Parse application logs to identify errors, performance issues, and security anomalies.
Works with
Supports multiple log formats including Apache, Nginx, application logs, and JSON with grep-based pattern matching
Covers error debugging, performance analysis (response times, throughput), security audits (SQL injection, XSS, brute force), and incident response
Includes pre-built grep patterns for HTTP error codes, time-based analysis, IP-based traffic analysis, and suspicious access patterns
R
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlog-analysisExecute the skills CLI command in your project's root directory to begin installation:
Fetches log-analysis from supercent-io/skills-template and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate log-analysis. Access via /log-analysis in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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# Common log locations
/var/log/ # System logs
/var/log/nginx/ # Nginx logs
/var/log/apache2/ # Apache logs
./logs/ # Application logs
Common error search:
# Search ERROR-level logs
grep -i "error\|exception\|fail" application.log
# Recent errors (last 100 lines)
tail -100 application.log | grep -i error
# Errors with timestamps
grep -E "^\[.*ERROR" application.log
HTTP error codes:
# 5xx server errors
grep -E "HTTP/[0-9.]+ 5[0-9]{2}" access.log
# 4xx client errors
grep -E "HTTP/[0-9.]+ 4[0-9]{2}" access.log
# Specific error code
grep "HTTP/1.1\" 500" access.log
Time-based analysis:
# Error count by time window
grep -i error application.log | cut -d' ' -f1,2 | sort | uniq -c | sort -rn
# Logs for a specific time window
grep "2025-01-05 14:" application.log
IP-based analysis:
# Request count by IP
awk '{print $1}' access.log | sort | uniq -c | sort -rn | head -20
# Activity for a specific IP
grep "192.168.1.100" access.log
Response time analysis:
# Extract response times from Nginx logs
awk '{print $NF}' access.log | sort -n | tail -20
# Slow requests (>= 1 second)
awk '$NF > 1.0 {print $0}' access.log
Traffic volume analysis:
# Requests per minute
awk '{print $4}' access.log | cut -d: -f1,2,3 | uniq -c
# Requests per endpoint
awk '{print $7}' access.log | sort | uniq -c | sort -rn | head -20
Suspicious patterns:
# SQL injection attempts
grep -iE "(union|select|insert|update|delete|drop).*--" access.log
# XSS attempts
grep -iE "<script|javascript:|onerror=" access.log
# Directory traversal
grep -E "\.\./" access.log
# Brute force attack
grep -E "POST.*/login" access.log | awk '{print $1}' | sort | uniq -c | sort -rn
# Log analysis report
## Summary
- Analysis window: YYYY-MM-DD HH:MM ~ YYYY-MM-DD HH:MM
- Total log lines: X,XXX
- Error count: XXX
- Warning count: XXX
## Error analysis
| Error type | Occurrences | Last seen |
|----------|-----------|----------|
| Error A | 150 | 2025-01-05 14:30 |
| Error B | 45 | 2025-01-05 14:25 |
## Recommended actions
1. [Action 1]
2. [Action 2]
-A, -B options)Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
supercent-io/skills-template
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Solid pick for teams standardizing on skills: log-analysis is focused, and the summary matches what you get after install.
log-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
log-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend log-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend log-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
log-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
log-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
log-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for log-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend log-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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