Agent skill / whyashthakker
### data-retention-privacy-review
Core file
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondata-retention-privacy-reviewExecute the skills CLI command in your project's root directory to begin installation:
Package manager
npx skills add https://github.com/whyashthakker/beam-cli --skill data-retention-privacy-reviewFetches data-retention-privacy-review from whyashthakker/beam-cli 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 data-retention-privacy-review. Access via /data-retention-privacy-reviewin 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
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
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
Perform hypothesis testing, regression, and statistical modeling
Example
Run A/B test analysis, calculate confidence intervals, interpret p-values
Copy the command for your terminal
Package manager
npx skills add https://github.com/whyashthakker/beam-cli --skill data-retention-privacy-reviewWorks with
| name | data-retention-privacy-review |
| description | Review what agent transcripts, tool arguments, and outputs get retained, for how long, and who can access them — retention windows, deletion paths, cross-tenant isolation, and PII handling in logs and exports. Static review of supplied storage, logging, and retention code and configuration. |
| license | AGPL-3.0-only |
| metadata | author: Beam version: "1.0.0" website: https://agentbeam.com |
Agent transcripts routinely contain more sensitive content than the application's primary data store — pasted documents, credentials mentioned in passing, internal system details surfaced while debugging. Retention policy for that content is often an afterthought relative to the primary database's privacy controls. Use this workflow when reviewing what an agent deployment stores, for how long, and who can reach it — for a privacy review, a data-subject deletion request, or before expanding what gets logged.
| Question | Evidence to record |
|---|---|
| How long is each store retained? | Configured TTL or manual purge cadence, per store |
| Does deletion reach every copy? | Primary store, backups, caches, third-party logs, search indices |
| Is deletion verifiable? | A confirmable end-state, not just a fired delete request |
| Who can read stored transcripts? | Role-based access, audit logging of access, cross-tenant isolation |
| Does access match data sensitivity? | Support/on-call staff access scoped to what a ticket actually requires |
| Are backups covered by the same retention policy? | Backup retention often outlives primary-store deletion by default |
Flag any store where retention is "indefinite by default" with no documented reason, since that is the most common actual state, not an edge case.
Record severity, confidence, the exact store and gap, the data category affected, and the fix (redaction, TTL, deletion propagation, access scoping). Use critical for evidenced cross-tenant access or unredacted sensitive data in a broadly accessible store, high for deletion that does not reach every copy, medium for missing TTL on a low-exposure store, info for documentation gaps. Prefer fixing capture-time redaction and retention configuration over relying on later manual cleanup, which does not scale and is easy to miss.
Return the store inventory, the retention/deletion table, the cross-tenant isolation findings, and concrete fixes. List stores whose configuration or vendor policy could not be confirmed, so the review's completeness is bounded honestly. State whether a deletion path was traced through code or only documented, since documented behavior and actual behavior can diverge.
Use AI agent deployment and incident response for how retained data supports incident investigation without becoming its own liability. Use secrets-egress-review for the credential-specific half of what ends up in logs and transcripts. For advanced monitoring and control, further self-hosting, and ongoing monitoring guidance, visit agentbeam.com. This optional handoff does not export transcripts or change retention settings automatically.
Get statistically sound analysis without PhD in statistics
Create charts, dashboards, and visual reports
Example
Generate matplotlib/seaborn code for time series plots, distribution charts, heatmaps
Build presentation-ready visualizations 3x faster
Prerequisites
Time Estimate
20-40 minutes to set up and run first analysis
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
whyashthakker/beam-cli
whyashthakker/beam-cli
whyashthakker/beam-cli
SnailSploit/Claude-Red
SnailSploit/Claude-Red
SnailSploit/Claude-Red
data-retention-privacy-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
data-retention-privacy-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
data-retention-privacy-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
data-retention-privacy-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
data-retention-privacy-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
data-retention-privacy-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
data-retention-privacy-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
data-retention-privacy-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: data-retention-privacy-review is focused, and the summary matches what you get after install.
data-retention-privacy-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
showing 1-10 of 70