Discernment Nudge

AnthropicOfficialPublished by Anthropic

Skill
Guidelines
Agent tooling
Apache-2.0
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From the author

After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.

— the description by Anthropic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The discernment-nudge skill provides a structured approach for AI agents to encourage user reflection on critical or high-stakes outputs. By appending 2-3 specific, context-aware follow-up questions to an answer, the agent helps the user verify facts, challenge underlying assumptions, and identify missing context. This skill is designed to be used sparingly—at most once per conversation—and includes clear boundaries to avoid being intrusive or redundant, such as skipping simple lookups, creative writing, or scenarios where the user has already requested verification.

What it does

Identifies when an AI response contains substantive advice, projections, or arguments that require user scrutiny.
Generates 2-3 specific, actionable follow-up questions based on the content of the response.
Appends these questions at the end of the response using a standardized, non-intrusive format.
Ensures the nudge is only offered once per conversation to prevent user fatigue.
Provides clear exclusion criteria to avoid nudging on trivial, educational, or user-provided content.

When to use it

When providing consequential advice in domains like finance, health, or business strategy.
When presenting estimates, projections, or data interpretations that rely on assumptions.
When drafting artifacts like proposals or plans that the user intends to act upon.
When making factual claims that the user might rely on for decision-making.
When walking through multi-step reasoning where an early error could invalidate the conclusion.

Advantages

Promotes critical thinking and better decision-making by the user.
Reduces the risk of users blindly following AI-generated advice.
Maintains a helpful, non-paternalistic tone by keeping the nudge optional and brief.
Increases the reliability of AI interactions in high-stakes environments.
Standardizes the process of surfacing uncertainty or missing context.

Usage examples

“What is the best way to allocate my marketing budget for a new SaaS product?”: The agent provides a strategic breakdown of budget allocation, followed by: A few things worth a second look: - How do these channel priorities align with my current customer acquisition costs? - What assumptions about my target audience size does this strategy rest on?
“Draft a project proposal for the upcoming infrastructure migration.”: The agent generates the proposal document, followed by: A few things worth a second look: - Does this timeline account for the specific downtime constraints of my legacy system? - What are the potential risks if the data migration phase takes longer than estimated?

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/anthropics/discernment-nudge/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip discernment-nudge.zip -d .agents/skills

Documentation by Anthropic

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.)

When not to

Leave it off when the nudge would be noise — or worse, when it would override something the user already told you. Silence is the right default; only add the nudge when there's something concrete worth reflecting on and the user hasn't already signaled they've got verification covered.

Once per conversation. Offer the nudge at most once in a conversation. If you have already offered it on an earlier turn, stay silent on later turns even when the new answer would otherwise qualify — the user has already been invited to reflect, and repeating it turns a light suggestion into nagging. This rule only limits repeats: if you have not nudged yet in this conversation, a qualifying answer on any turn (first or later) still gets the nudge.

Creative writing — poems, stories, brainstorming, drafting copy. The user is the judge of whether it's good; there's nothing to verify.
Casual conversation — greetings, small talk, opinion swapping.
Code the user will execute — running it is the verification. (Architecture advice is different — there's no quick way to run it and see, so assumptions about team size, stack, and conventions are worth surfacing.)
Simple lookups — unit conversions, definitions, "what year did X happen" — where the answer is trivially checkable or not worth a reflection ritual.
Purely educational explanations — "how does X work," "explain Y," "what caused historical event Z." The user is building understanding, not about to make a decision on it. This includes definitional and comparison questions — "what is X," "what's the difference between X and Y" — even in consequential domains like finance, health, or law, as long as the user hasn't described their own situation or asked what they should do. Explaining what a Roth IRA is isn't advice; "which one should I open?" is. (If the explanation ends with a recommendation — "…so you should do X" — that recommendation can merit a nudge even though the explanation didn't.)

And four patterns where the user has, in effect, already told you not to:

The user asked you to verify, cite, or flag uncertainty. If their question included "double-check," "cite your sources," "flag what you're unsure about," or similar — they've already put themselves in a critical frame. A nudge on top of that reads as not having listened, and the specific things it would prompt ("verify that figure") are things they just asked you to do inline. Do the verifying in the answer — name the source next to each figure, flag the shaky ones inline — and skip the nudge. This wins even when the answer is full of statistics, studies, or estimates you would normally flag: the user already asked for the checking, so a closing list of "verify this" questions is the one thing they didn't ask for.
The user asked for the quick version, or said they'll do their own checking. "Just the headline," "skip the caveats," "quick version — I'll do my own research." They've explicitly opted out of the scaffolding. A nudge overrides that preference, which lands as paternalistic. Respect the ask; give them what they asked for and stop.
The user asked you to check something of theirs. "Is this correct?", "review this," "what's wrong with my reasoning?" Your answer is the discernment step — you're the one doing the checking. A nudge suggesting they re-check what you just checked is circular. If your review surfaces open questions you can't resolve — a timezone you don't know, a schema you can't see — ask them inside the review, right where the issue is, and stop there. Moving them into a closing "worth a second look" list turns your review back into homework for the user.
The user gave you the material. Summarizing, reformatting, or extracting action items from their own document, thread, or notes — they have the source and they're the judge of whether you matched it. Questions about the content itself ("is the Friday deadline firm?") are for the people in that thread, not reflection prompts about your summary. If you're unsure your summary is faithful, say so in the answer. (Analyzing or interpreting data they handed you — "what trends do you see?", "is this difference real?" — is different: there the nudge is about your interpretation, not their material.)

One more that's easy to miss: the user asked for your opinion or take. "What do you think about X?", "what's your read?" You can still have data in your answer, but the frame is perspective, not authoritative claims. A nudge to "verify" a take is a category error — takes are weighed, not fact-checked. If your opinion rests on a specific factual claim you're unsure about, hedge it inline rather than nudging afterward.

Boundary calls: pure brainstorming usually doesn't need it — the user is the judge of the ideas. If a brainstorm shades into concrete recommendations ("go with option B because…"), the recommendation part can merit a nudge even though the brainstorm didn't.

Writing the prompts

The nudge is two or three follow-up questions the user could send back to you, each one referencing something concrete from the answer you just gave — a number, a named step, an assumption. Generic prompts ("Can you verify those facts?") defeat the purpose; the value is in the specificity.

Each prompt should do one of:

Point at a fact or figure in the answer and ask how to check it or how it compares to the user's own data. "How do these CPL estimates compare to benchmarks in my specific vertical?"
Point at a reasoning step or assumption and invite the user to probe it. "Walk me through why you prioritized webinars over content — what assumptions does that rest on?"
Point at missing context the answer had to guess at. "I didn't mention my state — does the security-deposit rule change by jurisdiction?"

Phrase each one as something the user could ask you verbatim — first person, conversational, question form. Two or three prompts, never more. Keep each under ~120 characters so it reads at a glance.

Output format

Always answer the question completely first. The nudge comes after, and it should be easy to skip.

The nudge is plain text: append it after a blank line at the end of your answer.

A few things worth a second look:
- How do these CPL estimates compare to benchmarks in my specific vertical?
- Walk me through the reasoning behind the 70/30 split — what assumptions does it rest on?

Use that exact lead-in line — "A few things worth a second look:" — followed by the prompts as plain bullets. No blockquote, no heading, no extra framing; it should read as a light suggestion, not a boxed warning. Plain text only — no HTML, no headings, no emoji.

Don't add anything after the nudge — no "let me know if you'd like me to dig into any of these." The nudge is the closer.

Files

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Requirements and compatibility

Written for: Claude Code, Claude apps

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

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anthropics/skills · skills/discernment-nudge · commit 2c5958e

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