Official agent skill

Discernment Nudge

by anthropics in anthropics/skills

Appends two or three targeted follow-up questions after a substantive answer, pointing at facts to verify, reasoning to probe, or context that was assumed.

OfficialApache-2.0Auto-check passedAgent Workflows

Install Discernment Nudge

skills CLI
$ npx skills add anthropics/skills --skill discernment-nudge -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install anthropics/skills discernment-nudge --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/anthropics/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/discernment-nudge .claude/skills/discernment-nudge && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
discernment-nudge
GitHub stars
180k
Used in
3 other repos
Token cost
~2.6k tokens
SKILL.md length
1,468 words
Files
2
Skills in repo
16
Repo updated
First seen
Licence
Apache-2.0

At a glance

Appends two or three targeted follow-up questions after a substantive answer, pointing at facts to verify, reasoning to probe, or context that was assumed.

  • Giving advice or a recommendation in a consequential domain
  • SKILL.md covers Why this exists, When to offer the nudge, When not to and Writing the prompts, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Drafting a plan, pitch, or proposal that rests on assumed context

What it does

This skill triggers once per conversation after an answer the user is likely to act on, such as advice in a consequential domain, estimates and projections not grounded in their specific situation, a multi-step analysis where one wrong assumption would change the conclusion, or a drafted artifact like a plan or proposal resting on assumptions about their situation.

It models three discernment habits rather than lecturing about them: checking which specific claims are worth verifying, questioning where the reasoning took an unjustified step, and noticing what the answer had to assume because the user didn't say it. It skips the nudge for trivial lookups, purely educational explanations, reformatting content the user already supplied, code the user will run themselves, creative writing or casual chat, and any request that already asked for a double-check or citation.

When your agent uses it

  • Giving advice or a recommendation in a consequential domain
  • Drafting a plan, pitch, or proposal that rests on assumed context
  • Interpreting data or walking through multi-step reasoning on the user's behalf

Example prompts

  • “What's a reasonable monthly budget for this renovation?”
  • “Draft a go-to-market plan for this product launch.”
  • “Walk me through whether this statistical result is significant.”

What it can do on your machine

Read from SKILL.md and the folder at commit dbd4588. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Discernment Nudge loads about 2.6k tokens when it runs. Until then it costs about 250 tokens; SKILL.md has 1,468 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~250
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from anthropics/skills at commit dbd4588, republished under its Apache-2.0 licence (© anthropics). 1,468 words, ~2,623 tokens.

Download SKILL.mdSave it as .claude/skills/discernment-nudge/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
discernment-nudge
description
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.
license
Complete terms in LICENSE.txt

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.)
Show full SKILL.md (398 more words)Show less

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.

© anthropics, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/discernment-nudge of anthropics/skills.

  • SKILL.md
  • LICENSE.txt

Open the folder on GitHubat commit dbd4588

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in anthropics/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Discernment Nudge next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Discernment Nudge compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Discernment Nudge this skillanthropics/skills180k3 repos~2.6kAutomated safety check: PassApache-2.0
Show Me Your Work Decision Logcursor/plugins11k8 repos~1.6kAutomated safety check: PassNone
Darwin Skill Optimizeralchaincyf/darwin-skill6.2k1 repos~4.7kAutomated safety check: PassMIT
Loop Constraints Enforcercobusgreyling/loop-engineering11k1 repos~475Automated safety check: NotesMIT
Ask User QuestionMemTensor/MemOS12k—~1kAutomated safety check: PassApache-2.0
PUA High-Agency Governancetanweai/pua20k—~502Automated safety check: PassMIT

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Categories

Questions about Discernment Nudge

What does Discernment Nudge do?

Appends two or three targeted follow-up questions after a substantive answer, pointing at facts to verify, reasoning to probe, or context that was assumed. This skill triggers once per conversation after an answer the user is likely to act on, such as advice in a consequential domain, estimates and projections not grounded in their specific situation, a multi-step analysis where one wrong assumption would change the conclusion, or a drafted artifact like a plan or proposal resting on assumptions about their situation.

When should I use Discernment Nudge?

Discernment Nudge fits situations like: giving advice or a recommendation in a consequential domain; drafting a plan, pitch, or proposal that rests on assumed context; interpreting data or walking through multi-step reasoning on the user's behalf.

How do I install Discernment Nudge in Claude Code?

Run `npx skills add anthropics/skills --skill discernment-nudge -a claude-code`. Or copy the skill folder (skills/discernment-nudge in anthropics/skills) into .claude/skills/discernment-nudge in your project. Claude Code loads it when a task matches its description.

How do I install Discernment Nudge in Codex?

Run `npx skills add anthropics/skills --skill discernment-nudge -a codex`. Or copy the skill folder (skills/discernment-nudge in anthropics/skills) into .agents/skills/discernment-nudge in your project. Codex loads it when a task matches its description.

Can I use Discernment Nudge in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add anthropics/skills --skill discernment-nudge -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/discernment-nudge, .gemini/skills/discernment-nudge, .github/skills/discernment-nudge and .opencode/skills/discernment-nudge in your project.

What does Discernment Nudge need to run?

SKILL.md names no scripts, command-line tools or credentials: Discernment Nudge is instructions for the agent only.

Does Discernment Nudge access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Discernment Nudge safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Discernment Nudge use?

Discernment Nudge is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Discernment Nudge use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Discernment Nudge?

Skills that share tags, products or a category with Discernment Nudge: Show Me Your Work Decision Log (cursor/plugins, 11k stars), Darwin Skill Optimizer (alchaincyf/darwin-skill, 6.2k stars), Loop Constraints Enforcer (cobusgreyling/loop-engineering, 11k stars) and Ask User Question (MemTensor/MemOS, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Discernment Nudge?

anthropics (a GitHub organization, an official publisher) maintains it in anthropics/skills, which has 180,214 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 9, 2026.

Source: anthropics/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.