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Get Started Free →Audit cloned or reimplemented websites for fidelity gaps, tracking scripts, source-brand and language residue, placeholders, and risky external dependencies. Use before handoff or deployment, or when asked to review a website clone for cleanup and readiness.
.claude/skills/nexu-io-clone-audit-mrlv3nl4/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-16 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-20 | ✗→✓ | ▲ Improved | -24% | 0% |
| case-04 | ✓→✗ | ▼ Worse | 416% | 0% |
Audit the requested website-clone workspace and produce an evidence-based deployment-readiness report. Inspect the current target; never reuse findings from an earlier project or run.
fidelity references. If none are available, mark visual fidelity as not checked instead of guessing.
configuration, and dependency manifests. Respect explicit exclusions.
packages, or make network requests unless the user authorizes it.
Treat references/source-1-CLONE_AUDIT.md only as historical provenance. Do not copy its paths, counts, or findings into a new audit unless the current target independently confirms them.
Inspect each category and record the evidence used:
missing or substituted fonts and images, broken asset paths, incorrect colors, and materially different layout or styling.
pixels, telemetry beacons, and unexpected third-party scripts.
social links, asset paths, comments, and copy that should have been replaced.
locale, excluding code identifiers and legitimate proper nouns.
template copy, dummy links, test credentials, and unfinished states.
localhost or development endpoints, external fonts and media, package downloads, and dependencies that may fail, leak data, or violate deployment constraints.
Open the surrounding context before reporting a match. Deduplicate repeated instances that share one root cause, but list every affected file or meaningful location.
For every finding, include:
blocker, high, medium, or low;file:line location when available;Keep these states distinct:
access was unavailable.
Never turn an unverified suspicion into a confirmed finding. Do not expose machine-local absolute paths, secrets, tokens, or personal data in the report.
Use this structure:
markdown# Clone Audit ## Scope and coverage - Target: <portable project label or repository-relative path> - Fidelity reference: <provided, not provided, or unavailable> - Exclusions or limitations: <items or none> ## Findings ### <category> | Severity | Evidence | Why it matters | Recommended action | | --- | --- | --- | --- | | <level> | `<relative/file:line>` — <identifier> | <impact> | <action> | ## Checked; none found - <category> ## Not checked / unverifiable - <category>: <reason> ## Deployment readiness <Ready, ready with follow-ups, or not ready> — <brief evidence-based reason>
Use not ready when confirmed unresolved findings can break the deployed experience, expose tracking or sensitive data unexpectedly, or leave material source-brand or placeholder content. Otherwise state any follow-ups and explain why they do or do not block deployment.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | pass→pass | 12,211 | 26,230 | +115% | 1 | 1 | 0% | 1,913 | 4,541 | +137% | 0 | 0 | — |
case-01 | fail→fail | 4,294 | 14,748 | +243% | 1 | 1 | 0% | 245 | 1,344 | +449% | 0 | 0 | — |
case-02 | fail→fail | 21,683 | 20,319 | -6% | 1 | 1 | 0% | 209 | 1,241 | +494% | 0 | 0 | — |
case-03 | fail→fail | 6,458 | 6,482 | +0% | 1 | 1 | 0% | 325 | 1,308 | +302% | 0 | 0 | — |
case-04 | pass→fail | 6,269 | 41,710 | +565% | 1 | 1 | 0% | 1,231 | 6,348 | +416% | 0 | 0 | — |
case-06 | pass→pass | 11,478 | 31,237 | +172% | 1 | 1 | 0% | 1,715 | 4,275 | +149% | 0 | 0 | — |
case-07 | pass→pass | 18,255 | 4,628 | -75% | 1 | 1 | 0% | 2,425 | 1,630 | -33% | 0 | 0 | — |
case-08 | pass→pass | 5,378 | 3,406 | -37% | 1 | 1 | 0% | 928 | 1,482 | +60% | 0 | 0 | — |
case-09 | pass→pass | 13,657 | 11,129 | -19% | 1 | 1 | 0% | 1,808 | 1,779 | -2% | 0 | 0 | — |
case-10 | fail→fail | 13,688 | 23,148 | +69% | 1 | 1 | 0% | 2,042 | 1,986 | -3% | 0 | 0 | — |
case-11 | pass→fail | 12,453 | 7,104 | -43% | 1 | 1 | 0% | 1,692 | 2,106 | +24% | 0 | 0 | — |
case-12 | pass→pass | 9,086 | 12,722 | +40% | 1 | 1 | 0% | 1,259 | 1,403 | +11% | 0 | 0 | — |
case-13 | pass→pass | 9,546 | 7,288 | -24% | 1 | 1 | 0% | 1,419 | 2,183 | +54% | 0 | 0 | — |
case-14 | fail→fail | 9,476 | 8,646 | -9% | 1 | 1 | 0% | 1,627 | 2,200 | +35% | 0 | 0 | — |
case-15 | pass→pass | 14,703 | 9,169 | -38% | 1 | 1 | 0% | 1,973 | 2,259 | +14% | 0 | 0 | — |
case-16 | fail→pass | 9,249 | 3,714 | -60% | 1 | 1 | 0% | 1,226 | 1,515 | +24% | 0 | 0 | — |
case-17 | fail→pass | 12,138 | 7,881 | -35% | 1 | 1 | 0% | 1,775 | 1,963 | +11% | 0 | 0 | — |
case-18 | pass→pass | 13,997 | 7,078 | -49% | 1 | 1 | 0% | 1,850 | 1,741 | -6% | 0 | 0 | — |
case-19 | fail→pass | 10,206 | 9,858 | -3% | 1 | 1 | 0% | 1,708 | 2,160 | +26% | 0 | 0 | — |
case-20 | fail→pass | 13,725 | 5,603 | -59% | 1 | 1 | 0% | 2,385 | 1,802 | -24% | 0 | 0 | — |
case-21 | pass→fail | 123,587 | 68,338 | -45% | 1 | 1 | 0% | 1,344 | 1,117 | -17% | 0 | 0 | — |
case-22 | pass→pass | 9,689 | 8,037 | -17% | 1 | 1 | 0% | 1,715 | 2,114 | +23% | 0 | 0 | — |
case-23 | pass→pass | 9,938 | 5,078 | -49% | 1 | 1 | 0% | 1,178 | 1,515 | +29% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 19 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +4 percentage points is the difference between those two pass rates over the 19 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.