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/talkpython/agentic-ai-for-python-courseWrote 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/commands/talkpython/agentic-ai-for-python-course/web-design-cleanup)<a href="https://agentmods.dev/commands/talkpython/agentic-ai-for-python-course/web-design-cleanup"><img src="https://agentmods.dev/badge/commands/talkpython/agentic-ai-for-python-course/web-design-cleanup/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/commands/talkpython/agentic-ai-for-python-course/web-design-cleanup"><img src="https://agentmods.dev/badge/commands/talkpython/agentic-ai-for-python-course/web-design-cleanup.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 | $0.00128 |
| Opus 5.5 | $0.00000 | $0.00051 |
| Sonnet 5.5 | $0.00000 | $0.00026 |
| Haiku 4.5 | $0.00000 | $0.00013 |
Grade A, and why
web-design-cleanup 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
Web Design Purist
I want you to act as a Web Design expert. You know HTML, JavaScript, CSS, and related technologies well. You embrace DRY principles and are good at reviewing others' code.
Right now, our job is to look at the HTML files to ensure that we don't have excessive inline CSS and JS. Sometimes this may be necessary (unless specified) to have style attributes on a html node directly. But using ... or ... blocks with css and js are not generally encouraged.
Try to find and move these to the correct static files.
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 · 8 lines · 0 tokens per session scan A feb4613f2d9a
web-design-cleanup is a command published in the GitHub repository talkpython/agentic-ai-for-python-course (75 stars, last pushed 11mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 128 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 2026-08-30.
Other commands, from other repositories
refactor-clean
Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.
full-review
Orchestrate comprehensive multi-dimensional code review using specialized review agents across architecture, security, performance, testing, and best practices.
pr-enhance
Enhance a pull request with a generated description, review checklist, risk assessment, and test coverage report.
tech-debt
Analyze and remediate technical debt — inventory debt items, score by impact, and produce a prioritized remediation plan with estimated effort.
team-review
Launch a multi-reviewer parallel code review with specialized review dimensions.
review_code
Use the text the user typed after the command name as the request for this command.