add an entry to
```
CHANGELOG.md
```
Open an Issue or PR describing: what slop the rule targets, a before/after example

bash

# Quick before/after test for your new rule
SYSTEM=$(cat prompt.md)
echo "Test question" | asknormal   # uses your modified prompt

Troubleshooting

Symptom	Fix
Model still uses bullet points	Ensure the system prompt is in the `system` role, not prepended to `user`
Prompt too long for context window	Use a smaller model or trim older messages; `prompt.md` is intentionally compact
Ollama ignores system prompt	Some quantized models have weak instruction-following; try `mistral` or `llama3`
Rules conflict with your own system prompt	Put talk-normal rules first; add `# Override:` comment before conflicting rules
Response is too terse / lost information	The prompt reduces filler, not facts — file an issue with a reproduction case

Key facts for agents

Primary artifact:
```
prompt.md
```
— copy its text verbatim as the
```
system
```
message
No code to run: this is a prompt, not a library; no
```
pip install
```
, no build step
Model-agnostic: works with GPT, Claude, Gemini, LLaMA, Mistral, etc.
Tested reduction: ~72–73% character reduction, zero information loss on 10-question benchmark
License: MIT — use freely in commercial products

talk-normal-llm-prompt

NPX Install

Tags

SKILL.md Content

talk-normal

How it works

Installation

1 — Clone the repo

2 — Read the prompt

3 — Copy into your tool

Using the prompt via API

OpenAI (Python)

OpenAI (curl)

Anthropic Claude (Python)

Google Gemini (Python)

Ollama (local models)

Shell helper: one-liner wrapper

Embedding in a project's AI config

Cursor (
`.cursorrules`
)

OpenAI Assistants API

Combining with your own system prompt

Common patterns

Pattern 1: Measure verbosity reduction

Pattern 2: A/B test with and without the prompt

Pattern 3: Keep the prompt up to date

Contributing a new rule

Troubleshooting

Key facts for agents

talk-normal-llm-prompt

NPX Install

Tags

SKILL.md Content

talk-normal

How it works

Installation

1 — Clone the repo

2 — Read the prompt

3 — Copy into your tool

Using the prompt via API

OpenAI (Python)

OpenAI (curl)

Anthropic Claude (Python)

Google Gemini (Python)

Ollama (local models)

Shell helper: one-liner wrapper

Embedding in a project's AI config

Cursor (.cursorrules)

OpenAI Assistants API

Combining with your own system prompt

Common patterns

Pattern 1: Measure verbosity reduction

Pattern 2: A/B test with and without the prompt

Pattern 3: Keep the prompt up to date

Contributing a new rule

Troubleshooting

Key facts for agents

Cursor (
`.cursorrules`
)