sybil-detection

sybil-detection is a skill for Claude Code from agiprolabs/claude-trading-skills. It costs 24 tokens per session (2,640 once invoked), scanned A, original, MIT.

An analysis method for finding groups of Solana wallets that may be controlled by one entity and for identifying manufactured token activity. It examines funding links, coordinated trades, bundled transactions, wash trading, and inflated holder counts.

In plain words
What is it for?
Use it to investigate coordinated wallet clusters, fake trading volume, artificial holder growth, bundled launches, and possible creator-led distribution or rug-pull preparation.
Why use it?
Token metrics can look healthy when a small number of actors control many wallets or trade with themselves. This helps separate organic demand from activity created to mislead observers.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the trading-skills plugin — 68 skills shipped together

Good fit Use it to investigate coordinated wallet clusters, fake trading volume, artificial holder growth, bundled launches, and possible creator-led distribution or rug-pull preparation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/agiprolabs/claude-trading-skills/sybil-detection
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add agiprolabs/claude-trading-skills --skill sybil-detection
Clone the repo
git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills

Made for: Claude Code.

Or install trading-skills, the plugin that ships this one along with the rest of its 68 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for sybil-detection

README.md
[![agentmods](https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/sybil-detection/github.svg)](https://agentmods.dev/skills/agiprolabs/claude-trading-skills/sybil-detection)
Your own site
<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/sybil-detection"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/sybil-detection/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for sybil-detection

Your own site · 80×15
<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/sybil-detection"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/sybil-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,640 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Data Exfiltration · line 33
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00024 $0.02640
Opus 5 $0.00012 $0.01320
Sonnet 5 $0.00005 $0.00528
Haiku 4.5 $0.00002 $0.00264

Measured 12d ago against content hash 549548699ec6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

sybil-detection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/detect_sybils.py, scripts/funding_tracer.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/sybil-detection/SKILL.md · 243 lines

How it starts

The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Sybil Detection — Coordinated Wallet & Fake Activity Analysis

Sybil attacks in Solana token markets involve a single entity operating many wallets to create the illusion of organic activity. This skill covers detecting coordinated wallet clusters, wash trading, bundled transactions, and fake holder inflation — critical for evaluating whether a token's metrics reflect real demand or manufactured signals.

Why Sybil Detection Matters

Token markets on Solana are rife with manufactured signals:

  • Inflated holder counts: 500 "holders" that are really 10 entities with 50 wallets each
  • Fake volume: Wash trading between self-controlled wallets to simulate demand
  • Artificial social proof: Many wallets holding small amounts to appear broadly distributed
  • Rug preparation: Creator distributes supply across many wallets, then sells coordinated
  • Bundled launches: PumpFun tokens where creator buys via Jito bundle in first slot

A token showing 1,000 holders with 80% funded from 3 wallets is fundamentally different from one with 1,000 independently-funded holders. Sybil detection separates real demand from theater.

Detection Categories

1. Funding Source Analysis

Trace each holder wallet back 1-2 hops to find who sent them SOL:

import httpx

def trace_funding_source(wallet: str, api_key: str, max_hops: int = 2) -> list[str]:
    """Trace SOL funding sources for a wallet via Helius parsed transactions."""
    url = f"https://api.helius.xyz/v0/addresses/{wallet}/transactions"
    resp = httpx.get(url, params={"api-key": api_key, "type": "TRANSFER", "limit": 50})
    transfers = resp.json()

    funders = []
    for tx in transfers:
        for transfer in tx.get("nativeTransfers", []):
            if transfer["toUserAccount"] == wallet and transfer["amount"] > 0.001 * 1e9:
                funders.append(transfer["fromUserAccount"])
    return funders

Key signals:

  • 3+ holder wallets funded from the same source = cluster
  • Funding within 24h of token creation = high suspicion
  • Funding amounts are identical (e.g., 0.05 SOL to each) = automated distribution

Read the full file on GitHub · 243 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 243 lines · 24 tokens per session scan A 549548699ec6

Subscribe to this mod's changes

sybil-detection is a skill published in the GitHub repository agiprolabs/claude-trading-skills (356 stars, last pushed 9d ago), licensed MIT. It adds 24 tokens to every session and 2,640 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.