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- Systematic Bug Diagnosis — Root Cause, Not Guesswork
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Systematic Bug Diagnosis — Root Cause, Not Guesswork
Finds why an app bug is happening using a step-by-step method (reproduce, isolate, test ideas, 5 Whys), then gives a small correct fix and a regression test — without guessing first.
$12
Systematic Bug Diagnosis — Root Cause, Not Guesswork
Example session with this skill installed
ends UTC (Z); API TZ=America/Chicago
Symptom: 8h shift stored as 13.0h; naive unit tests pass
clock_out:
started = started.astimezone().replace(tzinfo=None) # local wall
ended = ended.replace(tzinfo=None) # UTC wall kept
hours = (ended - started) / 3600
log: 08:00Z–16:00Z → hours_computed = 13.0
- Read your context and instructions
- Compiled the systematic bug diagnosis
Class: environment | Stage: FIX | Confidence: CONFIRMED | Reproducible: yes
H3 CONFIRMED: asymmetric normalize
start → Chicago wall (08:00Z → 03:00)
end → UTC wall kept (16:00)
→ 13.0h lie; true UTC delta = 8.0
Root cause: duration mixed local-converted start with UTC-stripped end
Fix: both sides astimezone(UTC) before subtract
Regression: clock_out(08:00Z, 16:00Z) asserts 8.0
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About this skill
The problem
- Guessing at fixes wastes hours and often papers over the real cause.
- Symptoms (wrong hours, weird totals, flaky tests) get “fixed” in the UI while the root bug stays.
- Teams need a shared, evidence-first debugging process — not another “try this” chat.
What it does
- Runs a scientific debugging workflow: reproduce → isolate → hypothesize → test → root cause → fix → verify.
- Classifies the bug (logic, state, type, race, environment, integration, data, regression) and picks a matching strategy.
- Tracks hypotheses with evidence for/against and a next experiment for each.
- Builds a 5-Whys chain that separates the symptom from the root cause.
- Proposes a minimal fix plus a regression test, with honest confidence: CONFIRMED / PROBABLE / SPECULATIVE.
Frameworks & tools
- Language-agnostic method; works on any stack when you provide code, errors, and context.
- Uses failing tests, logs, and diffs you already have — no special SaaS debugger required.
- SKILL.md workflow for Claude Code, Cursor, Codex CLI, OpenClaw, and similar agents.
Why this beats prompting it yourself
- Anti-guessing rule: no “the answer” fix until the cause is CONFIRMED or PROBABLE.
- If it cannot reproduce, you get a reproduction plan — not a random patch.
- Hypothesis table and 5 Whys keep the investigation honest and reviewable.
- Bug-class strategies (e.g. race vs environment) change how it debugs.
- Regression test is part of the deliverable so the bug stays fixed.
Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Testing & Debugging.
• Cannot execute your app unless you run the suggested experiments
• Heisenbugs may need several rounds
• Not for CI/CD pipeline, CrashLoop, or Helm/deploy failures
• Does not apply fixes unless you explicitly ask
• Confidence stays SPECULATIVE when evidence is thin — by design
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before buying a skill.
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Permissions
This skill only analyzes the code and symptoms you provide. It drafts a diagnosis, a suggested fix, and a test for you to apply. It does not change your repo, run installs, call the network, or read secret env values on its own. Grant Read only; leave Shell, Write, Browser, Network, and Env off unless you later ask the agent to apply a fix.