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
┌─────────────┐
│ 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 │
└──────────────┘
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.
output = generate(task_spec)
Phase 2: Check It Twice (Independent Dual Review)
Spawn two review agents in parallel. Critical invariants:
- Context isolation — neither reviewer sees the other's assessment
- Identical rubric — both receive the same evaluation criteria
- Same inputs — both receive the original spec AND the generated output
- Structured output — each returns a typed verdict, not prose
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.
"""
review_b = Agent(prompt=REVIEWER_PROMPT.format(...), description="Santa Reviewer B")
review_c = Agent(prompt=REVIEWER_PROMPT.format(...), description="Santa Reviewer C")
Rubric Design
The rubric is the most important input. Vague rubrics produce vague reviews. Every criterion must have an objective pass/fail condition.
| Criterion |
Pass Condition |
Failure Signal |
| Factual accuracy |
All claims verifiable against source material or common knowledge |
Invented statistics, wrong version numbers, nonexistent APIs |
| Hallucination-free |
No fabricated entities, quotes, URLs, or references |
Links to pages that don't exist, attributed quotes with no source |
| Completeness |
Every requirement in the spec is addressed |
Missing sections, skipped edge cases, incomplete coverage |
| Compliance |
Passes all project-specific constraints |
Banned terms used, tone violations, regulatory non-compliance |
| Internal consistency |
No contradictions within the output |
Section A says X, section B says not-X |
| Technical correctness |
Code compiles/runs, algorithms are sound |
Syntax errors, logic bugs, wrong complexity claims |
Domain-Specific Rubric Extensions
Content/Marketing:
- Brand voice adherence
- SEO requirements met (keyword density, meta tags, structure)
- No competitor trademark misuse
- CTA present and correctly linked
Code:
- Type safety (no
any leaks, proper null handling)
- Error handling coverage
- Security (no secrets in code, input validation, injection prevention)
- Test coverage for new paths
Compliance-Sensitive (regulated, legal, financial):
- No outcome guarantees or unsubstantiated claims
- Required disclaimers present
- Approved terminology only
- Jurisdiction-appropriate language
Phase 3: Naughty or Nice (Verdict Gate)
def santa_verdict(review_b, review_c):
"""Both reviewers must pass. No partial credit."""
if review_b.verdict == "PASS" and review_c.verdict == "PASS":
return "NICE"
all_issues = dedupe(review_b.critical_issues + review_c.critical_issues)
all_suggestions = dedupe(review_b.suggestions + review_c.suggestions)
return "NAUGHTY", all_issues, all_suggestions
Why both must pass: if only one reviewer catches an issue, that issue is real. The other reviewer's blind spot is exactly the failure mode Santa Method exists to eliminate.
Phase 4: Fix Until Nice (Convergence Loop)
MAX_ITERATIONS = 3
for iteration in range(MAX_ITERATIONS):
verdict, issues, suggestions = santa_verdict(review_b, review_c)
if verdict == "NICE":
log_santa_result(output, iteration, "passed")
return ship(output)
output = fix_agent.execute(
output=output,
issues=issues,
instruction="Fix ONLY the flagged issues. Do not refactor or add unrequested changes."
)
review_b = Agent(prompt=REVIEWER_PROMPT.format(output=output, ...))
review_c = Agent(prompt=REVIEWER_PROMPT.format(output=output, ...))
log_santa_result(output, MAX_ITERATIONS, "escalated")
escalate_to_human(output, issues)
Critical: each review round uses fresh agents. Reviewers must not carry memory from previous rounds, as prior context creates anchoring bias.
Implementation Patterns
Pattern A: Claude Code Subagents (Recommended)
Subagents provide true context isolation. Each reviewer is a separate process with no shared state.
reviewer_b = Agent(
description="Santa Review B",
prompt=f"Review this output for quality...\n\nRUBRIC:\n{rubric}\n\nOUTPUT:\n{output}"
)
reviewer_c = Agent(
description="Santa Review C",
prompt=f"Review this output for quality...\n\nRUBRIC:\n{rubric}\n\nOUTPUT:\n{output}"
)
Pattern B: Sequential Inline (Fallback)
When subagents aren't available, simulate isolation with explicit context resets:
- Generate output
- New context: "You are Reviewer 1. Evaluate ONLY against this rubric. Find problems."
- Record findings verbatim
- Clear context completely
- New context: "You are Reviewer 2. Evaluate ONLY against this rubric. Find problems."
- Compare both reviews, fix, repeat
The subagent pattern is strictly superior — inline simulation risks context bleed between reviewers.
Pattern C: Batch Sampling
For large batches (100+ items), full Santa on every item is cost-prohibitive. Use stratified sampling:
- Run Santa on a random sample (10-15% of batch, minimum 5 items)
- Categorize failures by type (hallucination, compliance, completeness, etc.)
- If systematic patterns emerge, apply targeted fixes to the entire batch
- Re-sample and re-verify the fixed batch
- Continue until a clean sample passes
import random
def santa_batch(items, rubric, sample_rate=0.15):
sample = random.sample(items, max(5, int(len(items) * sample_rate)))
for item in sample:
result = santa_full(item,
