buff-counterfeit

Automate Buff's three-tier memory system across global and project scopes.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/qkal/claude-beta-script --skill buff-counterfeit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: buff-counterfeit
Source: https://github.com/qkal/claude-beta-script/tree/main/skills/counterfeit
Command: npx skills add https://github.com/qkal/claude-beta-script --skill buff-counterfeit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Counterfeit automates Buff's three-tier memory system (preferences, patterns, corrections) across global and project contexts to ensure persistent recall, faster context switching, and smarter responses.

Core Features & Use Cases

  • Merges global preferences with project-specific context to guide interactions.
  • Captures corrections and learnings to improve future outputs and decisions.
  • Supports on-demand graph updates and lean memory payloads for large projects.

Quick Start

Ask Buff to show memory to review current preferences and recent corrections.

Frequently Asked Questions about buff-counterfeit

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

FAQPage Schema
How do I keep AI memory and user preferences consistent across coding sessions?▼

Persistent AI memory is managed by a three-tier system that records preferences, patterns, and corrections across global and project scopes to keep context consistent between sessions.

How do I save AI corrections so they apply to future project outputs?▼

Saving AI corrections is handled during ongoing interactions by capturing user feedback and applying project-level overrides, ensuring learnings improve future outputs and decisions automatically.

Can I merge global AI preferences with project-specific context?▼

Merging global AI preferences with project-specific context is supported, allowing project-level overrides to align learning with the current working environment while retaining broader session history.

What is the best way to update a knowledge graph incrementally during a session?▼

Incremental graph updates are supported on-demand, applying during session starts and correction flows to maintain lean memory payloads while preserving user intent and decisions for large projects.

How do I review current AI memory and recent corrections?▼

Reviewing current AI memory requires asking Buff to show memory, which displays active preferences, captured patterns, and recent corrections across the global and project scopes.