@JetBrains

Creator · JetBrains

Last updated · Aug 24, 2026

asking-user-questions

REVIEW · 70Registry indexed

Use when composing an ask_user_question round inside a workflow, or when a workflow skill names it at a question step. Shared norms for the tool — not a workflow, nothing to execute.

OpenAgentSkill Trust Score
70/100

Sandbox only

Quality63/100
Audit80/100
Stars38
Verified installs0

Install targets

Codex install prompt

Install the "asking-user-questions" agent skill from https://github.com/JetBrains/thinkrail/tree/main/packages/pi-thinkrail-workflow/skills/asking-user-questions. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use when composing an ask_user_question round inside a workflow, or when a workflow skill names it at a question step. Shared norms for the tool — not a workflow, nothing to execute. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"jetbrains-asking-user-questions","task":"Install asking-user-questions","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.

Supply asset profile

Coding and developer agents

Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.

Browse track

Scenario

Coding agents

I need a coding agent that can understand a repository, edit code, and review pull requests.

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add JetBrains/thinkrail --skill asking-user-questions

Maintenance

fresh

Pushed today

Risk

Needs review

Financial research output is not financial advice; require human review before any live investment decision

GitHub quality

38

63/100 Quality · 78/100 Trust

Coverage tags

CodingCoding agentsautomationagent-skill

Review notes

Financial research output is not financial advice; require human review before any live investment decision · Low GitHub adoption signal

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
63

Useful candidate, but compare it with alternatives before adopting.

Trust

Sandbox only
70

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

Audit

Needs review
80

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Run only in a sandbox and compare close alternatives before using it for real work.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

38 GitHub stars

Repo activity

38 stars, 8 forks

Maintenance

Pushed today

License

Apache-2.0

Install

npx skills add JetBrains/thinkrail --skill asking-user-questions

Install safety

standard package or runtime install path

Permission surface

no high-risk permission surface in public metadata

Agent outcomes

No agent outcome data yet

Docs

Usable metadata, review docs

Risk summary

Review before production

  • Financial research output is not financial advice; require human review before any live investment decision.
  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 38 GitHub stars

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

View technical data+

Suited tasks

  • Browser automation workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Navigate pages

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add JetBrains/thinkrail --skill asking-user-questions
Policy
review
Human review
yes

Trust and risk

Trust
70/100
Audit
80/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add JetBrains/thinkrail --skill asking-user-questions

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • Low GitHub adoption signal
  • No OpenAgentSkill engagement data yet
  • Financial research output is not financial advice; require human review before any live investment decision

Agent safety v2

60/100 · Review before install

Reviewed with permission notesreview

Usable candidate, but the agent should surface permission and audit notes before installation.

Require human approval before installing into a real workspace.

Resolve via API

medium

Browser automation

Skill may drive a browser or interact with web pages.

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

  • Financial research output is not financial advice; require human review before any live investment decision

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use asking-user-questions in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20asking-user-questions%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jetbrains-asking-user-questions/install
Install command: npx skills add JetBrains/thinkrail --skill asking-user-questions
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use asking-user-questions for this task. Review https://www.openagentskill.com/api/skills/jetbrains-asking-user-questions/install, then install with: npx skills add JetBrains/thinkrail --skill asking-user-questions

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

62/100

Browser automation

Platforms

Claude Code

Audit report

Needs review · 80/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for Browser automation

Prototype with this skill first; keep a fallback candidate ready.

62
Readiness
Prototype
Stage

Role in stack

Fallback candidate

Primary fit

Browser automation

Trust label

Prototype first

Install path

Command ready

Use when

  • Browser automation workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 63/100 quality profile

review first

  • Low GitHub adoption signal
  • No OpenAgentSkill engagement data yet

Implementation path

  1. 1Install it in a sandbox agent and run one Browser automation task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Sandbox only

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

70
OpenAgentSkill Trust Score

GitHub adoption

CHECK

38 GitHub stars

Stars/forks activity

CHECK

38 stars, 8 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

PASS

Apache-2.0

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • Financial research output is not financial advice; require human review before any live investment decision.
  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 38 GitHub stars
  • Stars/forks activity: 38 stars, 8 forks; issue activity unavailable in current metadata
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Run only in a sandbox and compare close alternatives before using it for real work.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

63
GitHub stars
38
Freshness
Today
Install ready
Yes
License
Apache-2.0
Review before install: Low GitHub adoption signal

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

--- name: asking-user-questions description: "Use when composing an ask_user_question round inside a workflow, or when a workflow skill names it at a question step. Shared norms for the tool — not a workflow, nothing to execute." ---

# Asking User Questions

The workflow family's shared norms for `ask_user_question`: how to compose rounds, shape options, and degrade when answers don't come. Process skills name this concept at the steps that ask; *when* to ask — and where the answers get recorded — stays with the referencing skill.

## Rounds, not chat turns

- One call = one **round**: up to 4 questions, 2–4 options each. Group everything the current step needs into a single round — never chain a second call straight after for a trivial follow-up. - **The call ends your turn.** The questionnaire is shown and your run stops; the answers arrive as the next user message (a structured "User has answered your questions:" message). Don't keep working on the blocked step after calling, and don't assume an answer until it arrives — whether that is seconds later or days later. - If the user replies with a free-form message instead of answering the card, that reply **supersedes** the round — treat it as their answer, and re-ask only what is still genuinely undecided. - Resolve the round, act on what you learned, and open a new round only when the answers raised a genuinely new question.

## Options

- Recommended option first, label suffixed "(Recommended)", plus a one-line `recommendedReason` saying why you recommend it over the alternatives (shown inline under the option as a `Why:` line). - Every option: a concise label (1–5 words, ≤ 60 chars) + a description carrying the trade-off or consequence of choosing it. Tailor options to the work at hand — never generic placeholders. - Options must be **decidable by the asked user**: frame them as observable behavior or outcomes ("collapsing a project stays collapsed after a rename"), never as implementation mechanics ("semantic guard", "activation ref"). If candidate options differ only internally — identical observable behavior — don't ask: decide yourself and record the reasoning in the workflow's artifact. - Never author your own "Other", free-text, or escape options — the tool adds a free-text row to every question and an always-available Skip, and reserved labels are rejected. This holds under `multiSelect` too: the free-text row stays and is *additive* — a typed answer arrives alongside the checked options, it does not replace them. - `multiSelect: true` when several answers are valid at once (feature checklists); single-select when confirming something or choosing one path. - `options[].preview` (markdown) when a concrete artifact — code, a config, a mockup — is clearer shown than described. Single-select only. - `header` is a short chip, ≤ 16 characters.

## Confirming an inference

When you have inferred something and need a yes/adjust rather than an open answer: the inferred statement *is* the question text, with "Looks right" as the first option (description: "accurate as written") and a genuine rejection option second (e.g. "Off base — ask me directly"). Edits arrive through the tool's automatic free-text row — do not author an edit option. Read the response as:

- **"Looks right"** → the inference holds; continue unchanged. - **Free-text tweak** (one fact changes) → update that field only; don't re-derive anything else. - **Substantial rewrite** → re-derive every inference that came from that statement before continuing. - **Rejection** → discard the inference entirely and ask an open-ended question instead.

## Degradation

- Skipped, declined, or unanswered questions are not blockers: proceed on best-guess assumptions, explicitly recorded as unconfirmed in the workflow's artifact (the referencing skill says where). - If the host reports no interactive UI (`ask_user_question` returns "not available"), state your assumptions the same way instead of blocking. - "I don't know / help me understand" is a mis-framing signal, not a missing-knowledge one: re-explain from user-visible behavior in plain language, then re-ask with behavior-framed options — don't repeat the same technical options with more detail.

Technical details

Version
1.0.0
License
Apache-2.0
Last updated
Aug 24, 2026
Published
Aug 24, 2026

Decision snapshot

Fallback candidate

62
Ready
Prototype
Stage

recent repository activity

Audit

Install review

Install and adoption review

80
Needs review
Security
87/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for asking-user-questions, ready for a manual X post.

Curator note
asking-user-questions: Use when composing an ask_user_question round inside a workflow, or when a workflow skill nam...

38 stars

https://www.openagentskill.com/skills/jetbrains-asking-user-questions?ref=x
Open X draft
Optional reply with install command
Listing + install path for asking-user-questions:
https://www.openagentskill.com/skills/jetbrains-asking-user-questions?ref=x

Install: npx skills add JetBrains/thinkrail --skill asking-user-questions

Listing source

Registry indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Creator
JetBrains
Indexed by
OpenAgentSkill community index

Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.

Claim this skill

Owner claim

Claim this skill listing

This Registry indexed listing is attributed to JetBrains but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

Creator backlink kit

Add the evidence badges to your README

Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/jetbrains-asking-user-questions?metric=listed&label=Listed)](https://www.openagentskill.com/skills/jetbrains-asking-user-questions)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/jetbrains-asking-user-questions?metric=trust&label=Trust)](https://www.openagentskill.com/skills/jetbrains-asking-user-questions)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/jetbrains-asking-user-questions?metric=audit&label=Audit)](https://www.openagentskill.com/skills/jetbrains-asking-user-questions/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/jetbrains-asking-user-questions?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/jetbrains-asking-user-questions)

Author

J

JetBrains

@jetbrains

Platform fit

Health signals

GitHub stars
38
Quality score
35/100
Last GitHub push
Aug 24, 2026
Framework hints
Unknown
OpenAgentSkill views
0
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Sandbox only

70
  • GitHub adoption38 GitHub starsCHECK
  • Stars/forks activity38 stars, 8 forks; issue activity unavailable in current metadataCHECK
  • Recent maintenancePushed todayPASS
  • License clarityApache-2.0PASS
  • README/SKILL.md completenessPublic metadata needs stronger README/SKILL.md contextINFO
  • Dependency/runtime riskno major dependency risk hints in public metadataPASS