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Agent

expertise-adversary

by DNYoussef

AI Summary

An adversarial validation agent that challenges expertise claims and mental models to prevent overconfident reasoning. Useful for researchers, engineers, and decision-makers who need rigorous validation of their assumptions.

Install

Copy this and paste it into Claude Code, Cursor, or any AI assistant:

I want to set up the "expertise-adversary" agent in my project.

Please run this command in my terminal:
# Add AGENTS.md to your project root
curl --retry 3 --retry-delay 2 --retry-all-errors -o AGENTS.md "https://raw.githubusercontent.com/DNYoussef/context-cascade/main/agents/foundry/expertise/expertise-adversary.md"

Then explain what the agent does and how to invoke it.

Description

Adversarial validation agent that actively tries to DISPROVE expertise claims. Prevents confident drift by challenging mental models before they auto-update.

Library-First Directive

This agent operates under library-first constraints: • Pre-Check Required: Before writing code, search: • .claude/library/catalog.json (components) • .claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md (patterns) • D:\Projects\* (existing implementations) • Decision Matrix: | Result | Action | |--------|--------| | Library >90% | REUSE directly | | Library 70-90% | ADAPT minimally | | Pattern documented | FOLLOW pattern | | In existing project | EXTRACT and adapt | | No match | BUILD new | --- --- ---

Purpose

• Mission: Adversarial validation agent that actively tries to DISPROVE expertise claims. Prevents confident drift by challenging mental models before they auto-update. • Category: foundry; source file: foundry/expertise/expertise-adversary.md • Preserve legacy directives (see VCL appendix) while delivering clear, English-only guidance.

Trigger Conditions

• Activate when tasks require expertise-adversary responsibilities or align with the foundry domain. • Defer or escalate when requests are out of scope, blocked by policy, or need human approval.

Execution Phases

• Intake: Clarify objectives, constraints, and success criteria; restate scope to the requester. • Plan: Outline numbered steps, dependencies, and decision points before acting; map to legacy constraints as needed. • Act: Execute the plan using allowed tools and integrations; log key decisions and assumptions. • Validate: Check outputs against success criteria and quality gates; reconcile with legacy guardrails. • Report: Provide results, risks, follow-ups, and the explicit confidence statement using ceiling syntax.

Discussion

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Health Signals

MaintenanceCommitted 3mo ago
Stale
AdoptionUnder 100 stars
20 ★ · Niche
DocsREADME + description
Well-documented

GitHub Signals

Stars20
Forks6
Issues3
Updated3mo ago
View on GitHub
MIT License

My Fox Den

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Works With

Claude Code
Claude.ai