Authorization and Reputation

Wildmeta implements comprehensive authorization and reputation systems that balance autonomous trading capabilities with security, trust, and accountability across the decentralized skills marketplace.

Authorization Architecture

The authorization framework maintains on-chain mapping between users and approved skill providers, ensuring only explicitly authorized skills can execute operations on behalf of users. This permission system operates at the smart contract level, providing cryptographic guarantees rather than relying on centralized access controls.

Authorization lists specify granular permissions for different operation types, enabling users to grant market-making capabilities to some skills while restricting others to read-only analysis functions. This flexibility ensures appropriate risk management while maximizing utility from diverse skill providers.

Dual-signature verification protects against unauthorized operations through cryptographic proof requirements. User signatures confirm consent for specific operations and spending limits, while skill provider signatures verify the authenticity of execution requests from approved entities.

Time-limited authorizations enable temporary skill access without permanent permission grants. Users can authorize copy trading skills for specific durations or trading competitions, automatically revoking access when defined periods expire or conditions are met.

ERC-8004 Standard Implementation

Wildmeta leverages the ERC-8004 standard for trustless agent operations, creating a robust foundation for reputation tracking and validation across the autonomous trading ecosystem. This implementation enables discovery and interaction with agents across organizational boundaries without pre-existing trust relationships.

Identity Registry utilizes ERC-721 NFT standards to provide portable, censorship-resistant identifiers for all skill providers. This approach makes skills immediately browsable and transferable through NFT-compliant applications while maintaining permanent on-chain identity records.

Reputation Registry captures structured feedback and experience data from user interactions with different skills. The system supports numerical scoring from 0-100 along with optional categorized tags that provide nuanced performance assessment across different market conditions and strategy types.

Validation Registry implements generic hooks for requesting and recording independent verification of skill performance. This system supports multiple validation methods including stake-secured re-execution, zero-knowledge machine learning proofs, and trusted execution environment attestations.

Staking and Slashing Mechanisms

Post-TGE implementation requires skill providers to stake WILD tokens proportional to their execution privileges and risk exposure. Staking amounts ensure adequate economic incentives for proper behavior while creating meaningful consequences for misconduct or poor performance.

Slashing protocols enable governance-based penalty enforcement when skills demonstrate misbehavior, including unauthorized trades, excessive risk exposure, or systematic underperformance relative to stated objectives. Community governance determines slashing conditions and penalty amounts based on violation severity and ecosystem impact.

Economic security scales with execution capabilities, requiring higher stakes for skills with broader authorization scope or larger potential impact on user portfolios. This graduated approach ensures cost efficiency for simple skills while maintaining strong security for high-impact operations.

Reputation Scoring Framework

Multi-dimensional reputation tracking captures performance across different categories including execution accuracy, risk management, communication quality, and adherence to stated strategies. This comprehensive approach enables users to evaluate skills based on factors most relevant to their trading objectives.

Historical performance data creates objective baselines for skill evaluation, tracking metrics like Sharpe ratios, maximum drawdowns, and strategy consistency over different market regimes. Time-weighted scoring emphasizes recent performance while maintaining historical context for long-term reliability assessment.

Cross-validation mechanisms prevent reputation manipulation through independent verification of claimed performance metrics. Multiple data sources and validation methods ensure reputation scores reflect actual skill quality rather than self-reported or manipulated statistics.

Trust Model Selection

Users can choose from multiple trust models based on their risk tolerance and verification preferences. Reputation-based systems rely on community feedback and historical performance data for skill evaluation and selection decisions.

Crypto-economic validation requires validators to stake capital and re-execute skill computations, with slashing penalties for incorrect validation. This approach provides strong security guarantees for users requiring maximum confidence in skill operations.

Cryptographic validation leverages trusted execution environments or zero-knowledge proofs to verify correct skill execution with mathematical certainty. This model offers the highest security level while maintaining confidentiality advantages for sensitive trading strategies.

Governance Integration

Community governance manages reputation thresholds, slashing conditions, and dispute resolution processes through transparent voting mechanisms. Token holder participation ensures stakeholder alignment with ecosystem health and user protection objectives.

Appeals processes provide recourse for skills facing reputation damage or slashing penalties, enabling fair resolution of disputes while maintaining system integrity. Multi-stakeholder review panels evaluate contested cases based on evidence and community standards.