Claude-skill-registry-data mimir-prometheus-troubleshoot

Help craft efficient Mimir/Prometheus queries, troubleshoot metric issues, avoid high-cardinality problems, and recommend best practices for aggregation, recording rules, and performance.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry-data

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry-data "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/mimir-prometheus-troubleshoot" ~/.claude/skills/majiayu000-claude-skill-registry-data-mimir-prometheus-troubleshoot && rm -rf "$T"

manifest: data/mimir-prometheus-troubleshoot/SKILL.md

source content

Mimir + Prometheus Troubleshooting & Query-Builder Skill

What this Skill does

Use this skill whenever a user needs help with:

PromQL queries
Metric debugging
Missing data / gaps
Cardinality optimization
Aggregation strategy
Recording rules

Best Practices

Low-cardinality label selection

Use labels such as:

```
job
```
,
```
instance
```
,
```
service
```
,
```
cluster
```
,
```
namespace
```
,
```
env
```

Avoid:

```
user_id
```
,
```
session_id
```
,
```
request_id
```
, raw UUIDs

Always narrow time ranges

Prefer

"5m"

"15m"

"1h"

Use correct aggregations

```
rate()
```
for counters
```
sum by (...)
```
for grouping
```
histogram_quantile()
```
for latency

Suggest recording rules if query is heavy

Example Queries

User Request	PromQL
"Error rate for payments in prod"	`sum by (job) (rate(http_requests_total{job="payments", env="prod", status=~"5.."}[5m]))`
"Latency p95 for frontend"	`histogram_quantile(0.95, sum by (le) (rate(http_request_duration_seconds_bucket{app="frontend"}[5m])))`

When to Suggest Loki or Tempo

For:

request IDs
root-cause event-level debugging
full request paths

→ Recommend Tempo + Loki correlations.

Limitations

Skill does not run PromQL