Santa Method

┌─────────────┐
│  生成器      │  阶段 1：列出清单
│  (代理 A)    │  生成交付物
└──────┬───────┘
       │ 输出
       ▼
┌──────────────────────────────┐
│     双重独立审查              │  阶段 2：复核两次
│                              │
│  ┌───────────┐ ┌───────────┐ │  两个代理，同一评分标准，
│  │ 审查者 B  │ │ 审查者 C  │ │  无共享上下文
│  └─────┬─────┘ └─────┬─────┘ │
│        │              │       │
└────────┼──────────────┼───────┘
         │              │
         ▼              ▼
┌──────────────────────────────┐
│       裁决门                  │  阶段 3：判定好坏
│                              │
│  B通过且C通过 → 好            │  两者必须通过。
│  否则 → 坏                    │  无例外。
└──────┬──────────────┬────────┘
       │              │
      好             坏
       │              │
       ▼              ▼
   [ 发布 ]    ┌─────────────┐
               │  修复循环    │  阶段 4：修复至通过
               │              │
               │ 迭代次数++   │  收集所有标记。
               │ 若 i > 最大: │  修复所有问题。
               │   升级处理   │  重新运行两个审查者。
               │ 否则:        │  循环直至收敛。
               │   跳至阶段2  │
               └──────────────┘

# The generator runs as normal
output = generate(task_spec)

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

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"  # 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

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)

    # 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)

# 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}"
)

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, rubric)
        if result.verdict == "NAUGHTY":
            pattern = classify_failure(result.issues)
            items = batch_fix(items, pattern)  # Fix all items matching pattern
            return santa_batch(items, rubric)   # Re-sample

    return items  # Clean sample → ship batch

圣诞老人的成本 = (生成代币) + 2×(每轮审查代币) × (平均轮数)
不做圣诞老人的成本 = (声誉损害) + (纠正努力) + (信任侵蚀)