Privacy Patterns
CheckedAnalyzes data access patterns to proactively identify and mitigate potential privacy risks within your organization.
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Showing Claude Code. One command installs this skill into any of the agents below.
- 1
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terminal$ npx skills add dajo-code/midnight-dev-skills --skill privacy-patterns -a claude-code Uses the open-source
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What we know about this skill
Publisher
fractionestate
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Install target
Live repository
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Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
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No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned fractionestate/midnight-dev-skills at 9cad80c46e.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 9cad80c46e
- Scanned
- 6 files, 1,147 lines
About this skill
What it does
This skill provides a collection of privacy patterns for AI agent development. It allows agents to understand and apply various strategies for protecting user data, including differential privacy, federated learning, and homomorphic encryption. The goal is to build more trustworthy and ethical AI systems that respect user privacy.
When to use it
- Developing a chatbot: Implement techniques to ensure conversations are not stored or used in ways that compromise user confidentiality.
- Building a data analysis tool: Apply differential privacy methods to generate insights from sensitive datasets without revealing individual records.
- Creating a personalized recommendation system: Utilize federated learning to train models on decentralized data sources while preserving user privacy.
- Designing an AI-powered healthcare application: Integrate homomorphic encryption to perform computations on encrypted patient data, maintaining confidentiality.
Key capabilities
- Differential Privacy implementation
- Federated Learning integration
- Homomorphic Encryption support
- Privacy Pattern Library access
Example prompts
- "Suggest privacy patterns for a chatbot that handles user financial information."
- "How can I apply differential privacy to my data analysis pipeline?"
- "Explain federated learning and its benefits for protecting user data in a recommendation system."
Tips & gotchas
This skill requires a foundational understanding of AI/ML concepts. Successfully applying these patterns often involves trade-offs between privacy guarantees and model accuracy or performance, so careful evaluation is needed.