name: civitai-analyst description: "Generate and execute SQL queries against the civitai_records PostgreSQL database to analyze video performance on Civitai. Use when users ask about: video engagement metrics (likes, hearts, comments), content performance analysis, tag/theme analysis, quality scores, weekly reports, comparing videos, content recommendations, trend analysis, or any Civitai data queries. Triggers: Civitai, video stats, engagement, likes, hearts, comments, weekly report, tag analysis, quality score, content strategy, top performers, SQL query, video comparison, WoW analysis, 数据分析, 视频表现, 周报, 内容分析." metadata: {"openclaw":{"requires":{"bins":["npx"],"env":["CIVITAI_RECORD_MCP_SERVER_TOKEN"]}}}

Civitai Analyst

Analyze video performance data on Civitai through natural language queries. Generate SQL, execute against the database, and provide actionable insights.

Capabilities

1. SQL Generation - Convert natural language to optimized PostgreSQL queries
2. Query Execution - Run queries via query_civitai_db
3. Data Analysis - Interpret engagement metrics and find patterns
4. Content Insights - Analyze tags, themes, quality scores from video_analysis
5. Recommendations - Suggest content strategies based on performance data
6. Weekly Reports - Generate JSON/HTML performance summaries

mcporter Setup

1. Export the required token and point mcporter at this skill's config:

export CIVITAI_RECORD_MCP_SERVER_TOKEN="<token>"
export MCPORTER_CONFIG="${SKILL_DIR}/mcporter.json"

2. Optional validation commands:

test -f "${MCPORTER_CONFIG}"
npx mcporter list --config "${MCPORTER_CONFIG}"
npx mcporter list civitai_records --config "${MCPORTER_CONFIG}"

Tool Usage

Execute SQL with mcporter so every query is auditable and consistently configured:

npx mcporter call civitai_records.query_civitai_db \
  --config "${MCPORTER_CONFIG}" --output json \
  sql="SELECT ..."

Rules:

1. Always include --output json
2. Pass SQL as a single string. For multi-line queries use sql=$(cat <<'SQL' ... SQL)
3. Surface mcporter errors directly—most rejections return JSON with details

Error Handling: If query is rejected, response contains:

{
  "allowed": false,
  "reason": "...",
  "violation_type": "...",
  "suggestions": "..."
}

Fix the SQL based on the error and retry.

Workflow

1. Understand - Parse user's question, identify metrics/filters needed
2. Generate SQL - Use schema.md for tables, query-index.md for templates
3. Execute - Run npx mcporter call civitai_records.query_civitai_db with validated SQL
4. Analyze - Interpret results, find patterns, compare data points
5. Present - Format with links, provide insights and recommendations

Key Parameters

civitai_account

• User-provided account identifier
• Default fallback: 'c29' if not specified

on_behalf_of

• User's first name, inferred from session context
• Used to filter assets/stats by uploader

Date Ranges

• Use calendar weeks (Monday 00:00 to Sunday 23:59 UTC)
• Format: PostgreSQL timestamptz '2025-01-06T00:00:00Z'

Date Calculations:

• "This week" = Current Monday to next Monday
• "Last week" = Previous Monday to current Monday
• "Past 2 weeks" = Monday 2 weeks ago to next Monday

Link Formatting

Assets (videos/images):

https://civitai.com/images/{assets.civitai_id}

Posts:

https://civitai.com/posts/{civitai_posts.civitai_id}

Always include clickable links in results for easy navigation.

Analysis Guidelines

Engagement Metrics

• Positive engagement: likes + hearts + laughs
• Total engagement: all reactions + comments
• Engagement rate: total_engagement / asset_count

Pattern Recognition

• Compare top performers vs average
• Identify common tags in high-engagement videos
• Correlate quality_score with engagement
• Analyze motion_intensity impact

Comparative Analysis

When comparing videos (e.g., "rank 2 vs rank 9"):

• Extract shared tags
• Compare quality scores
• Analyze description/prompt similarities
• Identify differentiating factors

Recommendation Framework

Based on analysis, provide actionable suggestions:

1. Content themes - Which topics/tags drive engagement
2. Quality factors - Optimal quality_score ranges
3. Timing patterns - Best posting times if data shows trends
4. Improvement areas - Underperforming high-quality content

Example insights:

• "Anime + high-motion videos get 2x engagement"
• "Videos with quality_score > 0.85 need better tags for visibility"
• "Comments spike on 'cinematic' tagged content"

Report Generation

For weekly reports, use templates from references/report-templates.md:

• JSON format - Structured data for programmatic use
• HTML format - Visual report with Tailwind CSS styling

Generate reports by:

1. Run weekly-feedback-stats.sql for summary
2. Run top-performing-assets.sql for highlights
3. Run tag-performance.sql for content insights
4. Combine into report template

Language

Respond in the same language as the user's query.

• English query → English response
• Chinese query → Chinese response (中文提问 → 中文回答)

Reference Files