Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Analyzes social trust signals in agent marketplaces to identify when a skill's reputation is built on coordinated manipulation rather than genuine community validation. Detects sockpuppet networks, burst voting patterns, and manufactured engagement that inflate a skill's apparent trustworthiness.
Marketplace reputation systems treat all upvotes equally; this tool analyzes behavioral fingerprints and statistical anomalies that distinguish manufactured consensus from earned trust.
When to use it
- Auditing a highly-upvoted skill before installing it on a production agent
- Investigating a publisher whose skills all launched with sudden mass popularity
- Checking whether a marketplace's top-rated skills reflect real independent opinions
- Flagging suspicious engagement before recommending a skill to a team
- Identifying mutual support networks spread across multiple publisher accounts
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Assess social trust integrity for the `ai-assistant-toolkit` publisher
- 1Collect engagement velocity data for all skills published under the account
- 2Analyze account creation dates and cross-voting patterns among early upvoters
- 3Compare each skill's upvote-to-install ratio against organic baselines for similar skills
- 4Detect cross-publisher coordination by checking upvoter overlap with other publisher networks
- 5Assess top reviews for linguistic diversity and specificity versus template-generated phrasing
Manipulation verdict MANUFACTURED: all four skills show coordinated burst voting, sockpuppet cohort fingerprints, upvote-to-install ratios 4-18x above organic baseline, and a mutual support network with two other publishers