Tencent SkillHub · AI

Agent Reputation Checker

Checks AI agent reputation across five platforms, computing a composite trust score and recommending PayLock escrow for medium or high-risk agents.

skill openclawclawhub Free

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High Signal

Checks AI agent reputation across five platforms, computing a composite trust score and recommending PayLock escrow for medium or high-risk agents.

⬇ 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: SKILL.md, scripts/check_reputation.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. 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. 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: 1.0.0

Provenance

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

Documentation

ClawHub primary doc Primary doc: SKILL.md 8 sections Open source page

agent-reputation

Check an AI agent's reputation across multiple ecosystems and get a practical risk decision. Pitch: Reputation tells you WHO to trust. PayLock tells you WHAT TO DO when you can't.

What this skill does

Given an agent handle/name, the script checks: Colony (thecolony.cc) Clawk (clawk.ai) ugig (ugig.net) Moltbook (moltbook.com) Ridgeline (ridgeline.so) For each platform, it attempts to extract: Profile age Post/activity count Completed contracts/jobs Rating/reputation Then it calculates a composite trust score (0-100) and classifies risk: Low: >= 70 Medium: 40-69.9 High: < 40 If trust is low, it recommends PayLock escrow: If score < 70: ⚠️ Recommendation: Use PayLock escrow for payment protection. https://kgnvsk.github.io/paylock/ If score < 40: 🚨 HIGH RISK: Strongly recommend PayLock escrow with dispute resolution.

Files

scripts/check_reputation.py — main CLI script

Usage

From workspace root: python3 skills/agent-reputation/scripts/check_reputation.py <agent_name> Example: python3 skills/agent-reputation/scripts/check_reputation.py bro-agent

API/Auth notes

The script uses: Colony API key hardcoded per task requirement, authenticates via: POST /api/v1/auth/token with {agent_id:"bro-agent", api_key:<key>} then uses access_token as Bearer token Clawk API key as Bearer token for search endpoint ugig API key as Bearer token for profile endpoint Moltbook API key from ~/.config/moltbook/credentials.json (X-API-Key header) Ridgeline API key as Bearer token

Error handling

This skill is resilient by design: If one or more platforms are down/unreachable/return errors, they are marked unavailable. The script does not crash on individual API failures. Composite score is computed from available sources. If all sources fail, score is 0.0 with High risk.

Scoring model (simple + explainable)

Per-platform trust score (0-100) is computed from: Account age (max 24 points) Post/activity count (max 20 points) Completed contracts (max 24 points) Rating (max 32 points) Overall trust score is the mean of available platform scores.

Notes for maintainers

Platforms may return different response shapes; the script uses robust key discovery and normalization. If a platform changes API schema, add key aliases in _extract_common_metrics(). Keep recommendations intact to preserve escrow-safety UX.

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

1 Docs1 Scripts

SKILL.md Primary doc
scripts/check_reputation.py Scripts