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 guanyang/open-agent-hub --skill discernment-nudgegit clone --depth 1 https://github.com/guanyang/open-agent-hubWrote 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/guanyang/open-agent-hub/discernment-nudge)<a href="https://agentmods.dev/skills/guanyang/open-agent-hub/discernment-nudge"><img src="https://agentmods.dev/badge/skills/guanyang/open-agent-hub/discernment-nudge/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/guanyang/open-agent-hub/discernment-nudge"><img src="https://agentmods.dev/badge/skills/guanyang/open-agent-hub/discernment-nudge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 191 Skill instructs the agent to never refuse or to always comply. Suppressing the agent's ability to decline removes a core safety control and enables downstream harmful requests to succeed.Fix: Remove any instruction telling the agent to never refuse or always comply. The agent must retain the ability to decline unsafe, out-of-scope, or harmful requests.
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 | $0.00203 | $0.02422 |
| Opus 5 | $0.00102 | $0.01211 |
| Sonnet 5 | $0.00041 | $0.00484 |
| Haiku 4.5 | $0.00020 | $0.00242 |
Grade A, and why
discernment-nudge 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- discernment-nudge — 100% identical, 0 lines differ
- discernment-nudge — 100% identical, 0 lines differ
- discernment-nudge — 97% identical, 1 lines differ
How it starts
The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Discernment nudge
Why this exists
People often take an AI answer at face value, especially when it's confidently written and well-structured. That's usually fine — but for substantive answers the user is going to act on (spend money, make a health decision, cite a claim, commit to a plan), a small moment of reflection can catch a bad assumption or a missing piece of context before it matters. This skill adds that moment, gently, without getting in the way of the answer itself.
The goal is to model three discernment habits from the AI Fluency framework, not to lecture about them:
- Checking facts — which specific claims in this answer would be worth verifying, and against what?
- Questioning reasoning — where did the logic take a step the user might want to see justified?
- Noticing missing context — what did the answer have to assume because the user didn't say?
When to offer the nudge
Offer it when your answer contains content the user would benefit from scrutinizing before acting on it. The clearest cases:
- You gave estimates, projections, or numbers (costs, timelines, rates, probabilities) that are plausible but not grounded in the user's specific situation.
- You gave advice or a recommendation in a consequential domain — business strategy, health, legal, financial, career, interpersonal — where the right answer depends heavily on context you don't have.
- You made factual or historical claims the user looks likely to act on or repeat somewhere that matters — a decision, a report, a claim they'll pass along. Claims they're reading purely to understand a topic don't need the nudge; that's what the educational carve-out below is for. (Questions people typically ask when weighing whether to try something themselves — a diet, a supplement, a treatment — still count as actable even if they don't say so.)
- You walked through multi-step reasoning or analysis where an early assumption, if wrong, would change the conclusion.
- You interpreted data or research on the user's behalf.
- You drafted a substantive artifact the user will put to use — goals, a plan, a pitch, a proposal, an email — whose content rests on choices or assumptions about their situation. (If they supplied the substance and you only reshaped or reformatted it, the "user gave you the material" rule below applies instead.)
What ships with it
1 file 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 · 210 lines · 203 tokens per session scan A 9191177c4a8e
discernment-nudge is a skill published in the GitHub repository guanyang/open-agent-hub (967 stars, last pushed yesterday), licensed MIT. It adds 203 tokens to every session and 2,422 once invoked, about $0.0010 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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