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 todosgit 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/todos)<a href="https://agentmods.dev/skills/jetbrains/thinkrail/todos"><img src="https://agentmods.dev/badge/skills/jetbrains/thinkrail/todos/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/todos"><img src="https://agentmods.dev/badge/skills/jetbrains/thinkrail/todos.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 | $0.00058 | $0.03645 |
| Opus 5.5 | $0.00023 | $0.01458 |
| Sonnet 5.5 | $0.00012 | $0.00729 |
| Haiku 4.5 | $0.00006 | $0.00364 |
Grade A, and why
todos 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 5d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chat TODO plan
What it is
- A plan scoped to this chat — your tasks for the conversation. Shown to the user in the Todo panel; lives with the session (not committed to the repo).
- Group = task, item = step. One user ask = one group; its title is the outcome ("Fix login
redirect"), not the process. The steps inside are the items — each has a title, a status
(
pending→in_progress→done), and an optional note. The note is agent-facing working detail — the human's default plan view shows titles + status and keeps notes behind a disclosure — so it's where your own tracking, reminders, and the done-criterion live (e.g. "login e2e green"), never a place the user must read to follow along. A task's own status is never stored — it derives from its steps. - Loose items are the shared raw-input queue — and they sit at the END of the plan. Both sides
write there: the user adds from the UI, and you may drop an unprocessed note with
todo_add(nogroup, noafter). Loose is pre-work — raw kernels and half-formed asks, never a step you are about to execute.todo_listrenders them last, after every group, on purpose: an item added mid-task queues after your current work. So finish (or resume) the task you're on before you pick up a loose item — don't jump to a freshly-added item and abandon a step you had in progress. - Promote a loose item the moment you take it into work — with
todo_promote <loose-id>. One atomic call: the loose item disappears from the queue and a group inherits its id and title. No duplicate window, notodo_removedance, and if the session dies right after, the plan still honestly shows the user's ask as its own group. Thentodo_add group:"<same title>"the first substantive step and flip itin_progress— even a one-liner like "Investigate the ask and sketch the plan" is a real step, so the user sees her task is being worked on from the first beat. Only passtitle:totodo_promotewhen the user's wording is unusable as a group title; otherwise keep it verbatim. Size doesn't matter: a pending loose item is already in the plan, so even a 1–2-step one becomes a small group. Don't rewrite or drop a loose item for any other reason, and never author a group viatodo_add group:+todo_removefor a user-origin loose item — that's the old two-step dancetodo_promotereplaces. - It is shared and live: you maintain it, and the user edits it while you work — adding tasks,
removing ones they've dropped. The stored list is the source of truth; what you remember is only
a snapshot. Re-read it (
todo_list) to stay in sync, don't trust your memory of it. - It is the user's status window — how they follow what's happening at a glance, without reading the chat. Short, concrete step titles; statuses always current.
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.
- 5d ago Changed · +26 lines 81b0d033534c
- 12d ago Changed · +5 lines · -1 tokens per session 01b7e203ae6c
- 21d ago Changed · +25 lines 1aa2e60d5782
- 1mo ago Changed · +3 lines · -68 tokens per session d6906949a32e
- 1mo ago Changed · +12 lines b69939fbceb4
- 1mo ago First seen · 123 lines · 127 tokens per session scan A 65de35fef2a2
todos is a skill published in the GitHub repository JetBrains/thinkrail (892 stars, last pushed today), licensed Apache-2.0. It adds 58 tokens to every session and 3,645 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.
Other skills, from other repositories
workflow-helper
Guidance for creating, managing and executing workflow automations that chain multiple tools and agents together. Use when the user asks about building, listing, running or composing workflows and automated task pipelines.
composio-delivery
Delivers the finalized analyst brief to Slack, Notion, Google Sheets, and Gmail via authenticated Composio tools. Use whenever a completed, grounded brief must be distributed and logged.
computer-use
Read and drive native desktop apps through the accessibility layer — list apps, snapshot a window as a numbered element tree, then click/type/scroll/drag by element index or screen coordinates. Use for a desktop app, not a web page. macOS + Windows; off unless enabled in Settings.
meetings
Context for the Meetings app's data — where a meeting's notes, diagram, transcript-derived tasks and calendar cache live, what the lifecycle states mean, how the app's agents are driven. Load when asked about a meeting's notes or action items, or when touching the meetings app data dir.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.
subscription-setup
A setup workflow for creating recurring medical-learning subscriptions after a doctor’s profile has been registered.