Claude Code Guide is a reference collection for configuring and using Claude Code, Anthropic’s command-line coding agent. Developers use it to learn commands, skills, agents, MCP, automation, security, integrations, and troubleshooting. Its catalogue add-ons provide many of the documented skills and agents.
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 skills add zebbern/claude-code-guide --skill log-error-digestgit clone --depth 1 https://github.com/zebbern/claude-code-guideWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zebbern/claude-code-guide/log-error-digest)<a href="https://agentmods.dev/skills/zebbern/claude-code-guide/log-error-digest"><img src="https://agentmods.dev/badge/skills/zebbern/claude-code-guide/log-error-digest/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zebbern/claude-code-guide/log-error-digest"><img src="https://agentmods.dev/badge/skills/zebbern/claude-code-guide/log-error-digest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00071 | $0.01060 |
| Opus 5.5 | $0.00028 | $0.00424 |
| Sonnet 5.5 | $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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- log-error-digest — 100% identical, 0 lines differ
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.
- 1mo 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 (4,665 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 1,060 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
log-diagnostic
A log-analysis tool that detects JSON, syslog, and Nginx formats and reports grouped errors, occurrence counts, severity levels, and time distribution. It can filter entries by level or time range and export JSON.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
performance-analysis
Performance analysis, bottleneck detection, and optimization recommendations. Use when profiling slow code or systems, hunting a performance regression, or producing an optimization plan with measurable targets.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms.
Error Log Summarizer
Parses raw error logs and produces a concise, prioritized summary of unique issues with root cause hints.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms.