Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add product-on-purpose/thinking-framework-skills --skill think-authentic-dissentgit clone --depth 1 https://github.com/product-on-purpose/thinking-framework-skillsWrote 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.
[](https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-authentic-dissent)<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-authentic-dissent"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-authentic-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.
<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-authentic-dissent"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-authentic-dissent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00073 | $0.01007 |
| Opus 5 | $0.00036 | $0.00504 |
| Sonnet 5 | $0.00015 | $0.00201 |
| Haiku 4.5 | $0.00007 | $0.00101 |
Grade A, and why
think-authentic-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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Authentic Dissent
Genuine minority dissent makes a group reason better: a person who truly holds a contrary view makes the majority search more broadly and consider more options, even when the dissenter is wrong. The catch, established by the same research, is that role-played devil's advocacy does not replicate this - assigned dissent gets discounted as performance. So an AI cannot be the dissent; anything a model argues against a plan is constructed, the weaker kind. This skill therefore does not pretend to be the dissenter. It engineers the conditions for real dissent: it audits whether genuine dissent exists, surfaces who holds it, plans how to elicit and protect it, and flags constructed dissent as constructed. The output is a dissent audit and plan.
When to Use
- A group decision shows suspiciously smooth consensus and nobody is really pushing back.
- You can influence how challenge is gathered (who speaks, anonymous input, outside reviewers).
- Before a high-stakes call where you want genuine, not performed, challenge.
When NOT to Use
- As a source of dissent itself: the model's contrarian view is constructed, not authentic (use
red-team-lightfor that, which is honest about being constructed). - In a purely solo setting with no access to other people - you cannot manufacture authentic dissent.
- When genuine dissent already exists and is being heard.
- To "assign a devil's advocate" and consider the job done (the evidence says that does not deliver the benefit).
Instructions
When asked to set up or audit dissent, follow these steps:
- Audit the consensus. Is the agreement genuine, or is it smoothness from anchoring, hierarchy, or conformity? Note signs (no one names a downside, the senior view landed first, dissent would be costly).
- Locate real dissent. Identify whether anyone actually holds a minority view, and whether it is being voiced, ignored, or suppressed.
- Label what is in play. Mark any current "dissent" as authentic (a real holder) or constructed (assigned/role-played/AI). Do not let constructed dissent count as the real thing.
- Plan to elicit and protect genuine dissent. Concrete moves: anonymous pre-reads, asking the quietest person first, bringing in an outside reviewer who genuinely disagrees, separating generation from evaluation, protecting the dissenter from cost.
- For high stakes, prescribe a real dissenter. Recommend finding a person who actually holds the contrary view, rather than relying on a constructed critique.
- Emit the dissent audit and plan per
references/TEMPLATE.md.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 63 lines · 73 tokens per session scan A afe7dbf29d91
think-authentic-dissent is a skill published in the GitHub repository product-on-purpose/thinking-framework-skills (15 stars, last pushed today), licensed Apache-2.0. It adds 73 tokens to every session and 1,007 once invoked, about $0.0004 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 2026-08-30.
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