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 fmind/dot --skill colabgit clone --depth 1 https://github.com/fmind/dotWrote 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/fmind/dot/colab)<a href="https://agentmods.dev/skills/fmind/dot/colab"><img src="https://agentmods.dev/badge/skills/fmind/dot/colab/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/fmind/dot/colab"><img src="https://agentmods.dev/badge/skills/fmind/dot/colab.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00017 | $0.01116 |
| Opus 5.5 | $0.00007 | $0.00446 |
| Sonnet 5.5 | $0.00003 | $0.00223 |
| Haiku 4.5 | $0.00002 | $0.00112 |
Grade A, and why
colab 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 14d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Colab CLI
Use colab to inspect existing sessions or run work on an accelerator the workstation lacks. The official Colab skill documents every command; this skill owns provider selection, session hygiene, and the spend boundary.
Authentication
Pass the global --auth adc or --auth oauth2 before every subcommand: CLI 0.7.4 defaults to OAuth, so do not rely on implicit defaults or upstream main. ADC reuses credentials from gcloud; for user ADC, run dot login colab, which requests the configured ADC grant only when it lacks the Colab scopes and verifies session access (--dry-run previews the native commands; the authentication guide owns its scopes and semantics). Session state lives under ~/.config/colab-cli/. Examples assume ADC; substitute oauth2 for an existing OAuth profile.
Inspect without allocating
Check colab version and installed help, then use colab --auth adc sessions and colab --auth adc status; they synchronize session metadata without allocating or stopping a VM. Check authentication diagnostics before interpreting an empty listing as success.
Use colab --auth adc usage for compute-unit rate and balance; if it is unavailable, report a missing capability instead of allocating a VM. colab pay opens a purchase page and is not a balance query. Session inspection does not require new, run, exec, or stop.
Run accelerator work
Follow this workflow only when remote execution is in scope; establish the authorized accelerator, duration, and budget before allocation.
- Prefer ephemeral runs for self-persisting scripts:
colab runrents a VM, runs the script, and attempts to release it. Save needed artifacts to an authorized durable destination before the script exits; local VM files disappear on release. Use an explicitly managed session when artifacts must be downloaded afterwards. A shebang#!/usr/bin/env -S colab --auth adc run --gpu T4supports single-file execution per python-script.
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.
- 14d ago Changed c9134c55aae9
- 15d ago Changed · -5 lines · -2 tokens per session 9cc51b71a089
- 17d ago Changed · +8 lines e70128f684cf
- 23d ago Changed · -1 lines c95919150f8b
- 24d ago Changed 62e580347b4e
- 26d ago Changed e5cc65d82fb3
- 29d ago Changed 53403cac31a4
- 1mo ago Changed · +10 lines · -23 tokens per session ca3ed47e9812
- 1mo ago Changed · +1 lines f4c261fa1098
- 1mo ago First seen · 44 lines · 42 tokens per session scan A fa8f51bcc383
colab is a skill published in the GitHub repository fmind/dot (12 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 1,116 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 2026-09-03.
Other skills, from other repositories
agent-evaluation
Evaluate stochastic LLM/RAG/model/retrieval/tool agents in trials. Compare baseline/candidate on development/sealed holdouts with calibrated deterministic/model/trace graders; measure reliability, variance, leakage, safety, and cost.
prompt-design
Design production LLM or agent prompt stacks: instructions, tool contracts, examples, outputs, and runtime context. Use for precedence, conflicts, dynamic or untrusted context; prove behavior with agent-evaluation.
dotfiles-bootstrap
Bootstrap a workstation with the dotfiles framework. Takes a GitHub user / owner+repo / explicit clone URL and runs dot init (which shells out to chezmoi) with the right safety prompts. Honors the active agent profile (ask / plan / apply / audit) so it defaults to dry-run in safer modes and full apply in apply.
vibe
Delegate a coding task to a cheap AI model (Mistral Vibe by default, but any provider Vibe knows about — DeepSeek, Gemini Flash, etc.) and supervise the result via git diff. Claude orchestrates, the cheap model codes. Claude consumes 500-1500 tokens per delegation regardless of how many file reads the delegate does…
prompt-engineering
Draft, review, or improve a prompt for an LLM or agent against a research-backed rubric — explicit instructions, instruction/data separation, output contracts, reasoning scaffolds, grounding, verification, and evals. Use when writing a prompt, auditing one that misbehaves, or explaining a prompting principle.
go-stack
Build Go projects, libraries, CLIs, TUIs, web apps, or ADK agents with the standard package layout and pinned tooling.