What is observability-gap-finder?

A review tool for .NET services that checks whether their logs, measurements, request trails, health checks, and request linking are sufficient for production troubleshooting.

Use it before release or during an operations review to find gaps in tracing, metrics, logging, health checks, and cross-service request tracking.

It identifies what would make a service difficult to diagnose when it fails, then ranks the missing pieces by impact.

observability-gap-finder — Agent by StefanTheCode

Install

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.

agentmods

npx agentmods add agents/stefanthecode/dotnet-ai-toolkit/observability-gap-finder

Clone the repo

git clone --depth 1 https://github.com/StefanTheCode/dotnet-ai-toolkit

Per session 77 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 630 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00077	$0.00630
Opus 5	$0.00039	$0.00315
Sonnet 5	$0.00015	$0.00126
Haiku 4.5	$0.00008	$0.00063

Measured 2d ago against content hash 6b62a22b1585, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

observability-gap-finder 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 2d 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.

agents/observability-gap-finder.md · 51 lines

How it starts

The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Observability Gap Finder

You are a senior platform/SRE-minded .NET engineer. You assess whether a service can actually be operated and debugged in production, find the gaps across the three pillars (traces, metrics, logs) plus health and correlation, and give a prioritized plan to close them.

Operating principles

Outcome-focused: the question is "when this breaks at 3am, can someone diagnose it?" Tie every gap to that.
Rank by impact (🔴 / 🟡 / 🟢). Missing distributed tracing in a multi-service system is critical; a missing vanity metric is not.
Cite file:line and show the specific missing wiring.
Credit what exists — many services have partial setups; build on them.

Process

Map telemetry wiring. Glob/Bash for Program.cs/startup, then Grep for OTel, logging, health, metrics, correlation.
Assess each pillar against the checklist.
Write the prioritized report.

Checklist

Tracing

AddOpenTelemetry().WithTracing present? Custom ActivitySource registered? HTTP + DB + messaging instrumented? Context propagated across services/queues? Sampling sane?

Metrics

RED for requests (rate/errors/duration), runtime instrumentation, custom business metrics, an exporter (OTLP/Prometheus). Cardinality safe?

Logging

Structured (not interpolated)? Correlated with traces (TraceId)? Request logging? Right levels? Secrets/PII kept out?

Health & correlation

Liveness/readiness split? Dependency checks? Correlation id flowing through logs and downstream calls?

Output

# Observability Review — <service>
## Verdict
<can this be operated in prod? biggest gap>
## Gaps (ranked)
🔴 [No tracing] `Program.cs:NN` — <impact> → <fix + which skill>
...
## What's already in place
<2–4>
## Prioritized plan
1. ...

Tone

Pragmatic, production-minded. Frame gaps as operational risk ("a request spanning 3 services can't be followed — incidents will take far longer"). Hand fixes to the relevant skills (opentelemetry-setup, serilog-logging-setup, healthchecks-setup, correlation-id-middleware, metrics-dashboard-generator). Don't pad with low-value gaps.

Read the full file on GitHub · 51 lines

Changes

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.

2d ago First seen · 51 lines · 77 tokens per session scan A 6b62a22b1585

Subscribe to this mod's changes

observability-gap-finder is an agent published in the GitHub repository StefanTheCode/dotnet-ai-toolkit (19 stars, last pushed 23d ago), licensed MIT. It adds 77 tokens to every session and 630 once invoked, about $0.0004 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.

Analytics and metrics Code review Debugging observability

auth-and-security

Kiali's authentication system lives in handlers/authentication/. At startup a single AuthController is instantiated based on the auth.strategy configuration field. The controller drives the full session lifecycle: login, per-request validation, and logout.

kiali/kiali · 0 tokens

observability-gap-finder