Tencent SkillHub · AI

Agent Trust Protocol

Manage and update agent trust scores with Bayesian updates, domain-specific trust, revocation, forgetting, and visualize trust via dashboard.

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High Signal

Manage and update agent trust scores with Bayesian updates, domain-specific trust, revocation, forgetting, and visualize trust via dashboard.

⬇ 0 downloads ★ 0 stars Unverified but indexed

Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: serve_dashboard.py, dashboard.html, README.md, atp.py, package.json, moltbook_trust.py

Validation

Use the Yavira download entry.
Review SKILL.md after the package is downloaded.
Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

Download the package from Yavira.
Extract it into a folder your agent can access.
Paste one of the prompts below and point your agent at the extracted folder.

New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 2.0.1

Provenance

Publisher: FELMONON
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 12 sections Open source page

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

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"

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

3 Scripts1 Docs1 Config1 Files

README.md Docs
atp.py Scripts
moltbook_trust.py Scripts
serve_dashboard.py Scripts
package.json Config
dashboard.html Files