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.
git clone --depth 1 https://github.com/karthikrshet/ClaudeMarkWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/karthikrshet/claudemark/clean-user-facing-text)<a href="https://agentmods.dev/rules/karthikrshet/claudemark/clean-user-facing-text"><img src="https://agentmods.dev/badge/rules/karthikrshet/claudemark/clean-user-facing-text/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/karthikrshet/claudemark/clean-user-facing-text"><img src="https://agentmods.dev/badge/rules/karthikrshet/claudemark/clean-user-facing-text.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00000 | $0.00242 |
| Opus 5.5 | $0.00000 | $0.00097 |
| Sonnet 5.5 | $0.00000 | $0.00048 |
| Haiku 4.5 | $0.00000 | $0.00024 |
Grade A, and why
clean-user-facing-text 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 1mo 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.
What it actually says
Cursor Rule: Clean User-Facing Text Hygiene
When writing or editing user-facing copy, documentation, and manuscripts:
- Never emit invisible Unicode formatting carriers: Disallow zero-width spaces (
U+200B), zero-width non-joiners (U+200C), word joiners (U+2060), and byte order marks (U+FEFF) in ordinary prose. - Standardize Spaces: Use regular ASCII spaces (
U+0020) instead of non-breaking spaces (U+00A0), thin spaces (U+2009), or mathematical spaces unless explicitly required for typography. - Preserve Valid Code and Syntax: Do not strip syntax tokens from code blocks or formula strings.
- Use ClaudeMark For Verification: Verify clean text with
python claudemark.py unicode inspect <file>orpython claudemark.py unicode visualize <file>.
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.
- 1mo ago First seen · 14 lines · 0 tokens per session scan A 5b23b3b37cff
clean-user-facing-text is a cursor rule published in the GitHub repository karthikrshet/ClaudeMark (10 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 242 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other cursor rules, from other repositories
agent-autonomy
AI agent autonomy threat agent — detects excessive autonomy, goal misalignment, unconstrained action scope, missing human-in-the-loop checkpoints, cascading multi-agent failures, and autonomous resource consumption against Processes.
case-uco-sdk
CASE/UCO SDK usage patterns for building digital forensics investigation graphs.
extension-authoring
Guidance for authoring CASE/UCO extension ontologies per the CDO Community Playground Guide.
mcpnuke-development
Core mcpnuke development standards and superpowers workflow integration.
mcpnuke-tests
Test conventions for mcpnuke — enforces TDD workflow.
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.