adversarial-qc
adversarial-qc is a Multi-agent review & consensus skill for AI agents, published by LegalQuants in the lq-skills repository on GitHub. Skillspace rates adversarial-qc B (81/100) and it passed the safety scan; the rating is based on static analysis only.
Adversarial quality control for AI deliverables. Run structured parallel verification using one or two models before delivering reports, plans, analyses, scripts, or any substantive work. Use when: (1) reviewing a deliverable before sending to a human, (2) user asks to 'QC this', 'review this', 'check this', 'verify this', (3) user wants a quality gate on AI output, (4) validating factual claims, numbers, logic, or completeness. Configurable: single-model (two agents, same model) or cross-model (two different providers). Outputs a QC certificate with pass/fail/review items and evidence.
- Grade
- B
- Quality
- 81 / 100
- Safety
- Passed
- GitHub stars
- 40
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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