sillytavern-overseer

Enforce a four-stage oversight protocol with mandatory annotations for coding tasks.

322|29|Updated Aug 18, 2025
One-click install
npx skills add https://github.com/linkerlin/PUAX --skill sillytavern-overseer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sillytavern-overseer
Source: https://github.com/linkerlin/PUAX/tree/main/skills/sillytavern-overseer
Command: npx skills add https://github.com/linkerlin/PUAX --skill sillytavern-overseer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses AI performance issues by implementing a strict, high-pressure oversight system to ensure maximum productivity and adherence to protocols, preventing AI "slacking" or low-quality output.

Core Features & Use Cases

  • Four-Stage Process: Guides AI through scanning, solutioning, execution, and self-checking.
  • Mandatory Output Annotation: Requires specific "target output" and "failure signal" for each step.
  • Rigorous Checklist: Enforces a multi-point checklist for verification and problem-solving.
  • Use Case: When an AI is struggling with a complex coding task and producing suboptimal results, this Skill can be activated to force a structured, high-accountability approach, ensuring all steps are meticulously followed and verified.

Quick Start

Use the sillytavern-overseer skill to manage the task of designing a highly available microservice circuit breaker mechanism.

Frequently Asked Questions about sillytavern-overseer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I enforce strict supervision and quality assurance for AI agents during software development?▼

You can enforce AI agent quality assurance by applying a high-pressure oversight protocol that mandates a four-stage execution process: scan, solution, execute, and self-check. This enforces strict accountability and prevents suboptimal output during software development.

What is the best way to stop an AI from producing low-quality code on complex debugging tasks?▼

The best way to stop an AI from producing low-quality code on complex debugging tasks is to activate a structured oversight system. This requires the AI to annotate target outputs and failure signals at each step before passing a rigorous verification checklist.

How does the four-stage scan, solution, execute, and self-check process work for AI agents?▼

The four-stage process works by forcing the AI agent to sequentially scan the problem, design a solution, execute the task, and perform a self-check. Each stage requires mandatory annotations for target output and failure signals to ensure meticulous execution and verification.

Can I use a mandatory checklist to improve AI agent productivity and execution accuracy?▼

Yes, you can use a mandatory multi-point checklist to improve AI agent productivity and execution accuracy. This checklist enforces rigorous verification and problem-solving accountability, ensuring the AI meticulously follows all required steps without slacking.

Does this AI supervision protocol require specific dependencies or platforms to function?▼

No, this AI supervision protocol does not require specific dependencies or platforms to function. It is a standalone oversight system designed to enforce high-pressure accountability and quality assurance directly within your existing software development workflows.

When should I avoid using a high-pressure oversight protocol for AI debugging?▼

You should avoid using a high-pressure oversight protocol for AI debugging when dealing with simple tasks that do not require meticulous execution or complex verification. In such cases, the mandatory four-stage process and rigorous checklists may unnecessarily reduce productivity.