Multi-agent adversarial verification with convergence loop. Two independent review agents must both pass before output ships. Use when output must clear two independent adversarial reviewers before it ships.
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Multi-agent adversarial verification with convergence loop. Two independent review agents must both pass before output ships. Use when output must clear two independent adversarial reviewers before it ships.
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
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.
# The generator runs as normal
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.
"""
# 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")
# Both run concurrently — neither sees the other
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
defsanta_verdict(review_b, review_c):
"""Both reviewers must pass. No partial credit."""if review_b.verdict == "PASS"and review_c.verdict == "PASS":
return"NICE"# Ship it# Merge flags from both reviewers, deduplicate
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 = 3for iteration inrange(MAX_ITERATIONS):
verdict, issues, suggestions = santa_verdict(review_b, review_c)
if verdict == "NICE":
log_santa_result(output, iteration, "passed")
return ship(output)
# Fix all critical issues (suggestions are optional)
output = fix_agent.execute(
output=output,
issues=issues,
instruction="Fix ONLY the flagged issues. Do not refactor or add unrequested changes."
)
# Re-run BOTH reviewers on fixed output (fresh agents, no memory of previous round)
review_b = Agent(prompt=REVIEWER_PROMPT.format(output=output, ...))
review_c = Agent(prompt=REVIEWER_PROMPT.format(output=output, ...))
# Exhausted iterations — escalate
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.
# In a Claude Code session, use the Agent tool to spawn reviewers# Both agents run in parallel for speed
# Pseudocode for Agent tool invocation
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
defsanta_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, rubric)
if result.verdict == "NAUGHTY":
pattern = classify_failure(result.issues)
items = batch_fix(items, pattern) # Fix all items matching patternreturn santa_batch(items, rubric) # Re-samplereturn items # Clean sample → ship batch
Failure Modes and Mitigations
Failure Mode
Symptom
Mitigation
Infinite loop
Reviewers keep finding new issues after fixes
Max iteration cap (3). Escalate.
Rubber stamping
Both reviewers pass everything
Adversarial prompt: "Your job is to find problems, not approve."
Subjective drift
Reviewers flag style preferences, not errors
Tight rubric with objective pass/fail criteria only
Fix regression
Fixing issue A introduces issue B
Fresh reviewers each round catch regressions
Reviewer agreement bias
Both reviewers miss the same thing
Mitigated by independence, not eliminated. For critical output, add a third reviewer or human spot-check.
Cost explosion
Too many iterations on large outputs
Batch sampling pattern. Budget caps per verification cycle.
Integration with Other Skills
Skill
Relationship
Verification Loop
Use for deterministic checks (build, lint, test). Santa for semantic checks (accuracy, hallucinations). Run verification-loop first, Santa second.
Eval Harness
Santa Method results feed eval metrics. Track pass@k across Santa runs to measure generator quality over time.
Continuous Learning v2
Santa findings become instincts. Repeated failures on the same criterion → learned behavior to avoid the pattern.
Strategic Compact
Run Santa BEFORE compacting. Don't lose review context mid-verification.
Metrics
Track these to measure Santa Method effectiveness:
First-pass rate: % of outputs that pass Santa on round 1 (target: >70%)
Mean iterations to convergence: average rounds to NICE (target: <1.5)
Issue taxonomy: distribution of failure types (hallucination vs. completeness vs. compliance)
Reviewer agreement: % of issues flagged by both reviewers vs. only one (low agreement = rubric needs tightening)
Escape rate: issues found post-ship that Santa should have caught (target: 0)
Cost Analysis
Santa Method costs approximately 2-3x the token cost of generation alone per verification cycle. For most high-stakes output, this is a bargain:
Cost of Santa = (generation tokens) + 2×(review tokens per round) × (avg rounds)
Cost of NOT Santa = (reputation damage) + (correction effort) + (trust erosion)
For batch operations, the sampling pattern reduces cost to ~15-20% of full verification while catching >90% of systematic issues.
