china / that / country
General SOP for common requests related to tversky, cognitive, kahneman.
npx skills add ECNU-ICALK/AutoSkill --skill china-that-country --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.
# china / that / country General SOP for common requests related to tversky, cognitive, kahneman. ## Prompt Follow this SOP (replace specifics with placeholders like <PROJECT>/<ENV>/<VERSION>): 1) Offline OpenAI-format conversation source. 2) Title: a6670364e57fd6f0ac8f22d2e81ca1c0.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) Describe a time when you as reporting senior manager sought others’ views on an important topic or issue. 8) any specific accounting related topic 9) how about getting opinion for asset related topic 10) how about getting opinion for inventory in oil and gas related topic 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 for a process or checklist. - Use when you want to reuse a previously mentioned method/SOP. ## Examples ### Example 1 Input: Break this into best-practice
- Prompt
- Triggers
- Examples
- Example 1
What does the china / that / country skill do?
General SOP for common requests related to tversky, cognitive, kahneman.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill china-that-country --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.