Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/zebbern/claude-code-guide/log-error-digestnpx skills add zebbern/claude-code-guide --skill log-error-digestgit clone --depth 1 https://github.com/zebbern/claude-code-guideWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00071 | $0.01060 |
| Opus 5 | $0.00036 | $0.00530 |
| Sonnet 5 | $0.00014 | $0.00212 |
| Haiku 4.5 | $0.00007 | $0.00106 |
Grade A, and why
log-error-digest scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Log Error Digest
Automated log file analysis that produces error clustering, frequency statistics, and time distribution reports.
Features
- Error Clustering: Groups similar error messages by normalizing dynamic parts (IPs, UUIDs, numbers, etc.) to identify root causes
- Frequency Statistics: Counts occurrences by error type, sorted by severity
- Time Distribution: Shows error distribution by hour and by date, helping pinpoint peak error periods
Supported Log Formats
| Format | Description | Auto-detection |
|---|---|---|
| JSON | One JSON object per line with timestamp/level/message fields |
Starts with { |
| syslog | RFC 3164 format, e.g. Jan 1 12:00:00 host proc[pid]: msg |
Starts with month name |
| Nginx | Access log or error log format | Starts with IP or date/path pattern |
Usage
python scripts/analyze_logs.py <log_file_path> [options]
Parameters
| Parameter | Description | Default |
|---|---|---|
log_file |
Path to the log file (required) | - |
--format |
Log format: auto/json/syslog/nginx |
auto |
--top |
Show Top N error clusters | 20 |
--output |
Export results to a JSON file | Terminal output only |
--level |
Filter by log level (e.g. ERROR, WARN) |
All levels |
--since |
Only analyze logs after this time (ISO format) | No limit |
--until |
Only analyze logs before this time (ISO format) | No limit |
Examples
# Auto-detect format and analyze the entire log file
python scripts/analyze_logs.py /var/log/app.log
# Specify Nginx format, show only Top 10 errors
python scripts/analyze_logs.py /var/log/nginx/error.log --format nginx --top 10
# Filter ERROR level only, export JSON report
python scripts/analyze_logs.py app.log --level ERROR --output report.json
# Analyze logs within a specific time range
python scripts/analyze_logs.py app.log --since 2024-01-01T00:00:00 --until 2024-01-02T00:00:00
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 101 lines · 71 tokens per session scan A a76b63daae69
log-error-digest is a skill published in the GitHub repository zebbern/claude-code-guide (4,597 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 1,060 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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