Mistral Vibe is a command-line coding assistant that lets users converse with a codebase and use natural language to explore or change projects. Developers use it for file editing, code searching, version-control tasks, and shell commands through Mistral’s models. Its catalogue add-ons provide skills and instructions for customizing the assistant’s workflows.
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 mistralai/mistral-vibe --skill instrument-feature-analyticsgit clone --depth 1 https://github.com/mistralai/mistral-vibeWrote 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/mistralai/mistral-vibe/instrument-feature-analytics)<a href="https://agentmods.dev/skills/mistralai/mistral-vibe/instrument-feature-analytics"><img src="https://agentmods.dev/badge/skills/mistralai/mistral-vibe/instrument-feature-analytics/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/mistralai/mistral-vibe/instrument-feature-analytics"><img src="https://agentmods.dev/badge/skills/mistralai/mistral-vibe/instrument-feature-analytics.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 | $0.00065 | $0.01961 |
| Opus 5 | $0.00032 | $0.00981 |
| Sonnet 5 | $0.00013 | $0.00392 |
| Haiku 4.5 | $0.00006 | $0.00196 |
Grade A, and why
instrument-feature-analytics 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 11d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instrument a Feature for Analytics
Answer the tracking questions a software engineer would normally take to a data scientist or data engineer. When they bring one here, answer it the way a data person would, and proactively cover what they didn't think to ask.
The software engineer already knows how to emit an event technically. The hard part — and the reason they'd normally ask a data person — is deciding which events and properties matter for the metrics, and making sure they fit what already exists so the data is actually usable. Events land in mistral-data.lake_eu.logs_events.
Use the steps below as the full picture to reason from, not a rigid script: if the software engineer arrives with a specific question, answer that first, then pull in whatever other steps are relevant. If they arrive with "I'm adding feature X, help me track it", walk it in order.
Process
1. Understand the feature and the metrics behind it
Ask the software engineer a few questions before anything else:
- What is the feature? (a new onboarding step, a way to discover a feature, a status/lifecycle change, an action users repeat…)
- What will product and data want to measure about it? Adoption? A funnel and where people drop off? How often a status changes? Whether a surface gets noticed?
Then translate those questions into the events and properties that would answer them. Common patterns:
- Funnel (e.g. onboarding): one event per step (
started,step_completed,completed), with a property identifying the step and its order, plus a shared id to stitch the steps of one user together. - Discoverability: distinguish seen from acted on — an impression event when the thing is shown and an interaction event when the user engages, so you can compute a noticed→used rate.
- Status / lifecycle updates: one event per transition carrying
from_statusandto_status(and what triggered it), so changes can be reconstructed over time. - Repeated action / engagement: one event per occurrence with enough context (what, where) to segment it.
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.
- 11d ago First seen · 146 lines · 65 tokens per session scan A 831d34fa43bf
instrument-feature-analytics is a skill published in the GitHub repository mistralai/mistral-vibe (4,933 stars, last pushed today), licensed Apache-2.0. It adds 65 tokens to every session and 1,961 once invoked, about $0.0003 per session on Opus 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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