Implement comprehensive structured logging with proper levels, context, and centralized aggregation for effective debugging and monitoring.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionapplication-loggingExecute the skills CLI command in your project's root directory to begin installation:
Fetches application-logging from aj-geddes/useful-ai-prompts 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 application-logging. Access via /application-logging 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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Implement comprehensive structured logging with proper levels, context, and centralized aggregation for effective debugging and monitoring.
Minimal working example:
// logger.js
const winston = require("winston");
const logFormat = winston.format.combine(
winston.format.timestamp({ format: "YYYY-MM-DD HH:mm:ss" }),
winston.format.errors({ stack: true }),
winston.format.json(),
);
const logger = winston.createLogger({
level: process.env.LOG_LEVEL || "info",
format: logFormat,
defaultMeta: {
service: "api-service",
environment: process.env.NODE_ENV || "development",
},
transports: [
new winston.transports.Console({
format: winston.format.combine(
winston.format.colorize(),
winston.format.simple(),
),
}),
new winston.transports.File({
filename: "logs/error.log",
// ... (see reference guides for full implementation)
Detailed implementations in the references/ directory:
| Guide | Contents |
|---|---|
| Node.js Structured Logging with Winston | Node.js Structured Logging with Winston |
| Express HTTP Request Logging | Express HTTP Request Logging |
| Python Structured Logging | Python Structured Logging |
| Flask Integration | Flask Integration |
| ELK Stack Setup | ELK Stack Setup |
| Logstash Configuration | Logstash Configuration |
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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Keeps context tight: application-logging is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend application-logging for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
application-logging reduced setup friction for our internal harness; good balance of opinion and flexibility.
application-logging has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added application-logging from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for application-logging matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: application-logging is the kind of skill you can hand to a new teammate without a long onboarding doc.
application-logging fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
application-logging has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: application-logging is focused, and the summary matches what you get after install.
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