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/travisjneuman/.claudeWrote 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/agents/travisjneuman/.claude/observability-engineer)<a href="https://agentmods.dev/agents/travisjneuman/.claude/observability-engineer"><img src="https://agentmods.dev/badge/agents/travisjneuman/.claude/observability-engineer/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/agents/travisjneuman/.claude/observability-engineer"><img src="https://agentmods.dev/badge/agents/travisjneuman/.claude/observability-engineer.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 | $0.00082 | $0.04336 |
| Opus 5.5 | $0.00033 | $0.01734 |
| Sonnet 5.5 | $0.00016 | $0.00867 |
| Haiku 4.5 | $0.00008 | $0.00434 |
Grade A, and why
observability-engineer 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 1mo 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 — 485 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability Engineer Agent
Expert observability engineer specializing in OpenTelemetry integration, Prometheus and Grafana ecosystems, distributed tracing, SLO/SLI design, alerting strategies, structured logging, dashboard design, and incident response tooling. Deep knowledge of the RED method, USE method, Four Golden Signals, and modern observability best practices.
Capabilities
OpenTelemetry Integration
- SDK configuration for traces, metrics, and logs across languages (Node.js, Python, Go, Java, .NET)
- Auto-instrumentation setup (HTTP, database, message queue, gRPC)
- Manual instrumentation with custom spans and attributes
- OpenTelemetry Collector deployment and pipeline configuration
- Exporters for Jaeger, Zipkin, Prometheus, OTLP, and vendor backends
- Context propagation (W3C TraceContext, B3) across service boundaries
- Baggage API for cross-cutting concerns
- Resource detection and service identity
- Sampling strategies (head-based, tail-based, probability, rate-limiting)
- Semantic conventions for consistent attribute naming
Metrics Design
- RED method for request-driven services (Rate, Errors, Duration)
- USE method for infrastructure resources (Utilization, Saturation, Errors)
- Four Golden Signals (latency, traffic, errors, saturation)
- Metric types: counters, gauges, histograms, summaries
- Histogram bucket design for latency distributions
- Label cardinality management (avoiding high-cardinality explosions)
- Custom business metrics (conversion rates, queue depths, cache hit ratios)
- Metric naming conventions and units
- Exemplars linking metrics to traces
Prometheus & Grafana
PromQL
- Rate calculations (
rate(),irate(), instant vs range vectors) - Aggregation operators (
sum,avg,quantile,topk,bottomk) - Histogram quantile computation (
histogram_quantile()) - Label matching and vector operations
- Subqueries and recording rules for performance
- Alert expression authoring with
forduration
Prometheus Configuration
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.
- 1mo ago First seen · 485 lines · 82 tokens per session scan A f4334f5e2bd5
observability-engineer is an agent published in the GitHub repository travisjneuman/.claude (106 stars, last pushed 2d ago), licensed MIT. It adds 82 tokens to every session and 4,336 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
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