JetBrains/thinkrail is a desktop and mobile client that embeds the pi coding agent inside an interface for editing code and managing development workspaces. Developers use it to work on Git repositories through separate worktrees, a Monaco editor, terminals, Git views, specifications, and concurrent agent sessions. The catalogue entries are skills and instructions for its agent-based development workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add JetBrains/thinkrail --skill asking-user-questionsgit clone --depth 1 https://github.com/JetBrains/thinkrailWrote 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/jetbrains/thinkrail/asking-user-questions)<a href="https://agentmods.dev/skills/jetbrains/thinkrail/asking-user-questions"><img src="https://agentmods.dev/badge/skills/jetbrains/thinkrail/asking-user-questions/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/jetbrains/thinkrail/asking-user-questions"><img src="https://agentmods.dev/badge/skills/jetbrains/thinkrail/asking-user-questions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.01646 |
| Opus 5.5 | $0.00017 | $0.00658 |
| Sonnet 5.5 | $0.00009 | $0.00329 |
| Haiku 4.5 | $0.00004 | $0.00165 |
Grade A, and why
asking-user-questions 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 16d 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.
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Asking User Questions
The workflow family's shared norms for ask_user_question: how to interview in rounds, what makes a
question worth asking, how to shape options, and how to 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.
Interview in rounds until nothing is assumed
- Map the subject as a design tree: every decision branches into the decisions that hang off it. The frontier is every open decision whose prerequisites are already settled — the questions you can ask now without guessing at answers you haven't heard.
- One call = one round = the whole frontier, with no cap on its size. A question whose answer depends on another question still open belongs to a later round, never the same one. Never split independent questions across back-to-back calls.
- The call blocks the current run. Answers arrive as this tool's result. Don't keep working on the blocked step or assume an answer until it arrives — seconds or days later. A composer message sent while the card is open supersedes the round: the card closes unanswered and the message arrives as the next user message, so treat it as the user's reply and re-ask only what still matters.
- After each round, recompute the frontier: answers settle branches, unblock their dependents, and prune branches that no longer apply. Ask the next round; never pre-write later rounds.
- The interview is done when the frontier is empty — every branch visited, nothing material silently assumed. Then continue the referencing skill's next step; there is no extra "are we aligned?" round (the referencing skill's own gates still apply).
- Depth follows the work, not a quota: work with no open user decision gets zero rounds; a decision-heavy change may take several.
Facts are yours, decisions are theirs
- Never ask the user for a fact the workspace, the specs, or the web can answer. Look it up. When a lookup is slow, start it in the background (a sub-agent, when the host offers one) and ask the rest of the frontier meanwhile — only the questions downstream of that lookup wait.
- Every decision — scope, observable behavior, trade-offs the user will live with — goes to the user. Picking an option yourself and moving on is answering your own question, not inferring.
- If candidate options differ only internally — identical observable behavior — it is not a user decision: decide yourself and record the reasoning in the workflow's artifact.
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.
- 16d ago Changed · +1 lines 978a3ec34fb8
- 20d ago Changed · +45 lines 639d59161f36
- 1mo ago Changed 8d475a40d636
- 1mo ago First seen · 67 lines · 43 tokens per session scan A 8725dace0254
asking-user-questions is a skill published in the GitHub repository JetBrains/thinkrail (1,024 stars, last pushed today), licensed Apache-2.0. It adds 43 tokens to every session and 1,646 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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