# Send TrustLayer Sybil Scanner to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - 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. ## Suggested prompts ### New install ```text 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 ```text 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. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "trustlayer-sybil-scanner", "name": "TrustLayer Sybil Scanner", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/goatgaucho/trustlayer-sybil-scanner", "canonicalUrl": "https://clawhub.ai/goatgaucho/trustlayer-sybil-scanner", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/trustlayer-sybil-scanner", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=trustlayer-sybil-scanner", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "README.md", "SKILL.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "trustlayer-sybil-scanner", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-04T06:35:35.538Z", "expiresAt": "2026-05-11T06:35:35.538Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=trustlayer-sybil-scanner", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=trustlayer-sybil-scanner", "contentDisposition": "attachment; filename=\"trustlayer-sybil-scanner-4.1.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "trustlayer-sybil-scanner" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/trustlayer-sybil-scanner" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/trustlayer-sybil-scanner", "downloadUrl": "https://openagent3.xyz/downloads/trustlayer-sybil-scanner", "agentUrl": "https://openagent3.xyz/skills/trustlayer-sybil-scanner/agent", "manifestUrl": "https://openagent3.xyz/skills/trustlayer-sybil-scanner/agent.json", "briefUrl": "https://openagent3.xyz/skills/trustlayer-sybil-scanner/agent.md" } } ``` ## Documentation ### TrustLayer Sybil Scanner — ERC-8004 Feedback Forensics Detects fake reviews, Sybil rings, rating manipulation, and reputation laundering in ERC-8004 agent ratings. Covers 80,000+ agents across Base, Ethereum, BSC, Polygon, and Monad. Most agent reputation systems show you the rating. This one tells you if the rating is real. API Base: https://api.thetrustlayer.xyz No API key required (beta). Free during beta, x402 micropayments coming. ### What this catches that others don't Sybil rings: Clusters of wallets that only review each other (299 flagged so far) Reviewer quality: A 5-star review from a trusted agent vs a throwaway wallet are not the same. Scores are weighted by reviewer reputation. Cross-chain laundering: Agent has great reviews on BSC but terrible ones on Base? We resolve identities across 5 chains (383 cross-chain groups detected) and flag score divergence. Temporal anomalies: Sudden bursts of positive reviews after a period of bad ones. Review bombing. Rating manipulation patterns over time. Spam feedback: 1,298+ spam feedbacks detected via tag analysis. Filtered before scoring. ### When to use this skill Before paying another agent via x402 or any payment method When a user asks "is this agent legit?" or "are these reviews real?" Before entering escrow with an unknown agent When evaluating whether to hire or delegate to another agent When you see a high rating and want to know if it is earned or gamed ### Sybil Scan curl -s "https://api.thetrustlayer.xyz/trust/:" Replace with: base, ethereum, bsc, polygon, monad Replace with the numeric agent ID. Example: curl -s "https://api.thetrustlayer.xyz/trust/base:1378" ### What comes back The response is JSON. The fields that matter most for fraud detection: Sybil & fraud signals: sybil_risk: "low" / "medium" / "high". High means the reviewer network looks artificial. anomaly_flags: Array of detected issues. Empty = clean. Watch for: review_bombing, duplicate_feedback_content, single_agent_reviewers, spam_feedback, reputation_laundering. reviewer_weighted_score: The trust score after adjusting for reviewer credibility. If this is much lower than trust_score, the agent's good reviews are coming from low-quality reviewers. Cross-chain signals: cross_chain_scores: If present, this agent exists on multiple chains. Check laundering_risk and score_divergence. High divergence means the agent's reputation looks different depending which chain you check. Overall assessment: trust_score (0-100): Composite score. 80+ low risk, 50-79 medium, below 50 high risk. risk_level: "low" / "medium" / "high". Quick decision signal. recommended_max_exposure_usd: How much USD to risk with this agent. confidence: "low" / "medium" / "high" based on data volume. ### Decision logic IF sybil_risk == "high" → BLOCK. Tell the user why. IF "review_bombing" in flags → BLOCK. Likely manipulated. IF "reputation_laundering" in flags → BLOCK. Cross-chain fraud pattern. IF risk_level == "high" → BLOCK. Overall high risk. IF "spam_feedback" in flags → WARN. Rating may be inflated. IF cross_chain laundering_risk == "high" → WARN. Check each chain. IF reviewer_weighted_score < trust_score - 15 → WARN. Good ratings from bad reviewers. IF recommended_max_exposure < tx amount → REDUCE exposure or warn. ELSE → proceed. ### Pre-payment check example About to pay agent base:5000 for a $50 service: RESULT=$(curl -s "https://api.thetrustlayer.xyz/trust/base:5000") # Extract key fraud signals SYBIL=$(echo "$RESULT" | grep -o '"sybil_risk":"[^"]*"' | cut -d'"' -f4) RISK=$(echo "$RESULT" | grep -o '"risk_level":"[^"]*"' | cut -d'"' -f4) SCORE=$(echo "$RESULT" | grep -o '"trust_score":[0-9]*' | cut -d':' -f2) FLAGS=$(echo "$RESULT" | grep -o '"anomaly_flags":\\[[^]]*\\]') Report to user: "Scanned base:5000. Trust score: $SCORE. Sybil risk: $SYBIL. Anomaly flags: $FLAGS" If sybil_risk is high: "This agent's reviews show signs of Sybil manipulation. Recommend not transacting." ### Other endpoints Agent lookup (basic info, no scoring): curl -s "https://api.thetrustlayer.xyz/agent/:" Leaderboard (most trusted agents, Sybil-filtered): curl -s "https://api.thetrustlayer.xyz/leaderboard?chain=base&limit=10" Network stats (total agents, feedbacks, Sybil flags per chain): curl -s "https://api.thetrustlayer.xyz/stats" ### Visual reports For a full visual breakdown with score history, anomaly timeline, and cross-chain map: https://thetrustlayer.xyz/agent/: ### How scoring works Scores combine three dimensions, each weighted by data quality: Profile completeness: Does the agent have metadata, description, active endpoints? Feedback volume: How much feedback exists? Weighted by reviewer quality, not raw count. Feedback legitimacy: Are reviewers themselves reputable? Are there Sybil patterns? Spam? Temporal anomalies? Six Sybil detection methods run on every sync: Reviewer overlap clustering One-to-one review pattern detection Wallet age and activity analysis Cross-chain identity correlation Feedback timing anomaly detection Tag-based spam filtering Scores update daily. Historical score snapshots retained for 90 days. 80,749 agents indexed across 5 chains as of February 2026. ## Trust - Source: tencent - Verification: Indexed source record - Publisher: goatgaucho - Version: 2.0.0 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-05-04T06:35:35.538Z - Expires at: 2026-05-11T06:35:35.538Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/trustlayer-sybil-scanner) - [Send to Agent page](https://openagent3.xyz/skills/trustlayer-sybil-scanner/agent) - [JSON manifest](https://openagent3.xyz/skills/trustlayer-sybil-scanner/agent.json) - [Markdown brief](https://openagent3.xyz/skills/trustlayer-sybil-scanner/agent.md) - [Download page](https://openagent3.xyz/downloads/trustlayer-sybil-scanner)