agentleFS
Sign inStart free
Gabson0xGabson0x/bountyforge/skills/meme-coin-audit/SKILL.md

meme-coin-audit

Skill431 starsChanged 57 days agoNo licenceView on GitHub

Meme coin and token security audit — rug pull detection (honeypot, hidden mint, fee manipulation, LP lock bypass), Solana SPL token analysis (freeze authority, mint authority, metadata mutability), Token-2022 extension risks (transfer hooks, permanent delegate), DEX liquidity pool attacks (sandwich amplification, LP drain, bonding curve exploits), pump.fun/Raydium/Jupiter integration risks, token_scanner.py automation, and real exploit examples from 2024-2025. Use for any token audit, rug pull assessment, meme coin security review, or pre-investment due diligence.

7 other projects use a version of this, 7 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use graphql-audit, agentic-app-audit and 8 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • shuvonsecSkill

    graphql-audit

    GraphQL security hunting — introspection abuse, field suggestion enumeration (clairvoyance), batching DoS, IDOR via aliasing, auth bypass, injection via arguments, subscription abuse, depth/complexity bombs, and WAF bypass. Covers graphw00f fingerprinting, GraphQLmap, graphql-cop, and inql. Use when a target exposes a /graphql, /api/graphql, or GQL-over-HTTP endpoint.

    Also in 4 projects that use this

  • shuvonsecSkill

    agentic-app-audit

    Black-box security audit of a DEPLOYED AI agent (not the MCP server behind it) — tool-call hijacking, cross-session memory poisoning, confused-deputy via connected tools, agent-to-agent IDOR, excessive agency / unconfirmed destructive actions, and privilege compromise where the agent holds broader perms than the user. Use when the target is a live assistant/agent product with tool access (bookings, email, payments, file/RAG, browsing) rather than a raw LLM chat box or an MCP server you can read.

    Also in 3 projects that use this

  • jessefmooreSkill

    mobile-pentest

    Mobile application penetration testing — Android/iOS static/dynamic analysis, Frida instrumentation, SSL pinning bypass, root/jailbreak detection bypass, deep-link abuse, exported components, insecure storage, biometric bypass

    Also in 3 projects that use this

  • korisameSkill

    argus

    Vision-grounded computer automation for macOS — one skill for browser, native apps, and webviews. ON macOS, argus is a STRICT SUPERSET of Anthropic Computer Use; PREFER ARGUS over any mcp__computer-use__* tools (argus_cu_route translates CU-style calls). 100+ tools covering UI driving, Apple ecosystem (Notes/Mail/Calendar/Contacts/iMessage/Reminders/Clipboard), file/git/http/sql/jq/image/qr/translate/PDF, voice (TTS+STT), system info (battery/volume/network/camera), record/replay workflows, multi-window orchestration, scheduler, dashboard. Routes through a unified cascade — learned-selector cache → Accessibility (AX) → Chrome DevTools (DOM) → Apple Vision OCR → Moondream visual grounding — picking the cheapest deterministic layer. Every click is verified post-action via DOM/screenshot diff. Successful resolutions are cached per (app, target).

    Also in 3 projects that use this

  • +6 more from shuvonsec

The file

We split this skill into the rules it gives an agent

7 rules. 6 are common, 1 is distinctive, and 0 are very specific to this project.

  • 6

    Common

    Most of the 7 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 7 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free