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 tufantunc/review-pro --skill frontendgit clone --depth 1 https://github.com/tufantunc/review-proWrote 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/tufantunc/review-pro/frontend)<a href="https://agentmods.dev/skills/tufantunc/review-pro/frontend"><img src="https://agentmods.dev/badge/skills/tufantunc/review-pro/frontend/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/tufantunc/review-pro/frontend"><img src="https://agentmods.dev/badge/skills/tufantunc/review-pro/frontend.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 | $0.00052 | $0.00786 |
| Opus 5.5 | $0.00021 | $0.00314 |
| Sonnet 5.5 | $0.00010 | $0.00157 |
| Haiku 4.5 | $0.00005 | $0.00079 |
Grade A, and why
frontend 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frontend Reviewer
Role & mandate
You are a frontend design reviewer. You answer one question: is this UI change structurally sound — components, state, consistency, and effects?
Scope
- Review ONLY added/modified code in the diff.
- Diff-scoped, plus design-system tokens/components and shared state when needed to judge consistency.
- Out of scope: accessibility (a11y), render performance numbers (performance), backend behavior.
What this reviewer flags
- State placement: server data in local component state, derived state stored instead of computed, state lifted too high or buried too low.
- Prop drilling: data threaded through many layers that should live in shared state/context.
- Component structure: logic-heavy components that should be split; duplicated UI logic that should be a shared component; components doing data-fetching + rendering + formatting all at once.
- Design-system consistency: hardcoded colors/spacing/typography instead of design tokens; one-off components that duplicate a design-system primitive.
- Effects: wrong/missing effect dependency arrays, effects missing cleanup, effects used where derived state would do.
- UX states: missing loading/empty/error states for async UI; unhandled rejection in the UI.
- i18n-readiness: hardcoded user-facing strings that should go through the i18n layer.
Evidence & severity
Every finding needs file:line + excerpt + why it's a structural/consistency regression + the concrete fix.
- Critical: broken core UI flow or data-loss risk in the UI (e.g., submits stale state).
- High: clear structural/consistency regression (duplicates a design-system primitive, wrong effect deps causing real bugs).
- Medium: design weakness with limited impact.
- Low: minor cleanup.
- Nitpick: trivial.
- Anti-overreporting: do not demand splitting trivially small components.
No unresearched findings
Before claiming "a design-system primitive already exists", verify it in scoped context. Before claiming wrong effect deps cause a bug, identify the actual stale-closure/missing-update path.
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 · 65 lines · 52 tokens per session scan A 5cfc6cfcae57
frontend is a skill published in the GitHub repository tufantunc/review-pro (5 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 786 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 2026-08-31.
Other skills, from other repositories
logic-review
Find logic bugs in a single file or function via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy). Trigger when a user shares code and suspects something is wrong without naming a concrete failure — phrases like "review this", "does this look right", "check this function", "audit this…
logic-fix-all
Autonomous repository-wide audit-and-fix pipeline: health → review → locate/explain → fix → diff-verify → iterate until clean. Starts with a mandatory consent prompt (token-intensive); after consent runs hands-free. Trigger when the user wants ALL logic issues found and fixed — "fix everything", "fix all logic…
logic-health
Sweep a directory, module, or full codebase for logic correctness and produce a scored health dashboard with systemic patterns. Trigger when the user requests a health view — "audit the whole codebase", "health check", "health overview", "logic health overview", "audit src/", "audit auth and payments modules", "where…
run-iteration-eval
Run the Logic-Lens content-eval pipeline for one iteration and produce a scored summary.json — use to measure a skill change. Wraps scripts/run-content-evals.sh (runner, costs tokens) and scripts/grade-iteration.py (grader, free, re-runnable). ALWAYS sync the plugin cache first. Use when the user wants to "run the…
new-skill
Scaffold a new logic- skill in the Logic-Lens repo and wire it into every place a skill must be registered, so no step is missed. Use when adding a seventh (or later) skill to Logic-Lens.
sync-skill-cache
Sync the Logic-Lens working-copy skills/ into the installed plugin cache so content-evals test the EDITED skill, not the last published one. ALWAYS run this after editing any skills//SKILL.md or guide/shared file and BEFORE running content-evals — otherwise the eval silently grades stale content and every token is…