Monet Works — Content QA Remediation

QA remediation auto-fix pipeline for Monet Works content. Detects and repairs common content issues: banned phrases, missing disclaimers, missing CTAs, and e...

This package implements a plausible content QA fixer, but before you run it on real drafts: 1) Confirm which executable to run (the repo ships scripts/remediate.sh and scripts/auto-remediate.py; SKILL.md's 'content-qa' name is inconsistent). 2) Inspect auto-remediate.py for any network/HTTP calls or explicit calls to an LLM SDK (openai/anthropic); if it calls an external API, decide where API keys will come from and avoid running it with privileged credentials. 3) Because the docs reference 'references/' but templates are in data/, ensure the script will find the correct template files in your environment or update the paths. 4) If you expect the skill to call another internal skill (ogilvy-humanizer), get clarity on that integration and required permissions. 5) Run the script on non-sensitive test content in an isolated environment first and review the change-report JSON before trusting automated modifications. If the author can confirm (a) whether the script makes network/LLM calls and (b) the exact env vars needed, that would raise confidence and may resolve the current concerns.

Type: OpenClaw Skill Name: monet-works-content-qa-dv Version: 0.1.0 The Monet Works Content QA Pipeline is a legitimate tool designed for automated text remediation, such as replacing banned phrases and appending legal disclaimers. The core logic in 'scripts/auto-remediate.py' uses standard Python libraries for regex-based text processing and JSON handling, with no evidence of malicious execution, data exfiltration, or unauthorized network activity. While the README.md mentions LLM dependencies (OpenAI/Anthropic), the provided code relies on static data files in the 'data/' directory, and no suspicious instructions were found in the 'SKILL.md' prompt surface.