Agent skill · data analytics · jeremylongshore
mistral-performance-tuning
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".
Why this skill is useful
Provides specific caching strategies and model selection patterns for optimizing Mistral AI performance that the AI wouldn't reliably generate on its own.
What it needs
Requires mistral account access. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/mistral-performance-tuning--4d0407