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log-analyzer

Skill0 starsChanged 3 months agoMITView on GitHub

Groups a log file's errors by pattern and ranks offenders — normalizes volatile tokens (timestamps, UUIDs, IPs, paths, numbers) so "connection refused to db-01" and "db-99" count as one problem seen N times. Handles both level-tagged logs (ERROR/WARN/INFO) and exception-style stack logs (DaemonError:, Traceback). Offline, stdlib-only, deterministic.

13 other projects use a version of this.

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Projects with it also use test-generator and security-audit.

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SKILL.md

Log Analyzer

A 50,000-line log is not a report. This is.

Commands

python3 scripts/log_analyzer.py app.log
python3 scripts/log_analyzer.py app.log --level ERROR --top 10
python3 scripts/log_analyzer.py app.log --budget-errors 0     # CI: fail on any
python3 scripts/log_analyzer.py app.log --max-patterns 5      # CI: error diversity cap
python3 scripts/log_analyzer.py daemon.log --json

What it reports

  • Total lines, error count, warning count, distinct pattern count
  • Ranked patterns with occurrence counts and one verbatim example each

Volatile tokens (timestamps, UUIDs, hex, IPs, host:port, paths, numbers) are normalized to <tokens> so the same failure from different hosts/times groups into one pattern. Exception-style lines (FooError:, Traceback, FAILED) count as errors even without an ERROR level token.

Exit codes

0 = no errors / within budget, 1 = errors found or over budget, 2 = input error.

Pairs with

systematic-debugging (what to do with the top pattern once you have it), diff-review (the diff-level gate upstream of the runtime log).

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