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.
npx agentmods add skills/nirholas/three.ws/rug-pull-detectionnpx skills add nirholas/three.ws --skill rug-pull-detectiongit clone --depth 1 https://github.com/nirholas/three.wsWrote 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.
[](https://agentmods.dev/skills/nirholas/three.ws/rug-pull-detection)<a href="https://agentmods.dev/skills/nirholas/three.ws/rug-pull-detection"><img src="https://agentmods.dev/badge/skills/nirholas/three.ws/rug-pull-detection.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.01194 |
| Opus 5 | $0.00017 | $0.00597 |
| Sonnet 5 | $0.00007 | $0.00239 |
| Haiku 4.5 | $0.00003 | $0.00119 |
Grade A, and why
rug-pull-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 2d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rug Pull Detection
When to use this skill
Use when the user asks about:
- Whether a new token or project might be a scam
- Red flags to look for in a new project
- Evaluating the legitimacy of a low-cap or new token
- Checking if liquidity is locked
- Assessing contract ownership risks
Detection Framework
1. Contract Code Red Flags
Analyze the smart contract for dangerous functions:
| Red Flag | What It Means | Severity |
|---|---|---|
| Unverified source code | Cannot review what the contract does | Critical |
| Owner can mint unlimited tokens | Unlimited dilution or sell pressure | Critical |
| Hidden fees on transfer | Tax can be set to 99% trapping funds | Critical |
| Blacklist function | Owner can prevent specific wallets from selling | High |
| Proxy with no timelock | Owner can swap contract logic instantly | High |
| Whitelisted trading | Only approved wallets can sell | Critical |
| Max transaction bypassed for owner | Owner can dump while others are limited | High |
| Hardcoded router/pair addresses | Legitimate but check for hidden logic | Medium |
Specific code patterns to check:
onlyOwnerfunctions that modify fees, max transaction, or trading status- Functions that can disable selling or set transfer tax above 10%
- Hidden
transferoverrides that apply different rules to different addresses - Self-destruct or selfdestruct capability
2. Liquidity Analysis
Evaluate the safety of the trading pool:
- Liquidity locked: Is LP locked via a reputable locker (Unicrypt, Team.Finance, PinkLock)?
- Lock duration: Minimum 6 months for moderate trust, 12+ months for higher trust
- Lock amount: What percentage of total LP is locked? Should be >80%
- LP token holder: If LP is not locked, who holds it? Single wallet = high risk
- Liquidity depth: Very thin liquidity relative to market cap means easy manipulation
- Honeypot check: Can you actually sell the token? Test with small amounts
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.
- 2d ago First seen · 112 lines · 35 tokens per session scan A 0a7b02b6cc19
rug-pull-detection is a skill published in the GitHub repository nirholas/three.ws (110 stars, last pushed today), licensed Apache-2.0. It adds 35 tokens to every session and 1,194 once invoked, about $0.0002 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 2026-09-03.
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