Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/technickai/ai-coding-config/user-facing-languagegit clone --depth 1 https://github.com/TechNickAI/ai-coding-configWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00011 | $0.01011 |
| Opus 5 | $0.00005 | $0.00505 |
| Sonnet 5 | $0.00002 | $0.00202 |
| Haiku 4.5 | $0.00001 | $0.00101 |
Grade A, and why
user-facing-language scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to user-facing-language — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User-Facing Language Guide
Write for capable, intelligent users. Make them feel respected, capable, confident, understood, and excited.
Quality bar: Apple-level attention. Every word earns its place.
Voice
Be authentic. Explain something real, not sell something imaginary. "Exports to CSV in under 2 seconds" builds trust. "Fast and efficient" says nothing.
Be direct. Say what you mean. Cut unnecessary words.
Be confident. You built something real, so own it. Avoid hedging with "might," "could," or "potentially." If it saves time, say how much.
Be specific. Concrete details matter. "Handles codebases up to 1M lines" beats "Scales well."
Language Patterns
Use "we" for the team: "We built this to solve the N machines problem." Use "you" for users: "You configure it once, then it runs automatically." Use imperatives for instructions: "Install the package. Set your API key."
What Not to Write
AI Clichés
The "it's not just X, it's Y" pattern appears constantly in AI-generated content. Never use it.
✗ "It's not just a code editor, it's a complete development environment"
✗ "This isn't just about speed, it's about transforming your workflow"
✗ "If 2024 was the year of X, 2025 will be..."
✗ "Imagine a world where..."
✗ "Let's dive in..."
✓ "A code editor with integrated debugging and deployment"
✓ "Processes 10,000 records in 2 seconds instead of 5 minutes"
CRITICAL Means Something Dies
AI marks everything as CRITICAL. Reserve this word for when something actually breaks without it.
✗ "CRITICAL: Read this section"
✗ "CRITICAL: Follow these best practices"
✗ "CRITICAL: Understand the architecture"
✓ "CRITICAL: Set ANTHROPIC_API_KEY or Claude Code won't start"
✓ "CRITICAL: Back up data before running - this operation is destructive"
CRITICAL means the system fails, data is lost, or security is compromised. Don't use it for recommendations, best practices, or things that are merely helpful. Most things aren't critical.
Format
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 147 lines · 11 tokens per session scan A 858516453f76
user-facing-language is a cursor rule published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 2mo ago), licensed MIT. It adds 11 tokens to every session and 1,011 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to user-facing-language, differing in 5 lines, and is treated as a copy.
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