Santa Method

Two independent review agents evaluate your output against a rubric before it ships. Neither sees the other's assessment, breaking the bias problem where a single reviewer shares the same blind spots as the generator. Both must pass or the output loops back through a fix cycle, up to three iterations before human escalation. The rubric is everything here: vague criteria get vague reviews, so the framework pushes you to define objective pass/fail conditions for accuracy, completeness, compliance, whatever matters for your domain. Best for production code, customer-facing content, or anything where hallucinations and systematic errors are expensive. Overkill for drafts and exploration.

skills.sh installs: 5.4K
GitHub stars: 300.3K
last updated: Aug 21, 2026

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Install

$ npx -y skills add affaan-m/ecc --skill santa-method --agent claude-code

Installs into .claude/skills of the current project.

Files

Santa Method

Multi-agent adversarial verification framework. Make a list, check it twice. If it's naughty, fix it until it's nice.

The core insight: a single agent reviewing its own output shares the same biases, knowledge gaps, and systematic errors that produced the output. Two independent reviewers with no shared context break this failure mode.

When to Activate

Invoke this skill when:

Output will be published, deployed, or consumed by end users
Compliance, regulatory, or brand constraints must be enforced
Code ships to production without human review
Content accuracy matters (technical docs, educational material, customer-facing copy)
Batch generation at scale where spot-checking misses systemic patterns
Hallucination risk is elevated (claims, statistics, API references, legal language)

Do NOT use for internal drafts, exploratory research, or tasks with deterministic verification (use build/test/lint pipelines for those).