log-analysis

supercent-io/skills-template · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/supercent-io/skills-template --skill log-analysis
0 commentsdiscussion
summary

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
skill.md

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

  1. Set time range: clearly define the time window to analyze
  2. Save patterns: script common grep patterns
  3. Check context: review logs around the error too (-A, -B options)
  4. Log rotation: search compressed logs with zgrep as well

Constraints

Required Rules (MUST)

  1. Perform read-only operations only
  2. Mask sensitive information (passwords, tokens)

Prohibited (MUST NOT)

  1. Do not modify log files
  2. Do not expose sensitive information externally

References

Examples

Example 1: Basic usage

Example 2: Advanced usage

how to use log-analysis

How to use log-analysis on Cursor

AI-first code editor with Composer

1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/supercent-io/skills-template --skill log-analysis

The skills CLI fetches log-analysis from GitHub repository supercent-io/skills-template and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/log-analysis

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

Submit your Claude Code skill and start earning

GET_STARTED →

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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.762 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.

showing 1-10 of 62

1 / 7