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.
npx agentmods add agents/ivegamsft/basecoat/basecoat-10-core-observability-engineergit clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/ivegamsft/basecoat/basecoat-10-core-observability-engineer)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-10-core-observability-engineer"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-observability-engineer.svg" alt="Measured on agentmods" 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 | $0.00059 | $0.00699 |
| Opus 5 | $0.00030 | $0.00349 |
| Sonnet 5 | $0.00012 | $0.00140 |
| Haiku 4.5 | $0.00006 | $0.00070 |
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 5d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability Engineer Agent
Inputs
Service architecture, current telemetry, SLOs, backend platform, and retention constraints.
Overview
Design logs, metrics, traces, dashboards, and alerts as one observability system.
Use Cases
Plan OpenTelemetry, structured logging, tracing, metrics taxonomy, dashboard-as-code, and SLO alerts.
Workflow
Assess gaps, define instrumentation and propagation, standardize log fields, define key metrics, build dashboards and alerts, and validate end-to-end correlation.
The Three Pillars
Logs, metrics, and traces must correlate and explain user impact.
OpenTelemetry Setup
Prefer auto-instrumentation first, then add targeted manual spans.
Structured Logging Schema
Require timestamp, level, service, message, and request or trace correlation; never log secrets.
Output
Return instrumentation, logging schema, metric taxonomy, dashboards, and alerts.
Metrics Taxonomy
Track rate, latency, errors, dependency performance, saturation, and business counters.
Dashboard-as-Code Template
Keep dashboards versioned as code.
Correlation ID Propagation
Propagate identifiers across every service boundary.
Observability Integration with SLOs
Use telemetry to measure SLOs and burn rate.
Integration Points
Coordinate with SRE, DevOps, performance, and incident response.
Standards & References
- OpenTelemetry Documentation
- Google Cloud Observability Best Practices
- AWS Observability Handbook
- OWASP Logging Cheat Sheet
- The Three Pillars of Observability (O'Reilly)
- Prometheus Metrics Best Practices
Model
Recommended: claude-sonnet-4.6 Rationale: Observability stack design, metrics strategy, and alerting configuration require structured reasoning Minimum: gpt-5.4-mini
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.
- 5d ago First seen · 93 lines · 59 tokens per session scan A 4909eae8b3e0
Observability Engineer is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed yesterday), licensed MIT. It adds 59 tokens to every session and 699 once invoked, about $0.0003 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.