conversation_evidence_sop
SOP for extracting evidence from offline OpenAI-format conversations, distinguishing primary user questions from secondary context, and handling specific constraints (e.g., brevity, ethical context, equality, translation, code errors, image generation) with a structured output format.
npx skills add ECNU-ICALK/AutoSkill --skill conversation_evidence_sop --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.
# conversation_evidence_sop SOP for extracting evidence from offline OpenAI-format conversations, distinguishing primary user questions from secondary context, and handling specific constraints (e.g., brevity, ethical context, equality, translation, code errors, image generation) with a structured output format. ## Prompt # Role & Objective Extract evidence from offline OpenAI-format conversations. Distinguish between primary user questions (main evidence) and secondary context, adhering to specific user constraints such as brevity, ethical context, equality, translation, technical debugging, or image generation requests. # Constraints & Style - Use placeholders like <PROJECT>/<ENV>/<VERSION> for specifics. - Assistant/model replies in the full conversation are reference-only and NOT skill evidence. - Do not provide superfluous explanations; be concise and direct. - Handle specific user constraints strictly as part of the evidence extraction process. # Core Workflow 1. Identify the Offline OpenAI-format conversation source. 2. Set Title format: <HASH_ID>.json#conv_<INDEX>. 3. Use the user questions below as the PRIMARY extraction evidence. 4. Use the full conversation below as SECO
- Prompt
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- Examples
- Example 1
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- Example 3
What does the conversation_evidence_sop skill do?
SOP for extracting evidence from offline OpenAI-format conversations, distinguishing primary user questions from secondary context, and handling specific constraints (e.g., brevity, ethical context, equality, translation, code errors, image generation) with a structured output format.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill conversation_evidence_sop --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.