Agent skill · product strategy · affaan-m
santa-method
Multi-agent adversarial verification: two independent reviewers with the same rubric must both pass before output ships, with a fix-and-re-review convergence loop and human escalation cap. Use when gating publishing, production deploys, compliance or brand-sensitive content, or hallucination-prone claims before they ship.
Why this skill is useful
Introduces a structured multi-agent review process that mitigates biases and improves output quality through independent verification.
What it needs
About 7k tokens when loaded. Last updated 2026-08-06. 238,342 stars on the source repository.
What this skill does
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). Architecture Phase Details Phase 1: Make a List (Generate) Execute the primary task. No changes to your normal generation workflow. Santa Method is a post-generation verification layer, not a generation strategy. Phase 2: Check It Twice (Independent Dual Review) Spawn two review agents in parallel. Critical invariants: 1. Context isolation — neither reviewer sees the other's assessment 2. Identical rubric — both receive the same evaluation criteria 3. Same inputs — both receive the original spec AND the generated output 4. Structured output — each returns a typed verdict, not prose Rubric Design The rubric is the most important input. Vague rubrics produce vague reviews. Every criterion must have an objective pass/fail condition. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills affaan-m/santa-method--212a76