Mistral Performance TuningSAFE
Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.
Overview
Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.
4f83675ca38aOBSERVED · 2026-10-09Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| claude-code | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: mistral-performance-tuning description: >- Analyze and improve Mistral latency and throughput from measured queue, transport, streaming, retrieval, and token evidence. Use when optimizing a slow integration. Trigger with "speed up Mistral", "reduce Mistral latency", or "tune Mistral throughput". allowed-tools: Read,Glob,Grep,Write,Edit argument-hint: "<operation> <latency-slo> <load-profile>" version: 1.14.0 license: MIT author: Jeremy Longshore <[email protected]> tags: [saas, mistral, performance] model: inherit effort: high compatibility: "Designed for Claude Code; live or external Mistral actions require network access and explicit approval" --- # Mistral Performance Tuning ## Overview Optimize the measured bottleneck rather than changing models or caching content by instinct. Separate queue, connection, first-event, generation, tool, and application time. ## Prerequisites - A representative synthetic benchmark and explicit quality/safety acceptance. - Content-free latency, token, queue, retry, and error instrumentation. - Current workspace limits plus fixed model and request parameters. ## Current Contract Chat, streaming, embeddings, batch, FIM, OCR, and audio differ in latency and batching. Compare only compatible operations and current account access. ## Authentication Metrics may include timing, counts, endpoint class, and opaque model ID, but never prompts, outputs, files, credentials, or headers. ## Instructions 1. Define user SLOs and a stable workload covering typical, tail, and cancellation cases. 2. Measure queue, connection, first-event, terminal, parsing, retrieval, tool, and storage durations. 3. Identify the dominant segment and test one reversible hypothesis at a time. 4. Tune bounds, connection reuse, admission, streaming UX, retrieval size, or app concurrency. 5. Compare latency, errors, tokens, quality, safety, and spend using the same workload. 6. Canary the change, monitor regression, and retain prior configuration for rollback. ## Tool Discipline Use Read, Glob, and Grep to inspect code, locks, configuration, tests, and evidence. Use Write and Edit only for approved repository changes. Invocation alone does not authorize network calls, paid usage, uploads, stateful resources, admin mutations, deployments, or deletion. ## Approval Boundaries Live benchmarks, model changes, caching, concurrency, endpoint changes, or relaxed gates require approval. Performance never overrides data policy. ## Error Handling - Fast first event can hide worse terminal latency. - Caching user content can violate tenancy and deletion. - Concurrency can move latency into shared provider queues. ## Output Return workload hash, before and after segments, confidence, quality, safety and spend deltas, chosen change, canary, and rollback. State whether the SLO actually improved. ## Examples - Reduce retrieval context only after evaluation preserves quality. - Reuse connections while retaining cancellation and end-to-end deadlines. ## Validation Repeat warm and cold trials, vary concurrency, test cancellation and outage, and reject any weakened correctness or isolation. Preserve the exact workload for comparison. ## Resources - [Current first-party evidence map](references/official-docs.md) — recheck dated sources before relying on mutable endpoints, models, limits, prices, preview status, or retention. - Record live account observations as environment-specific evidence, not universal Mistral guarantees.
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | PASS |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
4f83675ca38afull audit observations/trust-audit/skill/jeremylongshore__mistral-performance-tuning.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-09 | 4f83675ca38a | SAFE | B | 89 | first audit |
Questions
What does the Mistral Performance Tuning skill do?
Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.
Is Mistral Performance Tuning safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Mistral Performance Tuning access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does Mistral Performance Tuning work with?
Its documentation mentions claude-code. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (4f83675ca38a), read on 2026-10-09. The repository is watched, and a new audit runs when it changes — this is the first audit.