observability-first-debugging_skill

This skill guides you through observability-first debugging by instrumenting code and basing hypotheses on concrete evidence.
  • Python

247

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill ntcoding/claude-skillz --skill observability-first-debugging

  • SKILL.md8.6 KB

Overview

This skill teaches an observability-first debugging methodology that eliminates guessing and speculation. It shows how to add targeted instrumentation to gather the exact data needed to explain a failure. Emphasis is on evidence before hypothesis and observation before solution.

How this skill works

The skill walks through a reproducible protocol: reproduce the symptom, add logging/prints/assertions to reveal inputs, branches, and external responses, then run and inspect the captured output. Based on that evidence you form a testable hypothesis, run minimal experiments, and iterate until the root cause is found. It includes concrete techniques for command-line tools, code, network calls, file operations, and environment issues.

When to use it

  • Investigating crashes, non-zero exit codes, or uncaught exceptions
  • Troubleshooting failing tests or unexpected return values
  • Diagnosing API errors, 4xx/5xx responses, or malformed payloads
  • Finding missing files, wrong working directories, or path issues
  • Debugging flakey or timing-dependent behavior (race conditions)

Best practices

  • Reproduce and document the exact symptom before changing code (copy error messages and steps)
  • Add observability first — print inputs, intermediate values, return values, and branch choices
  • Make hypotheses only after you have evidence; your hypothesis must explain all observed facts
  • Change one thing at a time and verify with instrumentation to know what fixed it
  • Prefer simple, direct output (print/log/assert) over complex guessing; capture full request/response data for network issues

Example use cases

  • A unit test reports "Expected 3, got undefined": add prints inside the test and target function to see where undefined originates
  • API POST returns 400: log URL, headers, payload, status, and response body to find the malformed field
  • FileNotFoundError: print cwd, directory listing, absolute path, and existence checks to reveal path mismatch
  • Intermittent race condition: add entry/exit logs around suspected sections and timestamp outputs to observe ordering
  • Deployment environment bug: log environment vars, PATH, and tool versions to spot configuration differences

FAQ

Keep logs minimal and deterministic (timestamps, ids). When timing sensitivity is suspected, add lightweight instrumentation or use deterministic test harnesses; reproduce locally when possible.

How long should I leave instrumentation in production?

Prefer short-lived, targeted instrumentation with structured logs and sampling. If useful long-term, convert to configurable debug logging with levels rather than permanent prints.

Built by
VeilStrat
AI signals for GTM teams
© 2026 VeilStrat. All rights reserved.All systems operational