--- id: "b9f66ab5-ae44-4638-ad00-b51a2904671f" name: conversation-evidence-sop description: "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." version: "0.1.15" tags: - "conversation" - "evidence" - "SOP" - "extraction" - "constraints" - "offline" - "openai" - "id_number" - "code_debugging" - "image_generation" triggers: - "Use when the user asks for a process or checklist." - "Use when you want to reuse a previously mentioned method/SOP." - "Use when extracting evidence with specific constraints (e.g., brevity, ethical context, equality, translation, code errors, image generation)." examples: - input: "Break this into best-practice, executable steps." - input: "Extract evidence regarding Maslow's theories and Gini coefficient from the conversation." notes: "Derived from candidate specific examples." - input: "Extract evidence regarding 'Awakening Age' and 'Li Keqiang' from the conversation." notes: "Derived from candidate specific context." - input: "Extract evidence regarding translation requests from news.gov.mb.ca." notes: "Derived from candidate specific context (2023/news/gov)." - input: "Extract evidence regarding the Python IndentationError and ID number validation system." notes: "Derived from candidate specific context (code debugging and ID validation)." - input: "Extract evidence regarding image generation rules (Unsplash API, Stable Diffusion format)." notes: "Derived from candidate specific context." --- # 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 // 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: .json#conv_. 3. Use the user questions below as the PRIMARY extraction evidence. 4. Use the full conversation below as SECONDARY context reference. 5. Extract and list Primary User Questions (main evidence) explicitly from the provided list. # Output Format For each step number, provide: - Status/Result - What to do next - Action - Checks - Failure rollback/fallback plan ## Triggers - Use when the user asks for a process or checklist. - Use when you want to reuse a previously mentioned method/SOP. - Use when extracting evidence with specific constraints (e.g., brevity, ethical context, equality, translation, code errors, image generation). ## Examples ### Example 1 Input: Break this into best-practice, executable steps. ### Example 2 Input: Extract evidence regarding Maslow's theories and Gini coefficient from the conversation. Notes: Derived from candidate specific examples. ### Example 3 Input: Extract evidence regarding 'Awakening Age' and 'Li Keqiang' from the conversation. Notes: Derived from candidate specific context. --- Source attribution: ECNU-ICALK Original project: https://github.com/ECNU-ICALK/AutoSkill License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: ECNU-ICALK Original project: https://github.com/ECNU-ICALK/AutoSkill Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Content & Media, Education & Research, Marketing Roles: Content Marketer, Copywriter, Researcher Specialties: Specialty not specified Tools: Openai Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 10d1370d606122ef6902952951a94cb26ab4cad7754ce9728f82df624bdd3beb