Use dual review agents to adversarially verify outputs before release.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "santa-method" skill from askskill: 1. Download https://raw.githubusercontent.com/affaan-m/ECC/main/skills/santa-method/SKILL.md 2. Save it as ~/.claude/skills/santa-method/SKILL.md 3. Reload skills and tell me it's ready
Use santa-method to review this code commit: have two independent review agents check functional correctness, edge cases, regression risk, and security issues. If they disagree, run a convergence loop until both pass, then return ship/no-ship, a risk summary, and fix suggestions. Here are the code and change notes:
A review report with both verdicts, convergence results, release recommendation, and fixes.
Use santa-method to audit this product requirements document: have two independent agents review requirement completeness, logical consistency, user scenario coverage, and ambiguity. If either fails, iterate with revision suggestions until both pass, then output the final issue list and revision recommendations. Document:
A document review showing gaps and ambiguities, plus revisions that passed dual checks.
Use santa-method to check this research summary: have two independent agents verify whether conclusions are supported by evidence, citations are accurate, and reasoning is sound. If disputed, run a convergence loop until both approve, then output a credibility assessment and required corrections. Content:
A research validation report with evidence checks, citation review, credibility rating, and corrections.
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.
Invoke this skill when:
Do NOT use for internal drafts, exploratory research, or tasks with deterministic verification (use build/test/lint pipelines for those).
┌─────────────┐
│ GENERATOR │ Phase 1: Make a List
│ (Agent A) │ Produce the deliverable
└──────┬───────┘
│ output
▼
┌──────────────────────────────┐
│ DUAL INDEPENDENT REVIEW │ Phase 2: Check It Twice
│ │
│ ┌───────────┐ ┌───────────┐ │ Two agents, same rubric,
│ │ Reviewer B │ │ Reviewer C │ │ no shared context
│ └─────┬─────┘ └─────┬─────┘ │
│ │ │ │
└────────┼──────────────┼────────┘
│ │
▼ ▼
┌──────────────────────────────┐
│ VERDICT GATE │ Phase 3: Naughty or Nice
│ │
│ B passes AND C passes → NICE │ Both must pass.
│ Otherwise → NAUGHTY │ No exceptions.
└──────┬──────────────┬─────────┘
│ │
NICE NAUGHTY
│ │
▼ ▼
[ SHIP ] ┌─────────────┐
│ FIX CYCLE │ Phase 4: Fix Until Nice
│ │
│ iteration++ │ Collect all flags.
│ if i > MAX: │ Fix all issues.
│ escalate │ Re-run both reviewers.
│ else: │ Loop until convergence.
│ goto Ph.2 │
└──────────────┘
Execute the primary task. No changes to your normal generation workflow. Santa Method is a post-generation verification layer, not a generation strategy.
# The generator runs as normal
output = generate(task_spec)
Spawn two review agents in parallel. Critical invariants:
REVIEWER_PROMPT = """
You are an independent quality reviewer. You have NOT seen any other review of this output.
## Task Specification
{task_spec}
## Output Under Review
{output}
## Evaluation Rubric
{rubric}
## Instructions
Evaluate the output against EACH rubric criterion. For each:
- PASS: criterion fully met, no issues
- FAIL: specific issue found (cite the exact problem)
Return your assessment as structured JSON:
{
"verdict": "PASS" | "FAIL",
"checks": [
{"criterion": "...", "result": "PASS|FAIL", "detail": "..."}
],
"critical_issues": ["..."], // blockers that must be fixed
"suggestions": ["..."] // non-blocking improvements
}
Be rigorous. Your job is to find problems, not to approve.
"""
# Spawn reviewers in parallel (Claude Code subagents)
review_b = Agent(prompt=REVIEWER_PROMPT.format(...), description="Santa Reviewer B")
review_c = Agent(prompt=REVIEWER_PROMPT.format(...), description="Santa Reviewer C")
…
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