Front-End Checklist is a collection of front-end web development rules organized as a practical review and quality workflow for people and AI agents. Developers use it to audit projects and pull requests, check implementation details, and find guidance for remediation and verification. The catalogue entries provide skills, instructions, MCP tools, and a plugin for using the checklist in agent workflows.
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 skills add thedaviddias/Front-End-Checklist --skill improve-rulegit clone --depth 1 https://github.com/thedaviddias/Front-End-ChecklistWrote 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/skills/thedaviddias/front-end-checklist/improve-rule)<a href="https://agentmods.dev/skills/thedaviddias/front-end-checklist/improve-rule"><img src="https://agentmods.dev/badge/skills/thedaviddias/front-end-checklist/improve-rule/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/skills/thedaviddias/front-end-checklist/improve-rule"><img src="https://agentmods.dev/badge/skills/thedaviddias/front-end-checklist/improve-rule.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00039 | $0.01272 |
| Opus 5.5 | $0.00016 | $0.00509 |
| Sonnet 5.5 | $0.00008 | $0.00254 |
| Haiku 4.5 | $0.00004 | $0.00127 |
Grade A, and why
improve-rule 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.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve a Front-End Checklist Rule
This skill helps you review and improve the quality of rule MDX files in the Front-End Checklist project. Use it to fix stub content, write better AI prompts, and enrich rules so they score well against the quality rubric.
Quality Dimensions
Every rule is scored across these dimensions (see references/scoring-rubric.md for full details):
| Dimension | Max | What "good" looks like |
|---|---|---|
prompts.check |
10 | Specific audit instruction, not generic |
prompts.fix |
10 | Actionable fix steps with CSS/HTML/JS specifics |
prompts.explain |
10 | Explains the why, not just restates the title |
tldr |
5 | 3+ concise bullets, each a standalone takeaway |
whyItMatters |
5 | 1–2 sentences, concrete user impact |
aiContext |
5 | "Applies to…" — when should an agent use this? |
relatedRules |
5 | 2+ slug + reason pairs |
codeExamples |
10 | 3+ ✅/❌ annotated blocks in the body |
bodyDepth |
10 | 300+ words, not a stub |
resources |
5 | External links (MDN, WCAG, articles) |
prompts.codeReview |
5 | Code review workflow prompt (optional) |
Base score: 100 with additional conditional V2 points. Target: ≥ 50 (passing).
Stub Prompts — What to Avoid
These patterns are automatically detected as low-quality:
- "Verify if the project adheres to: [Rule Title]"
- "Update the codebase to align with: [Rule Title]"
- "Explain the importance of [Rule Title]"
Replace them with specific, actionable instructions that name the exact HTML attributes, CSS properties, or JavaScript patterns involved.
Check
Run the quality scorer on a specific rule to see its current score:
pnpm score:rules packages/content/rules/en/{category}/{slug}.mdx
Then open the MDX file and identify which dimensions are failing.
Run the structure validator as well:
pnpm validate:rule-structure packages/content/rules/en/{category}/{slug}.mdx
The rule body must follow this order:
- intro paragraph before any H2
## Code Exampleor## Code Examples## Why It Matters- optional guidance sections
- final
## Verification
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 120 lines · 39 tokens per session scan A e814c5b5bd4b
improve-rule is a skill published in the GitHub repository thedaviddias/Front-End-Checklist (74,564 stars, last pushed 20d ago), licensed MIT. It adds 39 tokens to every session and 1,272 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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