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Arakiss

5 listings · 0 installs

Open-source publisher. Listings imported from github.com/Arakiss — credited to the original author with their license.

↗ github.com/Arakiss
5 results
Self-run
SKILL

Preference Marketing Web

Applies a scenario-matched preference brief to public marketing and editorial websites while protecting content hierarchy, conversion goals, accessibility, performance, responsive behavior, and declared discoverability requirements. Use for landing pages, portfolios, company sites, campaign pages, or article layouts after preferences have been calibrated or provided. Do not use for authenticated…

0
28
Free
Self-run
SKILL

Preference Product Ui

Applies a scenario-matched preference brief to task-oriented product interfaces such as dashboards, forms, tables, settings, and operational workflows while preserving correctness, accessibility, state coverage, and task completion. Use when designing or revising authenticated or data-dense application UI. Do not use for marketing pages, purely editorial layouts, or aesthetic review without a rep…

0
23
Free
Self-run
SKILL

Preference Calibrate

Elicits scenario-bounded human preferences through controlled alternatives, records literal choices and provenance, and turns supported signals into a reusable preference brief. Use when a decision-maker can reject work but cannot yet specify what to change, when repeated subjective directions have failed, or before delegating visual or editorial choices. Do not use to score taste or replace corr…

0
23
Free
Self-run
SKILL

Prose Verdict

Gates complete public-facing prose with a binary PUBLISH or HOLD verdict based on factual support, audience fit, clarity, structure, voice, and repetitive or generic writing patterns, without rewriting the text. Use immediately before publishing articles, essays, READMEs, landing-page copy, or announcements. Do not use for drafting, copyediting, private notes, or review of an incomplete excerpt.

0
23
Free
Self-run
SKILL

Preference Verify

Verifies that an exact artifact revision follows a scenario-matched preference record without merging preference fidelity with correctness or task fitness. Use after implementation, before release, or when checking drift against a recorded preference contract. Returns VERIFIED, NOT VERIFIED, or NO VERDICT with evidence. Do not use to generate alternatives, infer missing preferences, or produce a…

0
22
Free
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