Monet Works Content QA Pipeline
QA remediation auto-fix pipeline for Monet Works content. Detects and repairs common content issues: banned phrases, missing disclaimers, missing CTAs, and excessive length. Outputs fixed content to stdout and a structured change-report JSON to stderr.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/clawdiri-ai/monet-works-content-qa-dvMonet Works Content QA Pipeline
Goal Chain: L0 Medici Enterprises → L1 Monet Works → L2 Content Quality
Purpose
This skill is an automated QA and remediation pipeline for all content produced by Monet Works. It detects and automatically fixes common content issues, ensuring brand consistency and quality before publication.
How It Works
The content-qa CLI tool takes a markdown file as input and processes it through a series of checks and fixes. It outputs the remediated content to stdout and a structured JSON report of all changes to stderr.
Checks and Fixes
-
Banned Phrases:
- Detects: A list of 37+ banned phrases (e.g., "in conclusion," "game-changer," "level up").
- Fix: Automatically removes or rephrases them.
-
Missing Disclaimers:
- Detects: If the content discusses investments, finance, or taxes without the standard legal disclaimer.
- Fix: Appends the required disclaimer to the end of the document.
-
Missing Call-to-Action (CTA):
- Detects: If the content does not end with a clear CTA (e.g., a link to a product, a request to subscribe).
- Fix: Appends a default, context-aware CTA based on the content's topic.
-
Excessive Length:
- Detects: If the word count exceeds platform-specific limits (e.g., >600 words for LinkedIn).
- Fix: Uses an AI model to generate a concise summary and offers it as an alternative.
-
AI Writing Patterns:
- Detects: Integrates with the
ogilvy-humanizerskill to detect and fix common AI writing tics. - Fix: Applies the humanizer's transformations.
- Detects: Integrates with the
Usage
CLI
# Run the QA pipeline on a draft file
content-qa fix path/to/draft.md > path/to/fixed-draft.md 2> path/to/report.json
# Example report.json
{
"changes": [
{
"type": "banned_phrase",
"action": "removed",
"original": "In conclusion, this is a great product.",
"remediated": "This is a great product.",
"line": 42
},
{
"type": "missing_disclaimer",
"action": "appended",
"details": "Appended standard investment disclaimer."
}
],
"word_count": {
"original": 750,
"remediated": 738
},
"humanizer_score": {
"before": 0.68,
"after": 0.95
}
}
Integration
This is the final step in the Monet Works content generation pipeline, run just before scheduling a post.
# Part of the main content pipeline script
cat $DRAFT_FILE | content-qa fix > $FINAL_FILE 2> $QA_REPORT_FILE
Configuration
- Banned Phrases: Managed in
references/banned_phrases.txt. - Disclaimers: Templates in
references/disclaimers/. - CTAs: Default CTAs in
references/ctas.json.
This automated QA process saves hours of manual editing and ensures every piece of content meets our quality standards.
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-clawdiri-ai-monet-works-content-qa-dv": {
"enabled": true,
"auto_update": true
}
}
}Related Skills
Follow-Through Day (FTD) Detector
Detects Follow-Through Day (FTD) signals for market bottom confirmation using William O'Neil's methodology. Dual-index tracking (S&P 500 + NASDAQ) with state machine for rally attempt, FTD qualification, and post-FTD health monitoring. Use when user asks about market bottom signals, follow-through days, rally attempts, re-entry timing after corrections, or whether it's safe to increase equity exposure. Complementary to market-top-detector (defensive) - this skill is offensive (bottom confirmation).
Gumroad Page Generator
Generates Gumroad product page content from a simple spec.
Analytics Tracking
Track events and user behavior using Segment.
AutoSignals - Autonomous Trading Signal Optimization
Monitors and controls the AutoSignals autonomous research loop.
first-principles
Deep first-principles analysis of any topic, decision, strategy, or assumption. Strips inherited thinking, identifies what is provably true, and rebuilds from ground truth. Use when user asks for first principles analysis, wants to challenge assumptions, says "analyze this from scratch", "break this down", "what's really true here", or triggers with /firstprinciples. Also useful for strategic decisions, investment theses, product strategy, career moves, or any situation where conventional wisdom may be wrong.