The Order of the Agents – Make Codex and Claude Create the Perfect PRD

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Should we plan with Codex, then code with Claude? Or should we plan with Claude, then code with Codex? How about yes. I found myself converging to this workflow for more complicated features. I would give the same prompt to Codex and Claude and ask each to create a PRD file for the feature. The independence mattered, because the moment one model sees the other's plan, the answer collapses toward whoever spoke first. Once I had the PRDs, I would manually ask both Codex and Claude to critique the other plan, and revise theirs based on the findings of the other. Eventually I would converge to a final PRD where both models had reached agreement. This final PRD was meaningfully better than what either model produced alone. Doing this manually was annoying, so I packaged it. Presenting The Order of the Agents. The Order of the Agents convenes a sworn fellowship of AI agents (Codex, Claude, and other CLIs you trust) around a single question. Each agent takes a position, challenges the others, and revises in turn, until the Order issues a final decree. Every oath, critique, and revision is recorded as Markdown, so the reasoning behind the decision is auditable, shareable, and yours to keep. You can even do a grill-me style intake first to finesse the requirement before the Order convenes. That mode was inspired by Matt Pocock's grill-me skill. You can install it locally with npm: npm install agent-order Requires codex and claude CLIs (or other agent CLIs you trust) already installed and logged in. Use it like this: npx agent-order@latest "Research and draft a PRD for adding SSO to our app"

Visit product View on Hacker NewsApr 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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