Creator · JetBrains
Last updated · Aug 24, 2026
asking-user-questions
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.
Sandbox only
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.
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
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
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA 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.
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+
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.
Suited tasks
- Browser automation workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Navigate pages
Suited agents
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-questionsDo 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
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
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 JSON
/api/agent/resolve?task=Use%20asking-user-questions%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20asking-user-questions%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/jetbrains-asking-user-questions/install
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.
Install handoff
/api/skills/jetbrains-asking-user-questions/install
LLM text format
/api/skills/jetbrains-asking-user-questions/install?format=text
Find alternatives
/api/skills/search?q=asking-user-questions&limit=3
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-questionsRegistry 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.
Manifest
/api/registry/manifest/jetbrains-asking-user-questions
LLM text
/api/registry/manifest/jetbrains-asking-user-questions?format=text
Install alias
/api/registry/install/jetbrains-asking-user-questions
Recommend
/api/registry/recommend?task=Use%20asking-user-questions%20in%20an%20agent%20workflow&limit=3
Agent fit
Browser automation
Use-case tags
Platforms
Claude Code
Audit report
Needs review · 80/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Fallback candidate for Browser automation
Prototype with this skill first; keep a fallback candidate ready.
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
- 1Install it in a sandbox agent and run one Browser automation task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 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.
GitHub adoption
CHECK38 GitHub stars
Stars/forks activity
CHECK38 stars, 8 forks; issue activity unavailable in current metadata
Recent maintenance
PASSPushed today
License clarity
PASSApache-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.
Workflow fit
Use this skill in these scenarios
Operate web apps
Browser automation
I need my agent to control a browser, fill forms, and verify web app workflows.
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Search private knowledge
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Workflow fit
Add it to a complete workflow
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Compare before you install
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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
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 87/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- 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
Scenario-led draft for asking-user-questions, ready for a manual X post.
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
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
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- JetBrains
- Source
- JetBrains/thinkrail
- 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 skillOwner 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.
[](https://www.openagentskill.com/skills/jetbrains-asking-user-questions)
[](https://www.openagentskill.com/skills/jetbrains-asking-user-questions)
[](https://www.openagentskill.com/skills/jetbrains-asking-user-questions/audit)
[](https://www.openagentskill.com/skills/jetbrains-asking-user-questions)Author
JetBrains
@jetbrains
Tags
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
- 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
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