implementing-zero-knowledge-proof-for-authentication▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Zero-Knowledge Proofs (ZKPs) allow a prover to demonstrate knowledge of a secret (such as a password or private key) without revealing the secret itself. This skill implements the Schnorr identificati
| name | implementing-zero-knowledge-proof-for-authentication |
| description | Zero-Knowledge Proofs (ZKPs) allow a prover to demonstrate knowledge of a secret (such as a password or private key) without revealing the secret itself. This skill implements the Schnorr identificati |
| domain | cybersecurity |
| subdomain | cryptography |
| tags | - cryptography - zero-knowledge-proof - authentication - privacy - zkp |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - PR.DS-01 - PR.DS-02 - PR.DS-10 |
Implementing Zero-Knowledge Proof for Authentication
Overview
Zero-Knowledge Proofs (ZKPs) allow a prover to demonstrate knowledge of a secret (such as a password or private key) without revealing the secret itself. This skill implements the Schnorr identification protocol and a simplified ZKPP (Zero-Knowledge Password Proof) using the discrete logarithm problem, enabling authentication where the server never learns the user's password.
When to Use
- When deploying or configuring implementing zero knowledge proof for authentication capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Familiarity with cryptography concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Objectives
- Implement Schnorr's identification protocol for ZKP authentication
- Build a non-interactive ZKP using Fiat-Shamir heuristic
- Implement zero-knowledge password proof (ZKPP)
- Demonstrate completeness, soundness, and zero-knowledge properties
- Compare ZKP authentication with traditional password verification
Key Concepts
ZKP Properties
| Property | Description |
|---|---|
| Completeness | Honest prover always convinces honest verifier |
| Soundness | Dishonest prover cannot convince verifier (except negligible probability) |
| Zero-Knowledge | Verifier learns nothing beyond the statement's truth |
Schnorr Protocol
- Setup: Public generator g, prime p, q (order of g)
- Registration: Prover computes y = g^x mod p (public key from secret x)
- Commitment: Prover sends t = g^r mod p (random r)
- Challenge: Verifier sends random c
- Response: Prover sends s = r + c*x mod q
- Verify: Check g^s == t * y^c mod p
Security Considerations
- Use cryptographically secure random number generators
- Challenge must be unpredictable (from verifier's perspective)
- For non-interactive proofs, use Fiat-Shamir with collision-resistant hash
- ZKP alone does not provide forward secrecy; combine with TLS
Validation Criteria
- Honest prover always verifies successfully (completeness)
- Random response without secret does not verify (soundness)
- Server never receives the secret value
- Non-interactive proof is verifiable offline
- Multiple authentications produce different transcripts
- Protocol resists replay attacks
How to use implementing-zero-knowledge-proof-for-authentication 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 implementing-zero-knowledge-proof-for-authentication
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches implementing-zero-knowledge-proof-for-authentication from GitHub repository mukul975/Anthropic-Cybersecurity-Skills 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 implementing-zero-knowledge-proof-for-authentication. Access the skill through slash commands (e.g., /implementing-zero-knowledge-proof-for-authentication) 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▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★60 reviews- ★★★★★Maya Garcia· Dec 28, 2024
implementing-zero-knowledge-proof-for-authentication is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Chaitanya Patil· Dec 20, 2024
Useful defaults in implementing-zero-knowledge-proof-for-authentication — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Meera Johnson· Dec 20, 2024
Useful defaults in implementing-zero-knowledge-proof-for-authentication — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Maya Mehta· Dec 12, 2024
We added implementing-zero-knowledge-proof-for-authentication from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kwame Srinivasan· Nov 19, 2024
implementing-zero-knowledge-proof-for-authentication fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Piyush G· Nov 11, 2024
implementing-zero-knowledge-proof-for-authentication has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Isabella Iyer· Nov 11, 2024
implementing-zero-knowledge-proof-for-authentication has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Meera Smith· Nov 3, 2024
Keeps context tight: implementing-zero-knowledge-proof-for-authentication is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Mateo Smith· Oct 22, 2024
implementing-zero-knowledge-proof-for-authentication is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Sophia Bansal· Oct 10, 2024
We added implementing-zero-knowledge-proof-for-authentication from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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