nhouseholder/anti-slop
skills/anti-slop/SKILL.md
Zero tolerance for placeholder data in structured output. Every field must be real data or explicitly flagged as unfound. Always-on quality gate.
$ install --global
skillsauthnpx skillsauth add nhouseholder/nicks-claude-code-superpowers anti-slopInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 9, 2026, 2:40 AM20.4s1 file scanned
SKILL.md
- name:
- anti-slop
- description:
- Zero tolerance for placeholder data in structured output. Every field must be real data or explicitly flagged as unfound. Always-on quality gate.
- weight:
- passive
Anti-Slop — No Placeholder Garbage
Rules for Structured Output
- No silent placeholders — "Unknown", "N/A", "TBD" are never acceptable without explanation
- Research before defaulting — look it up before writing "Unknown"
- Flag gaps explicitly — "Not found — [reason]" beats "Unknown"
- Never mass-default — same placeholder across many records = systematic failure
- Partial data beats no data — "Published ~2020" beats "Unknown"
- Spot-check high-stakes output — 3 random entries minimum for financial/user-facing data
What To Do Instead
| Bad | Good | |-----|------| | "Author: Unknown" | "Author: Could not determine — paper via aggregator without attribution" | | "DOI: N/A" | "DOI: Not assigned (preprint, arXiv:2024.12345)" | | All dates "2024" | Research each actual date | | Repeated identical values | Each record gets its own real data | | Made-up data as real | Label clearly as example/generated |
When you can't find data: search harder → use partial data → flag specifically → ask the user → deliver with explicit gap report.
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