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 skills add manu14357/zskills --skill appinsights-instrumentationgit clone --depth 1 https://github.com/manu14357/zskillsWrote 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/skills/manu14357/zskills/appinsights-instrumentation)<a href="https://agentmods.dev/skills/manu14357/zskills/appinsights-instrumentation"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/appinsights-instrumentation/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/skills/manu14357/zskills/appinsights-instrumentation"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/appinsights-instrumentation.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 | $0.00059 | $0.02613 |
| Opus 5 | $0.00030 | $0.01307 |
| Sonnet 5 | $0.00012 | $0.00523 |
| Haiku 4.5 | $0.00006 | $0.00261 |
Grade A, and why
appinsights-instrumentation 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 12d 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 — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Insights Instrumentation
Implement high-value telemetry with minimal noise and strong trace correlation for production troubleshooting and performance optimization.
Use This Skill When
- The user needs monitoring for apps running in Azure
- The user needs distributed tracing across microservices
- The user needs alerting, dashboards, or root cause analysis visibility
- The user wants to migrate from custom logging to structured telemetry
Context: Observability Maturity
Immature: No monitoring, errors discovered by users
Developing: Logs and metrics collected, no correlation or alerting
Managed: Traces linked across services, alerts firing, dashboards useful → Target
Optimized: Real-time anomaly detection, SLO tracking, automated runbooks
Required Inputs
- Application stack: Node.js, Python, .NET, Java, Go, etc.
- Deployment target: App Service, Container Apps, AKS, Function, VM
- Critical user journeys: Signup → Confirmation, Payment → Order, Search → Results
- SLO targets: Availability (%), latency (ms p99), error budget
- Compliance needs: PII filtering, data residency, retention policy
- Existing telemetry: Custom logging, APM, or greenfield?
Decision Tree
Is this a new application or existing?
├─ New → Start with OpenTelemetry SDK from day one
└─ Existing → Assess current logging, add instrumentation incrementally
Which language?
├─ .NET → Application Insights .NET SDK (auto-instrumentation or manual)
├─ Node.js → OpenTelemetry + @opentelemetry/sdk-node
├─ Python → OpenTelemetry + opentelemetry-sdk
├─ Java → OpenTelemetry agent (zero-code) or manual SDK
└─ Other → OpenTelemetry language-specific SDK
How many services (microservices)?
├─ Single → Simple Application Insights setup, no special tracing
├─ 2-5 services → OpenTelemetry with correlation IDs across calls
└─ 5+ services → Distributed trace propagation (W3C Trace Context)
What's the expected volume?
├─ < 1M events/day → 100% sampling acceptable
├─ 1-10M → 10-25% sampling with head-based decisions
└─ > 10M → 1-5% sampling with tail-based (post-request) sampling decisions
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.
- 12d ago First seen · 327 lines · 59 tokens per session scan A 7847d4e05d5e
appinsights-instrumentation is a skill published in the GitHub repository manu14357/zskills (16 stars, last pushed 1mo ago), licensed MIT. It adds 59 tokens to every session and 2,613 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 2026-08-30.
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