trust-protocol
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- License 许可
- MIT
- 最近更新
- 2026-08-23
- Stars
- ★ 1
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- 0
02 资源文件 8
- 参考文档_meta.json288 B
- 脚本atp.py24.2 KB
- 参考文档dashboard.html10.5 KB
- 脚本demo.py13.5 KB
- 脚本moltbook_trust.py6.0 KB
- 参考文档package.json779 B
- 参考文档README.md3.9 KB
- 脚本serve_dashboard.py2.0 KB
03 SKILL.md 正文
Agent Trust Protocol (ATP)
Establish, verify, and maintain trust between AI agents. Bayesian trust scoring with domain-specific trust, revocation, forgetting curves, and a visual dashboard.
Install
git clone https://github.com/FELMONON/trust-protocol.git
# No dependencies beyond Python 3.8+ stdlib
# Pair with skillsign for identity: https://github.com/FELMONON/skillsign
Quick Start
# Add an agent to your trust graph
python3 atp.py trust add alpha --fingerprint "abc123" --score 0.7
# Record interactions — trust evolves via Bayesian updates
python3 atp.py interact alpha positive --note "Delivered clean code"
python3 atp.py interact alpha positive --domain code --note "Tests passing"
# Check trust
python3 atp.py trust score alpha
python3 atp.py trust domains alpha
# View the full graph
python3 atp.py status
python3 atp.py graph export --format json
# Run the full-stack demo (identity → trust → dashboard)
python3 demo.py --serve
Commands
Trust Management
atp.py trust add <agent> --fingerprint <fp> [--domain <d>] [--score <0-1>]
atp.py trust list
atp.py trust score <agent>
atp.py trust remove <agent>
atp.py trust revoke <agent> [--reason <reason>]
atp.py trust restore <agent> [--score <0-1>]
atp.py trust domains <agent>
Interactions
atp.py interact <agent> <positive|negative> [--domain <d>] [--note <note>]
Challenge-Response
atp.py challenge create <agent>
atp.py challenge respond <challenge_file>
atp.py challenge verify <response_file>
Graph
atp.py graph show
atp.py graph path <from> <to>
atp.py graph export [--format json|dot]
atp.py status
Dashboard
python3 serve_dashboard.py # localhost:8420
python3 demo.py --serve # full demo + dashboard
Moltbook Integration
python3 moltbook_trust.py verify <agent> # check agent trust via Moltbook profile
How Trust Works
- Bayesian updates: Each interaction shifts trust scores with diminishing deltas (prevents thrashing)
- Negativity bias: Negative interactions hit harder than positive ones boost
- Domain-specific: Trust an agent for code but not for security advice
- Forgetting curves: Trust decays without interaction (R = e^(-t/S))
- Revocation: Immediate drop to floor, restorable at reduced score
- Transitive trust: If you trust A and A trusts B, you partially trust B (with decay)
Integration with skillsign
ATP builds on skillsign for identity:
- Agents generate ed25519 keypairs with skillsign
- Agents sign skills, others verify signatures
- Verified agents get added to the ATP trust graph
- Interactions update trust scores over time
Triggers
"check trust", "trust score", "trust graph", "verify agent", "agent trust", "trust status", "who do I trust", "trust report"
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