log-analysis▌
supercent-io/skills-template · updated Apr 8, 2026
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Parse application logs to identify errors, performance issues, and security anomalies.
- ›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
Log Analysis
When to use this skill
- Error debugging: analyze the root cause of application errors
- Performance analysis: analyze response times and throughput
- Security audit: detect anomalous access patterns
- Incident response: investigate the root cause during an outage
Instructions
Step 1: Locate Log Files
# Common log locations
/var/log/ # System logs
/var/log/nginx/ # Nginx logs
/var/log/apache2/ # Apache logs
./logs/ # Application logs
Step 2: Search for Error Patterns
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
Step 3: Pattern Analysis
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
Step 4: Performance Analysis
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
Step 5: Security Analysis
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
Output format
Analysis report structure
# 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]
Best practices
- Set time range: clearly define the time window to analyze
- Save patterns: script common grep patterns
- Check context: review logs around the error too (
-A,-Boptions) - Log rotation: search compressed logs with zgrep as well
Constraints
Required Rules (MUST)
- Perform read-only operations only
- Mask sensitive information (passwords, tokens)
Prohibited (MUST NOT)
- Do not modify log files
- Do not expose sensitive information externally
References
Examples
Example 1: Basic usage
Example 2: Advanced usage
How to use log-analysis on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add log-analysis
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches log-analysis from GitHub repository supercent-io/skills-template and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate log-analysis. Access the skill through slash commands (e.g., /log-analysis) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★62 reviews- ★★★★★Tariq Singh· Dec 28, 2024
Solid pick for teams standardizing on skills: log-analysis is focused, and the summary matches what you get after install.
- ★★★★★Anika Gill· Dec 28, 2024
log-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Camila Thomas· Dec 24, 2024
log-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Shikha Mishra· Dec 4, 2024
I recommend log-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Camila Taylor· Dec 4, 2024
I recommend log-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Yash Thakker· Nov 23, 2024
log-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ren Harris· Nov 23, 2024
log-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Anika Patel· Nov 19, 2024
log-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Anika Thompson· Nov 19, 2024
Registry listing for log-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Liam Diallo· Nov 19, 2024
I recommend log-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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