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 fledgeling-co/fledgeling-plugins --skill design-reviewgit clone --depth 1 https://github.com/fledgeling-co/fledgeling-pluginsWrote 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/fledgeling-co/fledgeling-plugins/design-review)<a href="https://agentmods.dev/skills/fledgeling-co/fledgeling-plugins/design-review"><img src="https://agentmods.dev/badge/skills/fledgeling-co/fledgeling-plugins/design-review/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/fledgeling-co/fledgeling-plugins/design-review"><img src="https://agentmods.dev/badge/skills/fledgeling-co/fledgeling-plugins/design-review.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 | $0.00318 | $0.11109 |
| Opus 5 | $0.00159 | $0.05555 |
| Sonnet 5 | $0.00064 | $0.02222 |
| Haiku 4.5 | $0.00032 | $0.01111 |
Grade A, and why
design-review 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 yesterday.
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 — 397 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design Review
You are the last automated pass before a human looks at this interface. Your job is to find what is actually wrong, prove it with evidence, rank it honestly, and be explicit about what you could not check.
Two failure modes to avoid, both worse than a short report:
- Fabricated confidence. Claiming a surface is fine when you only looked at source, or calling a clean lint "verified". A gate proves a known defect has not returned; it cannot find the defect nobody has met yet.
- Undifferentiated noise. Flagging every padding value at the same volume as a keyboard trap. Reviews that block on cosmetics get switched off, and then nothing gets reviewed.
The rule everything else here follows
A check whose "pass" and "cannot run" look identical must report which one it is. Every mechanism in this skill is a way of holding that line, and every defect it has shipped came from breaking it.
The shape is always the same. A probe reads a property the engine does not implement, gets "" or 0px, and reports a clean surface. Or it reads that empty value as evidence of absence and manufactures a finding from it. Both outputs are formally indistinguishable from a real measurement, which is why neither gets caught by reading the report.
It has happened here, measured on this machine, obscura 0.2.0, 18 August 2026, on this skill's own eval fixture. probeContrast() guarded the unresolvable-backdrop case with if (cs.backgroundImage && ...); an unreadable channel returns "", which is falsy, so the guard never fired, the ancestor walk climbed past the gradient to the opaque white body, and white 72px display type on a purple gradient was reported at 1.0:1 — a fabricated Tier 1 Blocker. Five of the seven reported failures were scored against a backdrop that is not there — rgb(255,255,255) on a purple gradient — and one of those five, the h1, does not fail at all: its worst stop is 3.53:1 against a 3.0 floor. The other four are real failures carrying materially wrong ratios (1.0 or 1.59 quoted where 3.53 or 2.22 is true), which is the more insidious half: the verdict looks right, so nobody re-checks the number. The mitigation field built for exactly this case read false on every one, and nothing anywhere read the field. Five systematisation metrics read the same class of channel and so returned clean forever, where 0 distinct radii reads as a perfectly tokenised surface. And probeSharedRails() had an early return omitting one key its consumer indexed unguarded, so the runner died with a KeyError on the first viewport and printed a traceback where a review should have been.
What ships with it
41 files 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.
- assets/report-template.md 6.9 KB
- evals/evals.json 27 KB
- evals/fixtures/band-voids-clean.html 2.1 KB
- evals/fixtures/band-voids.html 2.7 KB
- evals/fixtures/checkout-repo/src/checkout/checkout.css 802 B
- evals/fixtures/checkout-repo/src/checkout/CheckoutForm.tsx 1.8 KB
- evals/fixtures/clean-card.html 5.0 KB
- evals/fixtures/divider-clipped.html 2.7 KB
- evals/fixtures/divider-gutter-clean.html 1.7 KB
- evals/fixtures/divider-gutter.html 2.0 KB
- evals/fixtures/imitation-material.css 1.8 KB
- evals/fixtures/implicit-tracks-clean.html 3.1 KB
- evals/fixtures/implicit-tracks.html 3.9 KB
- evals/fixtures/landing.html 3.2 KB
- evals/fixtures/pricing-repo/src/marketing/Footer.tsx 225 B
- evals/fixtures/pricing-repo/src/pricing/Hero.tsx 432 B
- evals/fixtures/pricing-repo/src/pricing/PlanCards.tsx 832 B
- evals/fixtures/pricing-repo/src/pricing/pricing.css 1.2 KB
- evals/trigger-evals.json 3.3 KB
- gemini.md 18 KB
- references/browser-drivers.md 18 KB
- references/capture-protocol.md 12 KB
- references/craft-visual.md 18 KB
- references/evidence.md 17 KB
- references/flows-forms-copy.md 18 KB
- references/gates-accessibility.md 20 KB
- references/gates-performance-motion.md 11 KB
- references/intent-conformance.md 9.1 KB
- references/layout-integrity.md 27 KB
- references/parity-oracle.md 6.7 KB
- references/reliability-envelope.md 12 KB
- references/severity-and-report.md 17 KB
- references/states-and-resilience.md 14 KB
- references/systematisation.md 14 KB
- scripts/analyze_styles.py 27 KB runs code
- scripts/annotate.py 5.9 KB runs code
- scripts/audit_run.py 23 KB runs code
- scripts/probes.js 118 KB runs code
- scripts/run_review.py 47 KB runs code
- scripts/scan_source.py 15 KB runs code
- scripts/worklist.py 9.8 KB runs code
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.
- yesterday Changed 3f1b40ae09ec
- 9d ago First seen · 397 lines · 318 tokens per session scan A 10c3d0013fc0
design-review is a skill published in the GitHub repository fledgeling-co/fledgeling-plugins (2 stars, last pushed yesterday), licensed MIT. It adds 318 tokens to every session and 11,109 once invoked, about $0.0016 per session on Opus 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.
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