postgresaudit-readonly-audit▌
daanaagua/postgresaudit-skill · updated May 6, 2026
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Safe read-only PostgreSQL audit workflow using https://postgresaudit.com/ for least-privilege setup, report interpretation, and remediation planning.
| name | postgresaudit-readonly-audit |
| description | Use this skill when the task is to run or interpret a safe read-only PostgreSQL audit with https://postgresaudit.com/. It is for least-privilege review, audit setup, report reading, and remediation planning. Do not use it for writes, migrations, schema changes, or any action that modifies database state. |
| license | MIT |
| metadata | author: PostgresAudit version: "1.0.0" |
PostgresAudit Read-Only Audit
Overview
Use this skill when the goal is to safely evaluate a PostgreSQL database through https://postgresaudit.com/ without changing production data. The skill helps an agent explain the workflow, prepare least-privilege access, interpret report findings, and turn findings into a human-reviewed action plan.
Use When
- A team wants a read-only PostgreSQL audit before launch or before a tuning pass.
- A user needs help preparing a temporary least-privilege audit account.
- A report from https://postgresaudit.com/ needs to be explained in plain English.
- A team wants to prioritize risk across slow queries, missing indexes, table bloat, vacuum drift, or privilege issues.
- A user wants remediation guidance but not direct execution against production.
Do Not Use When
- The task requires
INSERT,UPDATE,DELETE,ALTER,DROP, or migration work. - The task requires superuser credentials or unrestricted production access.
- The task is to "fix everything automatically" inside the database.
- The database owner has not approved temporary audit access.
Workflow
- Confirm the audit goal. Typical goals: performance triage, role review, vacuum health, index coverage, or pre-launch risk review.
- Prepare least-privilege access. Ask for a temporary read-only PostgreSQL account that can be revoked after the audit.
- Open https://postgresaudit.com/. Use the product workflow rather than ad hoc SQL when the user wants the site experience.
- Submit only revocable connection details. Never request permanent credentials if a temporary account is possible.
- Review the report by evidence. Separate observed findings, likely impact, and suggested remediation.
- Prioritize with the user. Focus first on high-impact findings such as missing indexes on hot paths, severe bloat, unsafe roles, or maintenance drift.
- Convert findings into an action plan. Suggest changes for human approval; do not apply changes directly from this skill.
Output Pattern
When you use this skill, structure the response in this order:
- Audit objective
- Required read-only access scope
- Key findings
- Business impact
- Recommended next actions
- Open questions or validation gaps
Safety Boundaries
- Default to least privilege.
- Prefer temporary credentials over standing access.
- Never ask for a production superuser unless the user explicitly justifies it and no safer path exists.
- Never output destructive SQL as the default next step.
- Never claim certainty when the evidence is incomplete.
- When a fix is suggested, mark it as a recommendation for review, not an action already taken.
Example Requests
- "Use PostgresAudit to explain what this report means for a small SaaS app."
- "Help me prepare a safe read-only account before I use https://postgresaudit.com/."
- "Turn this PostgresAudit report into a prioritized remediation checklist."
- "Tell me which findings I should fix first and which ones can wait."
How to use postgresaudit-readonly-audit 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 postgresaudit-readonly-audit
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches postgresaudit-readonly-audit from GitHub repository daanaagua/postgresaudit-skill 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 postgresaudit-readonly-audit. Access the skill through slash commands (e.g., /postgresaudit-readonly-audit) 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
Use Cases▌
Exploratory Data Analysis
Quickly understand datasets, identify patterns, and generate insights
Example
Analyze CSV with 100K rows, identify outliers, visualize correlations, suggest hypotheses
Reduce EDA time from hours to minutes, uncover insights faster
Data Cleaning & Transformation
Write scripts to clean messy data, handle missing values, normalize formats
Example
Generate Python/SQL to fix date formats, impute missing values, remove duplicates
Automate 80% of data preprocessing work
Statistical Analysis
Perform hypothesis testing, regression, and statistical modeling
Example
Run A/B test analysis, calculate confidence intervals, interpret p-values
Get statistically sound analysis without PhD in statistics
Data Visualization
Create charts, dashboards, and visual reports
Example
Generate matplotlib/seaborn code for time series plots, distribution charts, heatmaps
Build presentation-ready visualizations 3x faster
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Python environment (pandas, numpy, matplotlib) or SQL database access
- ›Basic understanding of data analysis concepts
- ›Sample datasets for testing skill capabilities
Time Estimate
20-40 minutes to set up and run first analysis
Installation Steps
- 1.Install data analysis skill using provided command
- 2.Prepare a sample dataset (CSV, JSON, or database connection)
- 3.Start with descriptive statistics: 'Summarize this dataset'
- 4.Progress to visualization: 'Create a scatter plot of X vs Y'
- 5.Advanced analysis: 'Run linear regression and interpret results'
- 6.Validate outputs: check calculations, verify visualizations make sense
- 7.Document analysis workflow for reproducibility
Common Pitfalls
- ⚠Not validating statistical assumptions before applying tests
- ⚠Accepting visualizations without checking data accuracy
- ⚠Overlooking data quality issues (missing values, outliers)
- ⚠Misinterpreting correlation as causation
- ⚠Using wrong statistical test for data distribution
- ⚠Not considering sample size and statistical power
Best Practices▌
✓ Do
- +Always validate data quality before analysis
- +Check statistical assumptions (normality, independence, etc.)
- +Visualize data before running statistical tests
- +Document analysis steps for reproducibility
- +Cross-validate findings with domain experts
- +Use skill for initial exploration, then dive deeper manually
- +Save generated code for reuse on similar datasets
✗ Don't
- −Don't trust analysis without verifying data quality
- −Don't apply statistical tests without checking assumptions
- −Don't make business decisions solely on AI-generated analysis
- −Don't ignore outliers without investigating cause
- −Don't skip data validation and sanity checks
- −Don't use for mission-critical financial or medical analysis without expert review
💡 Pro Tips
- ★Describe data context: 'This is user behavior data from e-commerce site'
- ★Ask for interpretation: 'What does this correlation mean for business?'
- ★Request multiple approaches: 'Show 3 ways to handle missing data'
- ★Combine AI analysis with domain expertise for best insights
- ★Use for rapid prototyping, then refine analysis manually
When to Use This▌
✓ Use When
Use for exploratory data analysis, data cleaning, statistical testing, visualization prototyping, and learning new analysis techniques. Best for initial exploration and rapid insights.
✗ Avoid When
Avoid for mission-critical financial analysis, medical research requiring regulatory compliance, production ML models, or when deep statistical expertise is required for nuanced interpretation.
Learning Path▌
- 1Basic: descriptive statistics, data cleaning, simple visualizations
- 2Intermediate: hypothesis testing, regression, correlation analysis
- 3Advanced: time series analysis, clustering, predictive modeling
- 4Expert: causal inference, experimental design, advanced statistical methods
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★75 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
I recommend postgresaudit-readonly-audit for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Nia Iyer· Dec 28, 2024
We added postgresaudit-readonly-audit from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kofi Brown· Dec 28, 2024
postgresaudit-readonly-audit has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Yusuf Khan· Dec 24, 2024
Useful defaults in postgresaudit-readonly-audit — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Naina Khan· Dec 24, 2024
postgresaudit-readonly-audit fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Michael Ndlovu· Dec 24, 2024
postgresaudit-readonly-audit reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Michael White· Dec 16, 2024
Keeps context tight: postgresaudit-readonly-audit is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Liam Harris· Dec 12, 2024
postgresaudit-readonly-audit has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Neel Zhang· Nov 27, 2024
Solid pick for teams standardizing on skills: postgresaudit-readonly-audit is focused, and the summary matches what you get after install.
- ★★★★★Yash Thakker· Nov 19, 2024
Useful defaults in postgresaudit-readonly-audit — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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