SlipItIn/.github/skills/slop-eval/SKILL.md

name, description, metadata

name	description	metadata
slop-eval	Objectively evaluate a UI/web design against the pols.dev anti-slop design law: detect catalogued slop tells with cited evidence, score 8 weighted axes (color, type, components, layout, motion, execution, signature, cohesion), and emit a Slop Report with a 0–100 Slop Index and grade. Use when the user asks to "evaluate design slop", "slop report", "is this design AI slop", "audit this landing page design", "de-slop review", or wants an objective score of how generic/machine-made a design looks. To fix text (not design), use human-ai or humanizar skills instead.	author version date repository license category https://ft.ia.br 1.0.0 2026-07-16 https://github.com/fabricioctelles/skills Apache-2.0 code-quality-and-review

Slop Eval

Evaluate a design the way skill-evaluation evaluates a skill: every finding cites concrete evidence, every axis gets a 0–100 score, arithmetic runs through a script, and the output is a structured report — never a vibe check.

The tell catalog lives in references/tells.md; read it before sweeping. The positive rubric (signature formula, cohesion checks, slop→premium pairs) lives in references/premium-markers.md; read it before scoring Axes 7–8.

Source

• The pols.dev anti-slop design law — the tell catalog, absolute rules, and signature formula are distilled from it.
• Method modeled on skill-evaluation (cite-or-cut, weighted axes, scripted scoring, failure-mode diagnosis).

Parameters

Parameter	Description	Default
target	What to evaluate: live URL, screenshot(s), code path, or Figma export	Ask user
brief	Brand brief or explicit user directions the design followed	None
output	Path to write the report	./SLOP-REPORT.md

Write the report in the language the user is speaking; keep tell IDs and names in English so they stay greppable against the catalog.

Evidence channels

What you can verify depends on what you were given. Never score a check you could not observe — mark it Unverifiable and exclude it (like N/A in skill-evaluation).

Channel	Can verify	Cannot verify
Code (CSS/JSX/HTML)	Fonts, hex values, gradients, shadows, radii, opacity:0 gating, icon imports, layout skeletons	Optical centering, rendered contrast, seams, whether controls respond
Screenshot(s)	Everything visual: palette, type, layout, alignment, centering, clipping, contrast, seams	Hover/scroll motion, dead controls, invisible-content trap, responsive behavior
Live URL (browse + screenshot)	All of the above plus interactions, motion, fold ownership	Only what you didn't exercise

With code, grep before you stare: fonts.googleapis|next/font, lucide-react, linear-gradient, box-shadow, border-radius: *9999, backdrop-filter, opacity: *0, initial={{ *opacity: *0, overflow: *hidden, clip-path, position: *fixed. Each hit is a lead, not a verdict — confirm against the catalog entry before recording it.

Evidence acquisition SOP

Route by what the target is; always end with an evidence inventory (what was captured, what is Unverifiable) — it feeds the report header.

Live URL — the richest channel; prefer it whenever reachable. Use whatever browser automation this session has (a browser MCP such as Playwright or Chrome DevTools, or npx playwright screenshot as the no-MCP fallback) and capture, saving every artifact to the scratchpad so findings can cite file + region:

1. Load at desktop (1440×900) and mobile (390×844); wait for network idle.
2. Full-page screenshot of both viewports immediately after load, before any scrolling — sections sitting at opacity:0 waiting for a scroll reveal show up blank here (M1 evidence).
3. Scroll pass top to bottom, then a second full-page capture; diff the two mentally for reveal-gated content, seams (C11, X13), and fold ownership (L16).
4. Interaction pass: hover the primary CTA, one card, one nav link (M2–M4); click every tab, accordion, toggle, and button (M8); Tab through the page and confirm a visible focus ring (X14).
5. Zoom crops at 2x of: anything near a clipped edge (X2), circled/tiled numbers and icons (X1), pricing columns side by side (X3), button labels (X5).
6. Pull the rendered sources for the code-channel greps: font names from the network panel or <link>/@font-face, computed hex values from the stylesheets.

No browser automation available → fetch the HTML/CSS (curl) and run the code channel on it, ask the user for full-page desktop + mobile prints, and mark every visual-only and interaction check Unverifiable until the prints arrive. Never score a visual check from raw HTML.

Screenshots — Read each image. If only partial crops were provided, ask for full-page desktop + mobile before sweeping (a hero-only print cannot support L11, L15, or the cohesion axis). All interaction checks (M1, M8, X14, hover tells) are Unverifiable.

Code path — run the greps, read every file they hit, plus the layout/ page components and global styles. If the project runs locally, start its dev server and continue under the Live URL SOP — code plus a live render is the only combination that can verify everything.

Figma export — treat as Screenshots for visual tells; additionally fonts, hex values, and spacing are exact from the file. Motion and interaction axes are Unverifiable (score NA for Axis 5 unless prototypes were shared).

Axes and weights

#	Axis	Weight	Scored from
1	Color & Light	2x	Tells C1–C15
2	Typography & Copy	2x	Tells T1–T10, W1–W3
3	Components & Ornament	1x	Tells K1–K27
4	Layout & Composition	2x	Tells L1–L21
5	Motion & Interaction	1x	Tells M1–M8
6	Execution & Craft	2x	Tells X1–X14
7	Signature & Uniqueness	3x	7-element formula (positive rubric)
8	Cohesion	2x	4 checks (positive rubric)

Axis 7 carries the heaviest weight on purpose: the law's deepest rule is that dodging the tell list is still slop — a page with zero tells and no signature is unfinished work wearing restraint as an alibi.

Scoring

Axes 1–6 (tell-counted). Count confirmed tells on the axis by severity, then: score = max(0, 100 − 30·critical − 15·major − 5·minor). Run scripts/score.py axis CRIT MAJOR MINOR — don't do it by hand. One tell, one count: a pattern repeated across sections is still one tell (note the repetition in the evidence; repetition may upgrade minor → major where the catalog says so).

Axis 7 (Signature). Score each of the 7 formula elements 0 (absent), 50 (attempted, weak), or 100 (strong) per the rubric in premium-markers.md; the axis is their mean.

Axis 8 (Cohesion). Same 0/50/100 on the 4 cohesion checks; mean.

Compounding rule. Three or more major layout tells on one page cap Axis 4 at 40 — a page assembled from known skeletons is slop no matter how clean each block is.

Gates (pass as --cap to the overall run):

• Signature gate: Axis 7 < 40 caps the overall at 59 (grade C max). No amount of clean spacing rescues a page with no signature.
• Absolute-rule gate: any confirmed critical tell caps the overall at 69 (no grade A with broken execution).

Overall & Slop Index.

overall    = sum(axis_score × weight) / sum(weight)   # capped by gates
Slop Index = 100 − overall

Run scripts/score.py overall 1:80:2 2:65:2 ... [--cap 59] [--cap 69]. Unverifiable axes score NA and drop out of both sums. --fail-below N exits non-zero for CI gating, e.g. gating a PR on its preview deploy:

# .github/workflows/slop-gate.yml (step excerpt)
- name: Slop gate
  run: |
    # run slop-eval against $PREVIEW_URL, export each axis score, then:
    python3 skills/slop-eval/scripts/score.py overall \
      1:$A1:2 2:$A2:2 3:$A3:1 4:$A4:2 5:$A5:1 6:$A6:2 7:$A7:3 8:$A8:2 \
      --fail-below 40

Grade scale

Grade	Overall	Slop Index	Verdict
A	80–100	0–20	Premium — deliberate, signed, executed
B	60–79	21–40	Considered — mostly deliberate, some defaults
C	40–59	41–60	Generic — clean but templated or unsigned
D	20–39	61–80	Slop — assembled from presets
F	0–19	81–100	Pure slop

Absolute rules check

Six execution laws, each pass/fail/unverifiable, reported in their own table. Any fail is a critical tell (counts on its axis AND triggers the absolute-rule gate):

1. Content visible by default — nothing gated on an entrance animation (opacity:0 + reveal) (M1)
2. Clear the cut — no text/control sliced by clip, notch, overflow, or fixed height (X2, X11)
3. Parallel alignment — comparable columns share baselines; buttons anchored (X3)
4. Real centering — everything meant to be centered is, mathematically and optically (X1)
5. Legible contrast — every text clears its background by a real value gap (X5)
6. Controls work — every interactive-looking control responds (M8)

Workflow

1. Gather evidence — route the target through the Evidence acquisition SOP above. Done when the evidence inventory states what was captured and what is Unverifiable.
2. Read references/tells.md — the catalog you sweep against.
3. Sweep axes 1–6 — walk the catalog group by group. Cite-or-cut: a tell is only recorded with concrete evidence (hex value, font name, file:line, or screenshot region); no evidence, no tell. Check each candidate against its premium-pair note — the crafted version of a pattern is not the tell. Done when every catalog group has been swept and every recorded tell carries a citation.
4. Run the absolute rules check — all six, pass/fail/unverifiable with evidence.
5. Score Axes 7–8 — read references/premium-markers.md, score the 7 signature elements and 4 cohesion checks with one-line justifications each. Done when all 11 items carry a score and a justification.
6. Compute — score.py axis per tell-counted axis, then score.py overall with weights and any triggered --cap. Never hand-compute.
7. Write the report — read references/output-template.md and emit exactly that structure to output, ending with the 3–5 prioritized fixes that would move the score most (biggest weighted deltas first; a missing signature usually outranks any single tell).

Gotchas

• The brief overrides the law. If the user or brand explicitly directed a choice (a color, a layout, an effect), it is not a tell — the law itself says the user's word wins 100%. Ask for the brief when the design clearly follows one; note excluded tells in the report.
• Context flips a tell. Mono on real data is correct; a populated, real-feeling product window is a signature, not the fake-window tell; a tight micro-grid with texture is premium, a full-page graph paper is slop. Always check the premium pair before recording.
• Don't reward the clean miss. Zero tells with a weak signature is the most common failure of designs that tried to avoid slop. The signature gate exists for this — apply it without mercy.
• Severity discipline. Critical is reserved for broken (the six absolute rules). A blue-purple gradient is loud but not broken: major.
• One-axis bleed. Some tells could sit on two axes (cut-off glow is color and execution). The catalog assigns each tell to exactly one axis — count it only there.
• Portfolio tells. L19 (recycling your own house style) needs prior work from the same author to verify; without it, mark Unverifiable rather than guessing.

Quality checklist

Final gate before delivering — each item re-checks a workflow step:

• every recorded tell has ID + severity + citation (step 3)
• every unverifiable check is marked, not silently passed (steps 1, 4)
• all 6 absolute rules reported (step 4)
• all 11 signature/cohesion items scored with justification (step 5)
• caps applied when gates triggered; math from score.py only (step 6)
• report matches the template, fixes ranked by weighted impact (step 7)