repo-native-alignment: Skill for Claude Code

.agents/skills/dissent/SKILL.md

dissent is a skill for Claude Code, Codex from open-horizon-labs/repo-native-alignment. It costs 43 tokens per session (2,857 once invoked), scanned A, original, MIT.

A decision-checking method that deliberately looks for reasons an important choice could be wrong.

In plain words
What is it for?
Use it to challenge architecture choices, major hiring decisions, public interfaces, or other high-stakes plans after options have been explored.
Why use it?
It helps uncover hidden assumptions and risks before a hard-to-reverse decision is made, especially when agreement comes too quickly.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is open-horizon-labs/repo-native-alignment's own configuration. It tells Claude Code and Codex how to work on repo-native-alignment itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything repo-native-alignment configures →

Reuse

Borrowing it

Nothing to install: this file belongs to open-horizon-labs/repo-native-alignment. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/open-horizon-labs/repo-native-alignment/main/.agents/skills/dissent/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/open-horizon-labs/repo-native-alignment

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for dissent

README.md
[![agentmods](https://agentmods.dev/badge/skills/open-horizon-labs/repo-native-alignment/dissent/github.svg)](https://agentmods.dev/skills/open-horizon-labs/repo-native-alignment/dissent)
Your own site
<a href="https://agentmods.dev/skills/open-horizon-labs/repo-native-alignment/dissent"><img src="https://agentmods.dev/badge/skills/open-horizon-labs/repo-native-alignment/dissent/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for dissent

Your own site · 80×15
<a href="https://agentmods.dev/skills/open-horizon-labs/repo-native-alignment/dissent"><img src="https://agentmods.dev/badge/skills/open-horizon-labs/repo-native-alignment/dissent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,857 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.02857
Opus 5 $0.00022 $0.01429
Sonnet 5 $0.00009 $0.00571
Haiku 4.5 $0.00004 $0.00286

Measured 11d ago against content hash de684338ef63, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

dissent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/dissent/SKILL.md · 373 lines

How it starts

The opening of the file, as written. The whole thing — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/dissent

Structured disagreement that strengthens decisions. The insight: find flaws before the one-way door closes.

Dissent is not attack. It's the practice of actively seeking reasons you might be wrong. The devil's advocate is a role, not a personality.

When to Use

Invoke /dissent when:

  • About to lock in a one-way door - architecture choices, major hires, public API contracts, anything hard to reverse
  • Confidence is high but stakes are higher - feeling certain is when you need dissent most
  • Team is converging too quickly - unanimous agreement without debate is a warning sign
  • You're defending a position - advocacy mode is the enemy of truth-seeking
  • The path forward seems obvious - obvious paths have hidden assumptions

Do not use when: Gathering initial options, brainstorming, or exploring. Dissent is for stress-testing decisions, not generating them.

The Dissent Process

Step 1: Steel-Man the Current Approach

Before attacking, fully articulate the position you're challenging:

"The current approach is [approach]. The reasoning is [reasoning]. The expected outcome is [outcome]. This is the strongest version of this position."

If you can't state the position charitably, you don't understand it well enough to challenge it.

Step 2: Seek Contrary Evidence

Actively search for information that contradicts the current approach:

  • What data would prove this approach wrong?
  • Who disagrees with this? What's their strongest argument?
  • What similar approaches have failed elsewhere? Why?
  • What are we ignoring because it's inconvenient?

"If this approach were wrong, what would we expect to see? Are we seeing any of that?"

Step 3: Pre-Mortem

Imagine it's six months from now and this decision failed. Work backward:

"This failed because [reason]. The warning signs we ignored were [signs]. The assumption that broke was [assumption]."

Generate at least three plausible failure scenarios:

  1. Technical failure - It doesn't work as expected
  2. Adoption failure - It works but nobody uses it / changes nothing
  3. Opportunity cost - It works but we should have done something else

Read the full file on GitHub · 373 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 373 lines · 43 tokens per session scan A de684338ef63

Subscribe to this mod's changes

dissent is a skill published in the GitHub repository open-horizon-labs/repo-native-alignment (5 stars, last pushed 2d ago), licensed MIT. It adds 43 tokens to every session and 2,857 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens