Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/JansenAnalytics/claudexnpx agentmods add skills/jansenanalytics/claudex/log-analyzerWrote 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/jansenanalytics/claudex/log-analyzer)<a href="https://agentmods.dev/skills/jansenanalytics/claudex/log-analyzer"><img src="https://agentmods.dev/badge/skills/jansenanalytics/claudex/log-analyzer.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00037 | $0.00480 |
| Opus 5 | $0.00018 | $0.00240 |
| Sonnet 5 | $0.00007 | $0.00096 |
| Haiku 4.5 | $0.00004 | $0.00048 |
Grade A, and why
log-analyzer 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 3d 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.
What it actually says
log-analyzer
Parse and analyze application logs: pattern matching, error extraction, correlation with timestamps, frequency analysis, anomaly detection. Use when: diagnosing production issues, finding error patterns in logs, correlating errors with deployments, analyzing log frequency/trends, or filtering noise from large log files.
Scripts
log-parse.py
Universal log parser with auto-format detection and filtering.
# Basic usage
python3 scripts/log-parse.py --input app.log
# Filter by level
python3 scripts/log-parse.py --input app.log --level error,warn
# Time-based filter
python3 scripts/log-parse.py --input app.log --since "2024-01-15 14:00"
# Pattern matching
python3 scripts/log-parse.py --input app.log --grep "database" --exclude "health"
# Markdown output
python3 scripts/log-parse.py --input app.log --level error --format markdown
Auto-detects: JSON logs, syslog, nginx access/error, Node.js console, Python logging.
error-summary.py
Aggregates and groups errors from log files.
python3 scripts/error-summary.py --input app.log --top 20
python3 scripts/error-summary.py --input app.log --input server.log --top 10
Groups by fuzzy-matched error message (strips variable parts like IDs, timestamps). Reports: unique count, first/last occurrence, frequency, sample stack trace.
log-correlator.sh
Correlates events across multiple log files within a time window.
bash scripts/log-correlator.sh --logs "app.log,nginx.log,system.log" --time "2024-01-15 14:32" --window 5m
References
references/log-formats.md— Common log format patterns with regexreferences/troubleshooting-guide.md— Systematic troubleshooting with logs
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.
- 3d ago First seen · 61 lines · 37 tokens per session scan A befcd17ecad5
log-analyzer is a skill published in the GitHub repository JansenAnalytics/claudex (5 stars, last pushed 2mo ago), licensed MIT. It adds 37 tokens to every session and 480 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
Log Analysis & SIEM Integration
Security log parsing, anomaly detection, SIEM query building, Sigma rule creation, and correlation rule development across Splunk, Elastic, QRadar, and Microsoft Sentinel.
self-ops
Runtime self-diagnosis knowledge: database layout, observability APIs, timeout hierarchy, restart procedure, failure replay. Use when the user asks to debug this runtime itself — inspect its database, traces, prompt snapshots, timeouts, service health, or configuration.
cross-source-correlation
Correlate near-in-time events across PR merges, deploys, monitoring (Sentry/NewRelic/Datadog/AppDynamics) and ticket trackers (Jira/Azure DevOps) into confidence-scored clusters that answer "which deploy caused this bug" without manual tab-switching.
m4-setup
Diagnose and repair common M4 environment, dataset, skill installation, backend, and vitrine setup problems. Use when M4 tools, datasets, skills, or visualization are missing or broken.
mpm-doctor
Run diagnostic checks on Claude MPM installation.
Distributed Tracing Testing
Testing distributed tracing implementations including context propagation, span correlation, baggage handling, and trace sampling verification.