Works with the AI tools you already use
Observability Coverage Auditor
by heyhridyansh
Audit production observability, identify telemetry blind spots, and generate remediation plans for services.
$11.99
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See it in action
You say
Audit the observability for our new payment processing service. I've attached the Go source code, the Prometheus alert rules, and a sample JSON log from a failed transaction.
Your agent does
Audit Verdict: Material observability blind spots. Score: 62/100. Critical Gap: Payment completion events are logged but lack correlation IDs for the downstream ledger service. Remediation: Add trace propagation to the worker pool and create an alert for DLQ depth > 5.
What you get
About this skill
The problem
Engineering teams often realize their observability is insufficient only after a major production incident occurs. Finding blind spots in logs, metrics, and traces across distributed services is manual, error-prone, and reactive.
What it does
- Audits critical production paths to ensure failures are detectable and diagnosable.
- Inventories telemetry across logs, metrics, traces, and correlation IDs to find coverage gaps.
- Evaluates queue depth, job execution, and external API integration visibility.
- Reviews alert quality and SLO signals to ensure on-call engineers receive actionable data.
- Generates a prioritized remediation plan and a 100-point observability coverage score.
Why this beats prompting it yourself
Writing a prompt to check "if my logging is good" misses the complex interplay between async jobs, distributed tracing, and business logic outcomes. This skill uses a structured 24-step rubric to verify correlation across the entire stack, ensuring you don't just have more data, but better answers during an incident.
Use cases
- Pre-release production readiness reviews for new microservices.
- Post-mortem analysis to identify why an incident wasn't detected or was hard to debug.
- Auditing external integration points and webhook pipelines for silent failures.
- Validating that PII is not leaking into logs or high-cardinality metric labels.
Known limitations
Cannot access live production systems or change alert rules. If runtime telemetry is not provided, results are limited to static design reviews.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Creator
26 skills on Agensi
I create practical, AI-ready skills for Cursor, Claude Code, Codex CLI, Replit, and other agents that support the SKILL.md format. My skills focus on specific business and workflow problems, including ecommerce creative audits, design quality checks, prompt and skill validation, content systems, and process automation. Each skill is built with clear inputs, structured outputs, defined permissions, safeguards, and real-world usability.
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