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 importing-a-codebasegit 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/importing-a-codebase)<a href="https://agentmods.dev/skills/jetbrains/thinkrail/importing-a-codebase"><img src="https://agentmods.dev/badge/skills/jetbrains/thinkrail/importing-a-codebase/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/importing-a-codebase"><img src="https://agentmods.dev/badge/skills/jetbrains/thinkrail/importing-a-codebase.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 | $0.00036 | $0.01647 |
| Opus 5.5 | $0.00014 | $0.00659 |
| Sonnet 5.5 | $0.00007 | $0.00329 |
| Haiku 4.5 | $0.00004 | $0.00165 |
Grade A, and why
importing-a-codebase 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 3d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Importing a codebase
The workspace holds real code but no specs. Reverse-engineer the spec graph the project should have had. When the repo already carries real spec-like documents, build the graph around them, not parallel to them. Do as much as possible yourself, from the files; ask the user only where the code genuinely can't tell you and the answer changes a spec.
Hold the writing-specs bar. Read that concept skill before drafting — everything in this flow is inferred rather than confirmed, so its honesty rules (draft until the user reviews, unconfirmed marked inline) bind hardest here.
1. Read first, ask last
Survey before you ask a single question. Read, in roughly this order:
- Agent files (mine these first — they state intent + conventions directly):
AGENTS.md,CLAUDE.md,.cursor/rules/*,.cursorrules,.github/copilot-instructions.md,GEMINI.md,.windsurfrules. - Docs:
README,docs/,CONTRIBUTING, ADRs. - Manifests & layout:
package.json/pyproject.toml/go.mod/Cargo.toml, workspace globs,tree-style structure, entry points, build/test scripts. - Code: entry points and the top of each candidate module — enough to see responsibilities and the dependency edges between them.
While you read, collect adoption candidates: durable, declarative documents that state the world as it is — architecture/design docs, ADRs / decision records, domain glossaries, protocol/contract docs. Never candidates (input only): READMEs, CONTRIBUTING, changelogs, roadmaps, TODOs, implementation plans (finished or planned), generated API docs.
Confirm with the spec tools (spec_grep / spec_graph) that there's no graph yet. If specs already
exist, stop and hand back to the setting-up-a-project dispatcher — this flow is for un-specced repos.
2. Build a working model
From what you read, form a working model of what the project is and how it's shaped — held in the conversation, not written to a file (this flow declares no working files):
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.
- 3d ago Changed · +10 lines b773b64c58e2
- 13d ago Changed 2b52730d241c
- 17d ago Changed · +1 lines 95b33fb65eea
- 1mo ago Changed · +1 lines · -33 tokens per session 7b0476d6906f
- 1mo ago First seen · 104 lines · 69 tokens per session scan A b28cc6d74fea
importing-a-codebase is a skill published in the GitHub repository JetBrains/thinkrail (520 stars, last pushed today), licensed Apache-2.0. It adds 36 tokens to every session and 1,647 once invoked, about $0.0001 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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