code-combat

Run multi-round adversarial negotiation to converge documentation findings with Codex fact-checking.

Updated Mar 22, 2026
One-click install
npx skills add https://github.com/engineai-nz/engineai-skills --skill code-combat
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: code-combat
Source: https://github.com/engineai-nz/engineai-skills/tree/main/review/code-combat
Command: npx skills add https://github.com/engineai-nz/engineai-skills --skill code-combat

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Code Combat resolves disagreements from an adversarial documentation review by running a structured, multi-round negotiation until the findings converge on actionable, evidence-based documentation improvements.

Core Features & Use Cases

  • Rebuttal-driven debate: Classifies opponent messages into concession, defence, counter-proposal, new information, new question, deflection, or scope shift to guide what to do next.
  • Fact-checking via Codex: Verifies each opponent’s technical claims against official documentation to prevent incorrect or outdated assertions from carrying through.
  • Resolution ledger tracking: Maintains a per-combat ledger of open, contested, resolved, and conceded findings so continuity is preserved across rounds.
  • Convergence and time-boxing: Detects when enough P0/P1 items are resolved (or deferred with rationale) and time-boxes repeated disputes to require human judgement.

Quick Start

Ask the assistant to run code-combat after an adversarial-review exists and you provide the opposing agent’s rebuttal response, so it can fact-check and produce a Round response plus an updated resolution ledger.

Frequently Asked Questions about code-combat

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

FAQPage Schema
How do I resolve disagreements from an adversarial documentation review?

To resolve disagreements from an adversarial documentation review, run structured multi-round adversarial negotiation that converges on actionable improvements. The process classifies opponent rebuttals, fact-checks claims, and tracks resolution status until findings converge or require human judgement.

How does fact-checking work during an AI debate over project documentation?

Fact-checking during an AI debate over project documentation works by invoking Codex to verify each opponent's technical claims against official documentation. This prevents incorrect or outdated assertions from carrying through the negotiation rounds, ensuring evidence-based documentation improvements.

What is a resolution ledger and how does it track adversarial review findings?

A resolution ledger tracks adversarial review findings by maintaining a per-combat record of open, contested, resolved, and conceded items. Stored as ledger.json, it preserves continuity across negotiation rounds so disputed documentation points are systematically tracked until convergence.

When should I stop an adversarial negotiation over build documentation?

Stop an adversarial negotiation over build documentation when convergence rules detect enough P0 and P1 items are resolved or deferred with rationale. Time-boxing rules also halt repeated disputes to require human judgement when progress stalls.

Can I receive adversarial review rebuttal results via Telegram delivery?

Yes, you can receive adversarial review rebuttal results via Telegram delivery. After classifying opponent responses, fact-checking claims, and updating the resolution ledger, the skill optionally delivers the drafted round response markdown directly through Telegram.

Do I need a prior adversarial-review artifact to start an AI documentation debate?

Yes, a prior adversarial-review artifact is required to start an AI documentation debate. The skill applies when a new opponent response references existing findings, loading prior findings and ledger state to fact-check and produce the next round response.