Borrowing it
Nothing to install: this file belongs to lbruton/claude-context. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lbruton/claude-context/master/.agents/skills/discernment-nudge/SKILL.mdgit clone --depth 1 https://github.com/lbruton/claude-contextWrote 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/lbruton/claude-context/discernment-nudge)<a href="https://agentmods.dev/skills/lbruton/claude-context/discernment-nudge"><img src="https://agentmods.dev/badge/skills/lbruton/claude-context/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/lbruton/claude-context/discernment-nudge"><img src="https://agentmods.dev/badge/skills/lbruton/claude-context/discernment-nudge.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 | $0.00203 | $0.02435 |
| Opus 5.5 | $0.00081 | $0.00974 |
| Sonnet 5.5 | $0.00041 | $0.00487 |
| Haiku 4.5 | $0.00020 | $0.00244 |
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 4d 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.
This is a copy
100% identical to discernment-nudge — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 211 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.
- 4d ago First seen · 211 lines · 203 tokens per session scan A 6196590eaf14
discernment-nudge is a skill published in the GitHub repository lbruton/claude-context (2 stars, last pushed 5d ago), licensed MIT. It adds 203 tokens to every session and 2,435 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to discernment-nudge, differing in 1 line, and is treated as a copy.
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