According to AlphaMoat, as of 2026-09, ERC-8004 Trustless Agents has 3,543 downloads and 0 stars — ranked #3,099 of 5,078 AI skills overall, and #629 of 969 in AI Agent.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 115 | 3.5K | - | - |
| SkillHub ↗ | 114 | 3.5K | - | — |
Platforms count differently and update at different times.
The skill can register agents, query registry data, update URIs, and submit or receive reputation feedback. Some commands submit real on-chain transactions; test on a test network, verify the target network and contract addresses, and obtain user confirmation before any mainnet transaction. Avoid private keys in command arguments and review uploaded metadata and Pinata credentials.
Profile published 2026-10-29
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 3.5K | — | - |
| 2026-08 | 3.4K | +2.5% | - |
| 2026-07 | 3.3K | +7.5% | - |
| 2026-06 | 3.0K | +4.0% | - |
| 2026-05 | 2.9K | — | - |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: erc-8004 description: ERC-8004 Trustless Agents - Register, discover, and build reputation for AI agents on Ethereum. Use when registering agents on-chain, querying agent registries, giving/receiving reputation feedback, or interacting with the AI agent trust layer. --- # ERC-8004: Trustless Agents On-chain identity, reputation, and validation for autonomous agents. **Now live on Ethereum Mainnet!** ## Overview
ERC-8004 provides three registries:
- **Identity Registry** - ERC-721 agent identities with registration metadata
- **Reputation Registry** - Signed feedback scores between agents/clients
- **Validation Registry** - Independent verification (zkML, TEE, stakers)
## Quick Reference
### Register Agent
```bash
./scripts/register.sh --uri "ipfs://..." --network mainnet
./scripts/register.sh --network sepolia # Testnet (no URI, set later)
```
### Query Agents
```bash
./scripts/query.sh total --network mainnet # Total registered
./scripts/query.sh agent 1 --network mainnet # Agent details
./scripts/query.sh reputation 1 # Reputation summary
```
### Update Agent
```bash
./scripts/set-uri.sh --agent-id 1 --uri "ipfs://newHash" --network mainnet
```
### Give Feedback
```bash
./scripts/feedback.sh --agent-id 1 --score 85 --tag1 "quality"
./scripts/feedback.sh --agent-id 1 --score 9977 --decimals 2 --tag1 "uptime"
```
## Networks
| Network | Status | Identity Registry | Reputation Registry |
|---------|--------|-------------------|---------------------|
| **Mainnet** | Live | `0x8004A169FB4a3325136EB29fA0ceB6D2e539a432` | `0x8004BAa17C55a88189AE136b182e5fdA19dE9b63` |
| Sepolia | Live | `0x8004A818BFB912233c491871b3d84c89A494BD9e` | `0x8004B663056A597Dffe9eCcC1965A193B7388713` |
| Base | Coming | TBD | TBD |
| Arbitrum | Coming | TBD | TBD |
| Optimism | Coming | TBD | TBD |
Contract addresses in `lib/contracts.json`.
## Registration File Format
```json
{
"type": "https://eips.ethereum.org/EIPS/eip-8004#registration-v1",
"name": "your-agent-name",
"description": "Agent description...",
"image": "ipfs://...",
"services": [
{ "name": "A2A", "endpoint": "https://agent.example/.well-known/agent-card.json", "version": "0.3.0" },
{ "name": "MCP", "endpoint": "https://mcp.agent.eth/", "version": "2025-06-18" },
{ "name": "ENS", "endpoint": "yourname.eth" }
],
"registrations": [
{ "agentRegistry": "eip155:1:0x8004A169FB4a3325136EB29fA0ceB6D2e539a432", "agentId": "1" }
],
"supportedTrust": ["reputation", "crypto-economic", "tee-attestation"]
}
```
Template at `templates/registration.json`.
## Reputation Scores
The reputation system uses signed fixed-point numbers (`value` + `valueDecimals`):
| Tag | Meaning | Example | value | decimals |
|-----|---------|---------|-------|----------|
| starred | Quality (0-100) | 87/100 | 87 | 0 |
| uptime | Uptime % | 99.77% | 9977 | 2 |
| tradingYield | Yield % | -3.2% | -32 | 1 |
| responseTime | Latency ms | 560ms | 560 | 0 |
## Trust Models
ERC-8004 supports three pluggable trust models:
- **Reputation-based** - Client feedback with scores, tags, and metadata
- **Crypto-economic** - Stake-secured validation with economic incentives
- **Crypto-verification** - TEE attestations and zkML proofs
## Dependencies
- `cast` (Foundry) - `curl -L https://foundry.paradigm.xyz | bash`
- `jq` - `brew install jq`
- Private key in `~/.clawdbot/wallets/.deployer_pk` or `PRIVATE_KEY` env
- IPFS: Set `PINATA_JWT` for uploads, or upload manually
## Resources
### Official
- [EIP-8004 Spec](https://eips.ethereum.org/EIPS/eip-8004) - Full specification
- [8004.org](https://8004.org) - Official website
- [Reference Implementation](https://github.com/erc-8004/erc-8004-contracts) - Contract source
- [Telegram Community](https://t.me/ERC8004) - Builder chat
- [Builder Program](http://bit.ly/8004builderprogram) - Join the ecosystem
### SDKs & Tools
- [ChaosChain SDK](https://github.com/ChaosChain/chaoschain/tree/main/packages/sdk) - JS/TS SDK
- [erc-8004-js](https://github.com/tetratorus/erc-8004-js) - Lightweight JS library
- [erc-8004-py](https://github.com/tetratorus/erc-8004-py) - Python implementation
- [Vistara Example](https://github.com/vistara-apps/erc-8004-example) - Full demo with AI agents
### Ecosystem
- [Awesome ERC-8004](https://github.com/sudeepb02/awesome-erc8004) - Curated resource list
- [A2A Protocol](https://a2a-protocol.org/) - Agent-to-Agent protocol ERC-8004 extends
- [Ethereum Magicians Discussion](https://ethereum-magicians.org/t/erc-8004-trustless-agents/25098)
## Genesis Month (February 2026)
ERC-8004 launched on mainnet January 29, 2026. February is "Genesis Month" - showcasing teams building the agentic economy. Get involved!
Records learnings, errors and corrections as Markdown files for later reuse and promotion into workspace memory.
Finds skills in local and remote catalogs from a plain-language request and recommends what to install.
A browser-automation command line skill for AI agents.
Turns corrections and self-review into tiered memory and promotes or archives it by use.
Reviews a third-party skill's source, permissions, and danger patterns against a checklist before it is installed.
Stores agent memory and cross-skill state as a typed, constraint-checked knowledge graph.
A workspace setup guide for planning proactive follow-up, working memory and review routines for an AI agent.
A guide to creating and updating effective Claude skills.
Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.