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

7installs

Sourceqingchunwuhui/xianfengaiskills

Added on2026-02-07

NPX Install

npx skill4agent add qingchunwuhui/xianfengaiskills qa-appender

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:

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
Read Classification Criteria
- Must read
```
references/classification_guide.md
```
  before execution
- Determine the attribution of the Q&A (Context Heavy vs Atomic Concept)
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
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
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
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