A multi-agent adversarial verification framework that uses independent reviewers to validate AI-generated content through a convergence loop.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install lobster-santa-method
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install lobster-santa-method using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Santa Method?
The Santa Method is an advanced architectural pattern for Openclaw Skills designed to solve the inherent bias of single-agent self-review. The core insight is that an agent reviewing its own work often shares the same knowledge gaps that created the errors. By spawning two independent reviewers with no shared context, this skill breaks the failure mode of recursive bias.
Operating as a post-generation verification layer, the Santa Method ensures that outputs meet strict rubric criteria before they are finalized. It utilizes a 'Naughty or Nice' gate where both reviewers must independently pass the content. If issues are flagged, a fix-loop iterates on the output until convergence is reached or the task is escalated to a human, ensuring high-fidelity results for production-grade environments.
Santa Method Use Cases
- Deploying code to production without manual human oversight
- Generating customer-facing technical documentation or educational materials
- Enforcing strict compliance, regulatory, or brand voice constraints
- Mitigating hallucination risks in data-heavy or legal content
- Managing batch generation at scale where individual spot-checking is impossible
How Santa Method Works
- Phase 1: Make a List: The generator agent produces the initial deliverable based on the task specification.
- Phase 2: Check It Twice: Two independent review agents are spawned in parallel, receiving the output and a strict pass/fail rubric without seeing each other's work.
- Phase 3: Naughty or Nice: A verdict gate checks the reviews; the output is marked 'Nice' only if both reviewers issue a pass. If either fails, all issues are collected.
- Phase 4: Fix Until Nice: A fix agent addresses the flagged issues, and the updated output is sent back to Phase 2 for a fresh round of independent review.
- Convergence: The loop continues until the output passes or the maximum iteration limit is reached, at which point it escalates to a human.
Santa Method Setup
To implement the Santa Method within your environment, ensure you have the capability to spawn subagents for context isolation.
# Example: Invoking the Santa Method logic via Claude Code subagents
# No specific installation required; integrate the prompt logic into your agent workflow.
Define your specific evaluation rubric in a JSON format or structured table to guide the independent reviewers effectively.
Santa Method Data Schema & Taxonomy
The skill organizes its evaluation data using structured JSON for reviewer feedback to ensure machine-readability during the fix cycle.
| Data Point | Type | Description |
|---|---|---|
verdict |
String | Must be either "PASS" or "FAIL". |
checks |
Array | List of rubric criteria with individual PASS/FAIL results and details. |
critical_issues |
Array | High-priority blockers that trigger the fix-loop. |
suggestions |
Array | Optional non-blocking improvements. |
iteration_count |
Integer | Tracks how many rounds of review have occurred. |
Santa Method Advanced Features
- Claude Code Subagents: Utilizes true context isolation by running reviewers as separate processes.
- Batch Sampling: Cost-effective strategy for large datasets by applying Santa Method to a 15% sample and identifying systematic patterns.
- Adversarial Prompting: Specific instructions that force reviewers to be hyper-critical to avoid 'rubber-stamping' outputs.
- Convergence Metrics: Tracks first-pass rates and mean iterations to measure the quality of the primary generator over time.
- Skill Integration: Compatible with Verification Loop for deterministic checks and Continuous Learning v2 for long-term behavioral improvement.
SKILL.md
Loading
Related Openclaw Skills
Lobster Dev Planner
A conversational AI project manager and lead architect that orchestrates multi-agent teams to transform ideas into enterprise-ready software.
WeChat Article Deep Analyzer
A sophisticated analysis tool that transforms unstructured WeChat Official Account articles into structured insights, including timelines, key figures, and core facts.
Prompt Refiner
A professional utility that transforms ambiguous user requests into actionable, structured prompts based on a rigorous four-element framework.
Site Summarizer
A powerful URL fetcher and summarization tool designed to extract key insights from web pages automatically.
Space Query Skill
A professional query generation tool for network mapping platforms like FOFA, Shodan, and Quake to identify exposed assets and vulnerabilities.
Dispatching Parallel Agents
A sophisticated workflow for delegating independent programming tasks to multiple specialized agents simultaneously to accelerate problem-solving.
Featured*
