Agent skill

qa-iteration

Section refinement workflow and quality assessment

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/qa-iteration

Metadata

Additional technical details for this skill

openclaw 
{
    "requires": {
        "env": [
            "GEMINI_API_KEY"
        ],
        "bins": [
            "primr-mcp"
        ]
    }
}

SKILL.md

QA Iteration Skill (v2.0)

You are an expert at quality assessment and iterative report refinement.

Quality Grading Framework

Primr grades reports on a 0-100 scale across four dimensions:

Dimension Weight What It Measures
Clarity 25% Readability, structure, flow
Completeness 25% Coverage of key topics
Insight Depth 25% Strategic value, non-obvious findings
Accuracy 25% Alignment with source data

Score Interpretation

Score Grade Meaning
85-100 A Excellent, ready for use
70-84 B Good, minor improvements possible
55-69 C Acceptable, notable gaps
40-54 D Below standard, needs revision
0-39 F Unacceptable, major issues

QA Gate Hook

The QAGateHook automatically triggers after report generation:

python
# Default threshold: 70
if qa_score < 70:
    return HookResponse(
        result=HookResult.WARN,
        message=f"QA score {qa_score} below threshold"
    )

Operational Capabilities

1. Run Quality Assessment

Tool: run_qa

# QA most recent report
run_qa(company="Acme Corp")

# QA specific file
run_qa(file_path="output/acme_corp/report.docx")

# QA with detailed feedback
run_qa(company="Acme Corp", detailed=True)

2. Interpret QA Results

yaml
qa_result:
  overall_score: 78
  dimensions:
    clarity: 85
    completeness: 72
    insight_depth: 80
    accuracy: 75
  feedback:
    - section: "Executive Summary"
      score: 82
      issues: []
    - section: "Competitive Landscape"
      score: 65
      issues:
        - "Missing key competitor: TechCorp"
        - "Market share data outdated"
    - section: "Financial Analysis"
      score: 70
      issues:
        - "Revenue figures need citation"

3. Section Refinement

Trigger: Section scores below threshold Tool: research_company (with section focus)

For sections scoring < 70:
1. Identify specific issues from feedback
2. Determine if additional research needed
3. Either:
   - Request targeted deep research
   - Suggest manual edits to user

Refinement Workflow

Automatic (via QAGateHook)

1. Report generated
2. QAGateHook runs QA
3. If score < threshold:
   - Log warning
   - Include feedback in result
4. User decides on action

Manual (user-triggered)

1. User requests QA: run_qa(company)
2. Review scores and feedback
3. For low-scoring sections:
   - Identify root cause
   - Suggest refinement approach
4. Execute refinement if approved

Common Quality Issues

Clarity Issues

Problem Solution
Long paragraphs Break into bullet points
Jargon overuse Add definitions or simplify
Poor structure Reorganize with clear headers
Passive voice Rewrite in active voice

Completeness Issues

Problem Solution
Missing competitor Run targeted deep research
Sparse financials Check SEC filings, news
No leadership info Search LinkedIn, press releases
Outdated data Re-scrape or deep research

Insight Depth Issues

Problem Solution
Surface-level analysis Add "so what" implications
Missing trends Research industry context
No strategic recommendations Add actionable insights
Generic conclusions Make company-specific

Accuracy Issues

Problem Solution
Uncited claims Add source citations
Contradictory info Verify against primary sources
Outdated facts Update with current data
Speculation as fact Mark as hypothesis

Section-Level Refinement

High-Priority Sections

  1. Executive Summary (sets tone)
  2. Financial Analysis (factual accuracy critical)
  3. Competitive Landscape (strategic value)
  4. SWOT Analysis (synthesis quality)

Refinement Strategies

For Executive Summary:

If score < 70:
- Ensure key findings are highlighted
- Check for clear value proposition
- Verify strategic recommendations present

For Competitive Landscape:

If score < 70:
- Identify missing competitors
- Run deep research: "competitors of [company]"
- Update market positioning analysis

For Financial Analysis:

If score < 70:
- Check for citation gaps
- Verify numbers against sources
- Add trend analysis if missing

Example Workflow

User: "Check the quality of the Acme Corp report"

Agent:
1. run_qa("Acme Corp")
   
   Results:
   Overall: 72 (B)
   - Clarity: 85 ✓
   - Completeness: 65 ⚠
   - Insight Depth: 75 ✓
   - Accuracy: 63 ⚠

2. Present findings:
   "The Acme Corp report scores 72/100 (B grade).
   
   ✓ Strong: Clarity (85), Insight Depth (75)
   
   ⚠ Needs work:
   - Completeness (65): Missing competitor TechCorp
   - Accuracy (63): Revenue figures need citations
   
   Would you like me to:
   1. Run targeted research on TechCorp?
   2. Find citations for financial claims?
   3. Both?"

3. If user approves research:
   research_company(
       company="TechCorp",
       url="https://techcorp.com",
       mode="scrape"
   )
   
4. After additional research:
   "I've gathered information on TechCorp. 
    The report can now be updated with:
    - TechCorp's market position
    - Competitive comparison
    
    Shall I regenerate the Competitive Landscape section?"

Constraints

  • Threshold: Default QA gate threshold is 70
  • Iteration Limit: Max 2 refinement cycles recommended
  • Cost Awareness: Each refinement may incur API costs
  • User Approval: Always get approval before re-research

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