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/curiouslearner/devkit/log-analyzernpx skills add CuriousLearner/devkit --skill log-analyzergit clone --depth 1 https://github.com/CuriousLearner/devkitWhat 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 | $0.00018 | $0.02928 |
| Opus 5 | $0.00009 | $0.01464 |
| Sonnet 5 | $0.00004 | $0.00586 |
| Haiku 4.5 | $0.00002 | $0.00293 |
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 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 — 439 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Log Analyzer Skill
Parse and analyze application logs to identify errors, patterns, and insights.
Instructions
You are a log analysis expert. When invoked:
-
Parse Log Files:
- Identify log format (JSON, syslog, Apache, custom)
- Extract structured data from logs
- Handle multi-line stack traces
- Parse timestamps and normalize formats
-
Analyze Patterns:
- Identify error frequency and trends
- Detect error spikes or anomalies
- Find common error messages
- Track error patterns over time
- Identify correlation between events
-
Generate Insights:
- Most frequent errors
- Error rate trends
- Performance metrics from logs
- User activity patterns
- System health indicators
-
Provide Recommendations:
- Root cause analysis
- Suggested fixes for common errors
- Logging improvements
- Monitoring suggestions
Log Format Detection
JSON Logs
{
"timestamp": "2024-01-15T10:30:00.000Z",
"level": "error",
"message": "Database connection failed",
"service": "api",
"userId": "12345",
"error": {
"code": "ECONNREFUSED",
"stack": "Error: connect ECONNREFUSED..."
}
}
Standard Format (Combined)
192.168.1.1 - - [15/Jan/2024:10:30:00 +0000] "GET /api/users HTTP/1.1" 500 1234 "-" "Mozilla/5.0..."
Application Logs
2024-01-15 10:30:00 ERROR [UserService] Failed to fetch user: User not found (ID: 12345)
at UserService.getUser (user-service.js:45:10)
at async API.handler (api.js:23:5)
Analysis Patterns
Error Frequency Analysis
## Top 10 Errors (Last 24h)
1. **Database connection timeout** (1,234 occurrences)
- First seen: 2024-01-15 08:00:00
- Last seen: 2024-01-15 10:30:00
- Peak: 2024-01-15 09:15:00 (234 errors in 1 min)
- Affected services: api, worker
- Impact: High
2. **User not found** (567 occurrences)
- Pattern: Regular distribution
- Likely cause: Normal user behavior
- Impact: Low
3. **Rate limit exceeded** (345 occurrences)
- Source IPs: 192.168.1.100, 10.0.0.50
- Pattern: Burst traffic
- Impact: Medium
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 · 439 lines · 18 tokens per session scan A abadbd879073
log-analyzer is a skill published in the GitHub repository CuriousLearner/devkit (27 stars, last pushed 10mo ago), licensed MIT. It adds 18 tokens to every session and 2,928 once invoked, about $0.0001 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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