color / skirt / opacity
General SOP for common requests related to color, skirt, opacity.
npx skills add ECNU-ICALK/AutoSkill --skill color-skirt-opacity --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# color / skirt / opacity General SOP for common requests related to color, skirt, opacity. ## Prompt Follow this SOP (replace specifics with placeholders like <PROJECT>/<ENV>/<VERSION>): 1) Offline OpenAI-format conversation source. 2) Title: a29f73ab28f7c559a87470d2aa2a88f8.json#conv_1 3) Use the user questions below as the PRIMARY extraction evidence. 4) Use the full conversation below as SECONDARY context reference. 5) In the full conversation section, assistant/model replies are reference-only and not skill evidence. 6) Primary User Questions (main evidence): 7) if i sahred a hexvalue palette for a stocking and then a skirt aswell could you tell me how they would pair together 8) Stocking Color: Ivory (#FFFFFF) | Opacity: 80% | a classic and elegant shade of off-white 9) Welt Color: Pale Silver (#C0C0C0) | Opacity: 90% | a light and subtle shade of silver 10) Welt Lacing Color: Candlelight Gold - #ECD9B0 Opacity: 100% A warm and luminous gold hue resembling the soft glow of candlelight For each step, include: action, checks, and failure rollback/fallback plan. Output format: for each step number, provide status/result and what to do next. ## Triggers - Use when the user asks f
- Prompt
- Triggers
- Examples
- Example 1
What does the color / skirt / opacity skill do?
General SOP for common requests related to color, skirt, opacity.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill color-skirt-opacity --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.