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Log Driven Diagnosis

skill-yeaight7-agent-powerups-log-driven-diagnosis · by yeaight7

Use when debugging complex runtime failures, distributed systems, or issues where a local debugger cannot be attached.

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Install

$ agentstack add skill-yeaight7-agent-powerups-log-driven-diagnosis

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Purpose

When you cannot step through code, logs are your only visibility. Be methodical about extracting signal from noise — never dump whole log files into context.

When to Use

  • Runtime failures where a debugger cannot be attached
  • Distributed systems where the failure spans services
  • Issues reproducible only from log evidence

Inputs

  • Log files or a log explorer covering the incident window
  • The incident timestamp and, if available, a request/trace ID

Workflow

  1. Time-bound the search. Never dump the whole log file — always grep for timestamps around the reported incident, or use tail:

``bash tail -n 200 app.log # most recent context grep -n "2026-06-06T14:0" app.log # window around the incident awk '/14:02:00/,/14:05:00/' app.log # bounded slice between timestamps ``

  1. Identify the request ID. If the system uses distributed tracing or request IDs, find the ID associated with the error, then search the log corpus for only that ID to trace the complete lifecycle of the failed request:

``bash grep -n "ERROR" app.log | head -5 # find the failing entry and its ID grep -n "" app.log # full lifecycle of that request # structured (JSON) logs: jq -c 'select(.request_id == "")' app.log.json ``

  1. Look for preceding warnings. The ERROR log is usually just the final crash. The actual root cause is often a WARNING or unexpected INFO log that occurred milliseconds earlier (e.g., a connection retry failing, or an empty array being returned):

``bash grep -n -B 20 "" app.log | grep -inE "warn|retry|timeout|empty" ``

  1. Add missing logs. If the logs do not provide enough visibility, your first action must be to add temporary logging to the application, reproduce the bug, and gather the new signals. Do not guess blindly if the logs are insufficient.

Output

  • The traced lifecycle of the failing request
  • The suspected root-cause log line(s), including any preceding warnings
  • Any temporary logging added (flagged for later removal)

Verification

  • [ ] Every search was time-bounded or ID-bounded — no full-file dumps in context
  • [ ] Full request lifecycle traced when an ID exists
  • [ ] Lines preceding the error inspected, not just the error line itself
  • [ ] Temporary logging added (and flagged for removal) where visibility was missing

Failure Modes

  • Error-line tunnel vision — reading only the final ERROR while the cause sits in an earlier WARNING.
  • Context flooding — dumping megabytes of log into the conversation instead of bounded slices.
  • Blind guessing — iterating on fixes when the honest move is adding logging and reproducing once more.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.