qa-appender

Original🇨🇳 Chinese
Translated

Intelligent Q&A Appender that appends Q&A content from conversations to existing notes or creates new independent cards. Adopting the C+I structure (Insight + Context), it resolves the conflict between "unintelligible after simplification" and "too verbose without simplification". Triggers: /qa, /ask

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SKILL.md Content (Chinese)

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Q&A Appender

Role: Knowledge Referee, which determines the attribution of Q&A content and appends it in the optimal format.

Core Responsibilities

Single Responsibility: Append Q&A content from conversations to existing notes or create new independent cards in the C+I structure.

Usage Scenarios

Trigger Commands:
/qa
or
/ask
Applicable Scenarios:
  • Have specific doubts (What/Why/How) about a certain point in an existing document
  • Received answers through conversations
  • Need to record the Q&A in notes

Core Logic: Knowledge Classification

I will act as a "Knowledge Referee" to determine the attribution of the Q&A:
  • Context Heavy: Unintelligible without the original context → Append to the end of the original text
  • Atomic Concept: Universal concept that can be understood independently of the original text → Generate an independent knowledge card
For detailed classification criteria, see:
references/classification_guide.md

Workflow

When you input
/qa
or
/ask
, I will:
  1. Intelligent Session Range Recognition
    • Automatic Recognition: Analyze recent conversations to identify the current discussion topic and question boundaries
    • Key Signals:
      • Time point when the user raised a new question
      • Topic transition markers (e.g., "Additionally", "Another question")
      • Problem resolution markers (e.g., "Okay", "Got it", "Resolved")
    • Extraction Principle: Only extract continuous conversation segments relevant to the current question, avoid including irrelevant content
    • Intelligent Judgment:
      • If it's a single Q&A (1-3 rounds): Extract the Q&A
      • If it's a complex discussion (4-10 rounds): Extract the complete discussion process
      • If it exceeds 10 rounds: Prompt the user to confirm whether to include all content
  2. Read Classification Criteria
    • Must read
      references/classification_guide.md
      before execution
    • Determine the attribution of the Q&A (Context Heavy vs Atomic Concept)
  3. Determine Processing Method
    • Context Heavy: Append the
      ## 💡 Practical Q&A
      section at the end of the original text
    • Atomic Concept: Generate an independent knowledge card
  4. Apply C+I Structure
    • Read
      references/ci_structure.md
      to obtain complete specifications
    • Strictly organize content according to the template format
    • Logic (Insight) comes first, Context comes below
  5. Obtain Confirmation
    • Provide the following information at once:
      • Session Range: Clearly inform which rounds of conversations were extracted (e.g., "Extracted the last 3 rounds of conversations")
      • Judgment Result: Context Heavy or Atomic Concept
      • Judgment Reason: Why this judgment was made
      • Target File Path: Which file will be written to
    • Prohibited: Displaying a complete preview of the content to be appended (the preview is too long and interferes with reading)
    • Confirm only once and wait for the user's reply
  6. Write to File
    • Directly write to the file after user confirmation
    • Prohibited: Displaying the preview or confirming again
    • Briefly inform the result after writing

Execution Specifications (Important)

Intelligent Range Recognition:
  • ✅ Automatically identify relevant conversation segments and avoid extracting irrelevant content
  • ✅ Clearly inform the extracted session range during the confirmation phase
  • ❌ Do not extract the entire conversation history
One-Time Confirmation Principle:
  • ✅ Confirm only once in step 5
  • ❌ Prohibited to confirm again in step 6
No Preview Principle:
  • ✅ Only display the session range, judgment result, and brief reason
  • ❌ Prohibited to display the complete preview of the content to be appended
  • Reason: Long preview content interferes with reading, and users trust the judgment logic
Efficient Execution:
Step 1: Intelligent session range recognition (internal processing)
Step 2-4: Read + analyze + organize content (internal processing)
Step 5: Output session range + judgment result + wait for confirmation (once)
Step 6: Directly write + brief notification (completed)

C+I Structure Overview

Adopting the C+I structure resolves the conflict between "unintelligible after simplification" and "too verbose without simplification":
  • C (Context) - Original Context: The complete conversation at that time, including code, error messages, and parameters
  • I (Insight) - Key Takeaway: A one-sentence universal principle without specific cases
For complete instructions and templates, see:
references/ci_structure.md

Collaboration with Other Skills

Upstream:
  • conversation-extractor
    : Generates basic notes
  • process-doc-generator
    : Generates process documents
Downstream:
  • The appended document can be further expanded using
    process-doc-generator

Resource Files

  • C+I Structure Details: references/ci_structure.md
  • Classification Guide: references/classification_guide.md