Agent skill

Santa Method

by affaan-m in affaan-m/ECC

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.

MITAuto-check passedEducation

Install Santa Method

skills CLI
$ npx skills add affaan-m/ECC --skill santa-method -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC santa-method --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/santa-method .claude/skills/santa-method && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
santa-method
GitHub stars
276k
Used in
3 other repos
Token cost
~3.1k tokens
SKILL.md length
929 words
Files
1
Skills in repo
683
Repo updated
First seen
Licence
MIT

At a glance

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.

  • Works in 4 steps: Make a List (Generate) → Check It Twice (Independent Dual Review) → Naughty or Nice (Verdict Gate) → …
  • Gating publishing
  • SKILL.md covers When to Activate, Architecture, Phase Details and Implementation Patterns, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Santa Method is an agent skill from affaan-m/ECC. 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.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Education, covering Quizzes and assessments. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • Gating publishing
  • Production deploys
  • Brand-sensitive content
  • Hallucination-prone claims before they ship

Example prompts

  • “/santa-method”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Make a List (Generate)
  2. Check It Twice (Independent Dual Review)
  3. Naughty or Nice (Verdict Gate)
  4. Fix Until Nice (Convergence Loop)

What it can do on your machine

Read from SKILL.md and the folder at commit 4eb71d9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Santa Method loads about 3.1k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 929 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit 4eb71d9, republished under its MIT licence (© affaan-m). 929 words, ~3,092 tokens.

Download SKILL.mdSave it as .claude/skills/santa-method/SKILL.md (or your agent's skills folder).
name
santa-method
description
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.
metadata.origin
Ronald Skelton - Founder, RapportScore.ai

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.

python
# 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:

  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
python
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.
"""
python
# 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.

CriterionPass ConditionFailure Signal
Factual accuracyAll claims verifiable against source material or common knowledgeInvented statistics, wrong version numbers, nonexistent APIs
Hallucination-freeNo fabricated entities, quotes, URLs, or referencesLinks to pages that don't exist, attributed quotes with no source
CompletenessEvery requirement in the spec is addressedMissing sections, skipped edge cases, incomplete coverage
CompliancePasses all project-specific constraintsBanned terms used, tone violations, regulatory non-compliance
Internal consistencyNo contradictions within the outputSection A says X, section B says not-X
Technical correctnessCode compiles/runs, algorithms are soundSyntax 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)
python
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

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)
python
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)

Critical: each review round uses fresh agents. Reviewers must not carry memory from previous rounds, as prior context creates anchoring bias.

Implementation Patterns

Subagents provide true context isolation. Each reviewer is a separate process with no shared state.

bash
# In a Claude Code session, use the Agent tool to spawn reviewers
# Both agents run in parallel for speed
python
# 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:

  1. Generate output
  2. New context: "You are Reviewer 1. Evaluate ONLY against this rubric. Find problems."
  3. Record findings verbatim
  4. Clear context completely
  5. New context: "You are Reviewer 2. Evaluate ONLY against this rubric. Find problems."
  6. Compare both reviews, fix, repeat

The subagent pattern is strictly superior — inline simulation risks context bleed between reviewers.

Show full SKILL.md (361 more words)Show less
Pattern C: Batch Sampling

For large batches (100+ items), full Santa on every item is cost-prohibitive. Use stratified sampling:

  1. Run Santa on a random sample (10-15% of batch, minimum 5 items)
  2. Categorize failures by type (hallucination, compliance, completeness, etc.)
  3. If systematic patterns emerge, apply targeted fixes to the entire batch
  4. Re-sample and re-verify the fixed batch
  5. Continue until a clean sample passes
python
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

Failure Modes and Mitigations

Failure ModeSymptomMitigation
Infinite loopReviewers keep finding new issues after fixesMax iteration cap (3). Escalate.
Rubber stampingBoth reviewers pass everythingAdversarial prompt: "Your job is to find problems, not approve."
Subjective driftReviewers flag style preferences, not errorsTight rubric with objective pass/fail criteria only
Fix regressionFixing issue A introduces issue BFresh reviewers each round catch regressions
Reviewer agreement biasBoth reviewers miss the same thingMitigated by independence, not eliminated. For critical output, add a third reviewer or human spot-check.
Cost explosionToo many iterations on large outputsBatch sampling pattern. Budget caps per verification cycle.

Integration with Other Skills

SkillRelationship
Verification LoopUse for deterministic checks (build, lint, test). Santa for semantic checks (accuracy, hallucinations). Run verification-loop first, Santa second.
Eval HarnessSanta Method results feed eval metrics. Track pass@k across Santa runs to measure generator quality over time.
Continuous Learning v2Santa findings become instincts. Repeated failures on the same criterion → learned behavior to avoid the pattern.
Strategic CompactRun 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.

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/santa-method of affaan-m/ECC.

Open the folder on GitHubat commit 4eb71d9

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

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Scholar EvaluationK-Dense-AI/claude-scientific-writer2.4k2 repos~2.9kAutomated safety check: NotesMIT

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Categories

Questions about Santa Method

What does Santa Method do?

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. Santa Method is an agent skill from affaan-m/ECC. 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.

When should I use Santa Method?

Santa Method fits situations like: gating publishing; production deploys; brand-sensitive content; hallucination-prone claims before they ship.

How do I install Santa Method in Claude Code?

Run `npx skills add affaan-m/ECC --skill santa-method -a claude-code`. Or copy the skill folder (skills/santa-method in affaan-m/ECC) into .claude/skills/santa-method in your project. Claude Code loads it when a task matches its description.

How do I install Santa Method in Codex?

Run `npx skills add affaan-m/ECC --skill santa-method -a codex`. Or copy the skill folder (skills/santa-method in affaan-m/ECC) into .agents/skills/santa-method in your project. Codex loads it when a task matches its description.

Can I use Santa Method in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill santa-method -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/santa-method, .gemini/skills/santa-method, .github/skills/santa-method and .opencode/skills/santa-method in your project.

What does Santa Method need to run?

SKILL.md names no scripts, command-line tools or credentials: Santa Method is instructions for the agent only. Our summary lists: Python 3.

Does Santa Method access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Santa Method safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Santa Method use?

Santa Method is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Santa Method use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Santa Method?

Skills that share tags, products or a category with Santa Method: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 66k stars), Codebase to Course (zarazhangrui/codebase-to-course, 5.7k stars) and AI Engineering Phase Quiz (rohitg00/ai-engineering-from-scratch, 66k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Santa Method?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,111 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 10, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.