The problem ClawMon solves

There are over 9,000 skills on ClawHub, the go-to learning hub for agents, but 1 in 9 skills either leak credentials or are confirmed malicious. The #1 most-downloaded skill is malware. Meanwhile, 100,000+ repo stars from OpenClaw and 35,000+ ERC-8004-registered agents mean the machine-to-machine economy is already moving at production speed (with a trust-layer that's more fitting for a Compaq Presario than a malicious agent swarm from the likes of Lazarus Group).

This is not a future problem. Just take a quick read of "How a Malicious Google Skill on ClawHub Tricks Users Into Installing Malware" (Snyk, Feb 26').

Agents are calling other agents' skills, paying each other via x402 micropayments, and making autonomous decisions without a human in the loop. We are giving them wallets. Traditional reputation systems (stars, reviews, thumbs up) have a game-theoretic breakpoint: the moment a steal opportunity exceeds accumulated reputation value, every rational agent defects. The more valuable the economy gets, the more rational it becomes to cheat.

And cheating is already organized. Sybil clusters, coordinated networks of fake identities inflating skill ratings, are invisible to ClawHub's current infrastructure. ClawMon's sybil detection layer catches them across eight detection vectors: mutual feedback graphs (A rates B, B rates A), velocity bursts, temporal decay for reputation laundering, submitter age weighting, coordinated new-address surges, SybilRank power iteration (Yu et al. 2008) that isolates addresses unreachable from trusted seeds, Jaccard similarity for coordinated review campaigns, and temporal correlation for bot networks submitting in machine-regular lockstep. The results are visualized live as a D3 network graph showing cluster relationships in real time.

The emerging ERC-8004 standard opens the door to solving this. On-chain identity and cryptographic feedback authorization give agents a verifiable behavior history, but the standard alone isn't enough. There needs to be an economic enforcement layer that makes cheating financially irrational, not just socially discouraged. That's the gap ClawMon fills.

Challenges I ran into

ClawHub has no pagination API, and rate-limits very aggressively. Also, ClawHub's CLI doesn't support cursor-based pagination, so pulling the full registry required a multi-phase strategy: first exhausting 6 sort orders at 200 skills each, then running 35 category-specific searches to catch skills the sort orders missed. Even then, ClawHub rate-limits after ~15 rapid calls. We built an incremental sync engine with exponential backoff and a layer that merges results across all phases, running background enrichment in batches of 300 every 5 minutes. The sync now runs every 6 hours and reliably indexes 2,300+ unique skills per cycle.

Use of AI tools and agents

ClawMon exists because of the agent economy and was built with agents.

How AI agents operate within the system:
ClawMon is infrastructure for AI agents. In the MCP ecosystem, agents autonomously discover, invoke, and pay for skills published by other agents, no human in the loop. ClawMon sits at the decision point: before an agent calls a skill, it queries ClawMon's trust API to get a hardened trust score, staking status, and TEE attestation.

That score determines whether the agent gets full access, throttled access, or is denied entirely. The entire feedback loop, skill invocation, x402 micropayment, cryptographic feedback submission, score recalculation, is machine-to-machine. Agents are both the producers and consumers of trust.

ERC-8004 is the on-chain identity layer that makes this work. Every agent registers with a wallet address and opts into open feedback authorization, meaning any agent that interacts with a skill can submit signed, on-chain feedback. ClawMon reads these registries cross-chain and folds the feedback into its scoring engine. The result is a trust graph where agents collectively police each other's behavior with real money (staked MON), enforcing honesty.

How AI tools were used to build it:
ClawMon was built entirely in Cursor with Claude as a pair-programming agent across every layer of the stack (Solidity smart contracts, TypeScript backend, React dashboard, and D3 network visualizations).

The agent was used for:

• Architecture design: working through the three-tier trust model, staking economics, and attack vector analysis in plan mode before writing code

• Smart contract development: drafting and iterating on TrustStaking, InsurancePool, AttestationRegistry, SkillPaywall, and Governance contracts, including edge cases around slash distribution math and unbonding timers

• Scoring engine logic: designing the naive-to-hardened scoring pipeline, sybil detection via graph analysis, temporal decay functions, and credibility-weighted feedback

• Attack simulation: building four attack modules (sybil farming, reputation laundering, attestation poisoning, trust arbitrage) that stress-test the system live on stage

• Frontend development: the evidence-board-themed dashboard, real-time WebSocket integration, D3 force-directed trust network graph, and staking/governance/insurance panels

• ClawHub sync engine: reverse-engineering ClawHub's CLI to build a multi-phase ingestion pipeline that indexes 2,300+ skills despite no pagination support and aggressive rate limits

The agent wasn't used to generate boilerplate and walk away. It was a collaborator through the full lifecycle: architecture decisions, implementation, debugging, and iteration. Every commit in the repo reflects that workflow.