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.
git clone --depth 1 https://github.com/evangelosmeklis/thufirWrote 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/commands/evangelosmeklis/thufir/analyze-prometheus)<a href="https://agentmods.dev/commands/evangelosmeklis/thufir/analyze-prometheus"><img src="https://agentmods.dev/badge/commands/evangelosmeklis/thufir/analyze-prometheus/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/commands/evangelosmeklis/thufir/analyze-prometheus"><img src="https://agentmods.dev/badge/commands/evangelosmeklis/thufir/analyze-prometheus.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00015 | $0.01552 |
| Opus 5 | $0.00008 | $0.00776 |
| Sonnet 5 | $0.00003 | $0.00310 |
| Haiku 4.5 | $0.00002 | $0.00155 |
Grade A, and why
analyze-prometheus 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 8d 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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Prometheus Alert Command
Purpose
Investigate a Prometheus alert by fetching alert details, querying related metrics, searching the codebase for relevant code, analyzing git history, and generating a root cause analysis report.
Instructions for Claude
When this command is invoked, perform comprehensive root cause analysis for a Prometheus alert:
Step 1: Gather Alert Information
If user provided alert name:
- Use prometheus MCP tool to fetch alert details
- Extract alert expression, labels, severity, and active time
If user provided alert JSON:
- Parse the alert JSON
- Extract key fields: alertname, expr, labels, annotations, activeAt
If no argument provided:
- Use AskUserQuestion to ask:
- "What is the alert name?"
- Or "Please paste the alert JSON"
- Fetch or parse accordingly
Extract from alert:
- Alert name
- PromQL expression that triggered
- Threshold value
- Time alert started firing
- Affected labels (service, instance, job, etc.)
- Alert severity
- Alert annotations (description, summary)
Step 2: Query Prometheus Metrics
Use prometheus MCP tools to query related metrics:
-
Execute alert expression to see current value:
- Query the alert's PromQL expression
- Check how far above threshold the metric is
-
Query metric over time to see pattern:
- Use query_range for last 2-6 hours
- Identify: sudden spike, gradual increase, or other pattern
-
Query breakdown metrics:
- Break down by labels (endpoint, instance, status code)
- Identify which specific component is affected
-
Query correlated metrics:
- Error rate metrics
- Latency metrics (p95, p99)
- Resource usage (CPU, memory)
- Request rate metrics
- Dependency health metrics
-
Analyze metric patterns:
- When did anomaly start?
- Sudden or gradual?
- Correlated with other metric changes?
Step 3: Search Codebase
Based on alert and metrics, search code for relevant files:
- From alert labels, identify:
- Service name
- Endpoint or feature affected
- Component mentioned in annotations
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.
- 8d ago First seen · 228 lines · 15 tokens per session scan A 5e506e4d12ad
analyze-prometheus is a command published in the GitHub repository evangelosmeklis/thufir (7 stars, last pushed 8mo ago), licensed MIT. It adds 15 tokens to every session and 1,552 once invoked, about $0.0001 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 2026-08-31.
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