Make Privacy Deletion Complete
Repair privacy deletion workflows that leave personal data behind in files, caches, search, analytics, logs, or third-party systems.
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You say
Identify where user data lives in our Node/Postgres stack and help us implement a durable deletion worker that also hits our S3 buckets and search index.
Your agent does
Project Detected: Node.js, PostgreSQL, AWS S3, Elasticsearch.
Subject Boundary:
- Subject: User
- Stable identity: user_id (UUID)
- Tenant scope: organization_id
- Authorization path: middleware/auth.js
Data Inventory:
- Primary records: users, profiles, billing_methods
- Derived data: activity_logs, audit_trail
- Files and indexes: s3://uploads/user-{id}, es_index/users
- External systems: Stripe, Postmark
- Backups and logs: CloudWatch logs (retention: 90 days)
Incomplete Erasure Found:
- S3 objects remain after DB row deletion
- Data remaining: Profile images, CSV exports
- Destination: AWS S3
- Exposure or recreation risk: Publicly accessible signed URLs persist until expiration.
What you get
About this skill
The problem
Standard account deletion often leaves fragments of personal data behind in search indexes, object storage, background job payloads, and third-party logs. Relying on simple database cascades fails to handle asynchronous systems, partial failures, or distributed data footprints.
What it does
- Traces personal data across relational databases, document stores, caches, and message queues.
- Builds durable orchestration to manage multi-system erasure with idempotent retries.
- Implements write barriers and tombstones to prevent data recreation from delayed processes.
- Coordinates de-identification for shared business records and retained audit evidence.
- Generates independent verification steps to prove data absence across every destination.
Frameworks & tools
Compatible with standard relational and document databases, S3-compatible object storage, Redis, Elasticsearch, and third-party API providers like Stripe, Segment, or SendGrid.
Why this beats prompting it yourself
Handling erasure manually often misses derived data in dead-letter queues or versioned storage buckets. This skill enforces a strict state model and subject-identity rules that prevent cross-tenant deletion and survive worker restarts, ensuring technical compliance that simple scripts cannot guarantee.
Use cases
- Repairing incomplete deletion logic that leaves orphaned files in object storage.
- Implementing durable erasure workflows that handle third-party API rate limits.
- Scrubbing personal identifiers from analytics events and log traces.
- Preventing account recreation from repopulating search indexes with stale data.
Known limitations
Cannot rewrite immutable backups or physical archives. Does not provide legal advice or determine statutory retention periods.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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