mistral-observability
Provides instrumentation patterns for tracking Mistral AI costs, token usage, and latency.
Install
mkdir -p .claude/skills/mistral-observability && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4750" && unzip -o skill.zip -d .claude/skills/mistral-observability && rm skill.zipInstalls to .claude/skills/mistral-observability
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Set up comprehensive observability for Mistral AI with metrics, traces,Key capabilities
- →Instrument Mistral API client calls
- →Emit Prometheus metrics for requests and tokens
- →Configure alerting rules for latency and cost
- →Generate Grafana dashboard panels
- →Implement structured logging for observability
How it works
The skill provides an instrumented wrapper for the Mistral client that calculates duration, token usage, and costs, then pushes these metrics to a backend. It also includes YAML configurations for Prometheus alerting and Grafana dashboard definitions.
Inputs & outputs
When to use mistral-observability
- →Track token consumption
- →Monitor API latency
- →Calculate integration costs
- →Setup dashboard metrics
About this skill
Mistral Content-Free Observability
Overview
Observe demand, reliability, latency, usage, and state convergence without credentials or content. Keep app telemetry authoritative; evaluate provider Public Preview observability separately.
Prerequisites
- Defined SLOs, error taxonomy, data classification, and telemetry retention.
- A correlation design using opaque application identifiers.
- Metrics for queue, transport, stream, usage, tools, batch, workflows, and spend.
Current Contract
Mistral documents Public Preview observability endpoints plus API/admin usage evidence. Preview adoption is optional and requires schema, retention, and availability evaluation.
Authentication
Never emit authorization, keys, prompts, responses, embeddings, files, tool data, or signed URLs. Restrict admin/preview observability access separately.
Instructions
- Define golden signals and state outcomes before fields.
- Emit endpoint class, opaque model, status/error, attempts, latency segments, usage, and terminal state.
- Propagate trace context without provider/customer content as identifiers.
- Dashboard SLOs, throttling, unfinished streams, duplicates, backlog, and spend anomalies.
- Alert on user impact and actionable budget/state thresholds with owners/runbooks.
- Evaluate beta observability for data, retention, access, export, failure, and 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
Provider observability, trace export, identifier retention, sampling changes, or content-bearing fields require approval. Application-owned content-free telemetry remains the safe default.
Error Handling
- Logging prompts turns telemetry into an uncontrolled content store.
2xxcan hide invalid output or unfinished state.- High-cardinality customer text exposes data and destabilizes monitoring.
Output
Return SLOs, telemetry schema and exclusions, dashboards, alerts and owners, preview decision, retention, evidence, and rollback. Identify every signal that remains unavailable.
Examples
- Measure queue, first event, terminal latency, usage, and validation without response text.
- Alert when workflow state stops converging despite a healthy stream.
Validation
Inspect telemetry for all paths, scan for secrets and content, and test alert and runbook routing. Confirm cardinality and retention remain within approved bounds.
Resources
- Current first-party evidence map — 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.
When not to use it
- →Logging message content containing PII
- →Labeling metrics by high-cardinality data like request IDs
Prerequisites
Limitations
- →Streaming responses require manual aggregation for accurate token counts
- →Pricing table in the wrapper must be updated manually when rates change
How it compares
Unlike manual logging, this approach provides pre-configured alerting rules and dashboard panels specifically mapped to Mistral API metrics.
Compared to similar skills
mistral-observability side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| mistral-observability (this skill) | 1 | 2mo | No flags | Intermediate |
| analyzing-logs | 14 | 2mo | Review | Beginner |
| obsidian-observability | 5 | 2mo | Review | Intermediate |
| instruments-profiling | 3 | 4mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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