clean-log-toolkit

v0.1.0

A small, honest local toolkit for the work agents end up doing constantly: read a log someone sent you, figure out the format, find the actual problems, and produce a summary you can paste into a ticket. Built on Python 3 standard library only. No awk/sed/jq wrappers, no pip installs, no remote calls.

This skill is the third of the "clean-*" trio:

• clean-csv-toolkit — structured tabular data
• clean-text-toolkit — unstructured text
• clean-log-toolkit — semi-structured timestamped logs

What this skill does

• scripts/parse.py — parse a log file into structured rows. Auto-detects apache-common, apache-combined, nginx-access, syslog, and json-line formats by sniffing the first ~50 lines. Falls back to a generic timestamp + level + message extractor when nothing matches. Pass --regex PATTERN with named groups to define a custom format. Output as .csv, .tsv, or .jsonl.
• scripts/errors.py — aggregate the errors in a log file. Counts by level (WARN / ERROR / FATAL by default), buckets the timeline by minute / hour / day, normalizes each message into a "fingerprint" (replaces numbers, UUIDs, hex tokens, file:line pairs, and embedded timestamps with placeholders) and surfaces the top-N most frequent error groups. Writes a JSON / Markdown / CSV report or prints a one-screen summary.
• scripts/grep.py — grep, but log-aware. Combine --pattern REGEX, --not-pattern REGEX, --level LVL[,LVL2...], --since TIMESTAMP, --until TIMESTAMP, and -B / -A / -C context lines into one filter pass. Output goes to stdout or to a file. Returns exit 0 on at least one match, 1 on zero matches.
• scripts/check_deps.sh — verify python3 is available.

What this skill does not do

• It does not tail/follow live log files (yet — possible v0.2 feature if there's demand).
• It does not call any LLM, web service, or remote API.
• It does not write outside the input/output paths the caller provides.

Quick start

1. Parse an unknown log file

# Auto-detect the format
python3 scripts/parse.py app.log app.csv

# Or be explicit
python3 scripts/parse.py access.log out.jsonl --format apache-combined
python3 scripts/parse.py syslog.txt out.csv --format syslog
python3 scripts/parse.py events.log out.csv --format json-line --fields ts,level,msg

2. Custom format via named-group regex

python3 scripts/parse.py app.log structured.csv \
    --regex '^(?P\x3Cts>\S+)\s+(?P\x3Clevel>\S+)\s+(?P\x3Cmessage>.*)$'

3. Aggregate errors and produce a report

# One-screen summary
python3 scripts/errors.py app.log

# Bucket by minute, top 20 message groups
python3 scripts/errors.py app.log --bucket minute --top 20

# Only count specific levels
python3 scripts/errors.py app.log --level ERROR,FATAL

# Write a Markdown report ready to paste into a ticket
python3 scripts/errors.py app.log --output report.md

# Or a JSON report for downstream tooling
python3 scripts/errors.py app.log --output report.json --bucket hour

# Or a CSV of the timeline only
python3 scripts/errors.py app.log --output timeline.csv --bucket minute

errors.py fingerprints messages so repeated errors that only differ in numbers / UUIDs / file-line refs collapse to one group with a count. Example: 50 occurrences of Connection timeout to 10.0.0.5 after 1234ms and Connection timeout to 10.0.0.7 after 567ms collapse into one group Connection timeout to \x3CN>.\x3CN>.\x3CN>.\x3CN> after \x3CN>ms with count 50.

4. Log-aware grep

# Pattern + level filter
python3 scripts/grep.py app.log --pattern "Database" --level ERROR,FATAL

# Time window
python3 scripts/grep.py app.log \
    --since "2026-05-10T10:00:00Z" \
    --until "2026-05-10T11:00:00Z"

# Context lines (1 before + 1 after each match)
python3 scripts/grep.py app.log --pattern "FATAL" -C 1 --with-line

# Exclude noisy lines while keeping the rest
python3 scripts/grep.py app.log --level ERROR --not-pattern "heartbeat"

# Invert: keep everything that does NOT match
python3 scripts/grep.py app.log --pattern "INFO" --invert

--since and --until accept the same timestamp formats parse.py understands: ISO 8601 (2026-05-10T10:00:00Z, 2026-05-10 10:00:00, with or without microseconds / timezone), apache-style (10/May/2026:10:00:00 +0000), and syslog (May 10 10:00:00 — current year assumed).

Exit codes

This 0 / 1 / 2 split is consistent across all three scripts so they slot into shell pipelines cleanly:

# Parse to JSONL, then summarize errors, then post to a ticket
python3 scripts/parse.py raw.log structured.jsonl \
  && python3 scripts/errors.py raw.log --output ticket.md \
  && cat ticket.md

Safety properties

• Pure Python 3 standard library. No third-party dependencies.
• No subprocess calls. No shell invocation.
• All file paths are validated against a strict allowlist regex that rejects shell metacharacters. The same safe_path() helper used in clean-csv-toolkit and clean-text-toolkit.
• Scripts only read the input paths the caller provides and write to the output paths the caller provides.
• All inputs default to UTF-8; reads fall back through utf-8-sig, cp1252, latin-1 if needed. Writes are always UTF-8.

Timestamp + level detection

_common.py ships a pragmatic timestamp parser that tries the following formats in order, picking the first that matches:

2026-05-10T10:00:00.123456+00:00     (ISO 8601 with TZ + microseconds)
2026-05-10T10:00:00+00:00            (ISO 8601 with TZ)
2026-05-10T10:00:00.123Z              (ISO 8601 UTC Zulu)
2026-05-10T10:00:00Z                  (ISO 8601 UTC Zulu)
2026-05-10T10:00:00                   (ISO 8601 no TZ)
2026-05-10 10:00:00                   (space-separated)
2026/05/10 10:00:00
10/May/2026:10:00:00 +0000           (apache common log)
May 10 10:00:00                       (syslog, no year)

Levels are detected case-insensitively from these tokens and folded to canonical names: TRACE, DEBUG, INFO, NOTICE, WARN (from WARN/WARNING), ERROR (from ERROR/ERR), FATAL (from FATAL/CRITICAL/CRIT/EMERG/EMERGENCY).

Known limitations

• The regex-based parsers are pragmatic, not strict — they accept slightly malformed Apache / nginx / syslog lines as long as the structure is close enough.
• errors.py fingerprint normalization is a best-effort heuristic. Two semantically different errors that happen to differ only in numbers / hashes will be collapsed; if that matters, use --top with a larger N and inspect the samples.
• parse.py does not follow a live log file. For tail-follow, pipe tail -F file | ... into your own tool. If there's enough demand for a built-in follower, it will land in v0.2.

Pairs well with

• clean-csv-toolkit — pipe parse.py output (CSV / JSONL) into inspect, validate, pivot, or transform to turn raw logs into reportable tables.
• clean-text-toolkit — pair parse.py with text-toolkit/redact.py to scrub PII before sharing log dumps.

v0.1.0 changes

• First public release of clean-log-toolkit.
• Three scripts: parse.py, errors.py, grep.py.
• Shared _common.py with safe_path, iter_lines, parse_timestamp, extract_timestamp, extract_level helpers (mirrors the design of clean-csv-toolkit/scripts/_common.py and clean-text-toolkit/scripts/_common.py).
• Auto-detects 5 log formats by sniffing the first 50 lines.
• Zero third-party dependencies; works on any system that ships Python 3.

License

MIT