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AC meme-coin-audit

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, and real exploit examples from 2024-2025. Use for any token audit, rug pull assessment, meme coin security review, or pre-investment due diligence.

elementalsouls/Claude-BugHunter Agent Skills author: elementalsouls 1 file body ≈ 3 579 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Meme coin and token security audit — rug pull detection (honeypot, hidden mint, fee manipulation, LP lock bypass), Solana SPL token analysis (freeze…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
95
Quality 40%
83
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token SKILL.md:176
      High-entropy token-like string (may be an id, hash or a credential)
      address constant ROUTER = 0x7a…88D;
    • low Secrets in code secret-high-entropy-token SKILL.md:177
      High-entropy token-like string (may be an id, hash or a credential)
      address constant WETH = 0xC0…Cc2;
    • low Risky intent intent-offensive-security SKILL.md:280
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **`triage-validation`** — When deciding if a rug-pull finding qualifies as a bug bounty submission. Workflow primitive: many "rug vector" observations are pre-rug warnings, not exploitable bugs in a
    • low Risky intent intent-offensive-security SKILL.md:283
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
      - **`bb-methodology`** — When confirming engagement mode. Workflow primitive: PART 0 separates "pre-investment due diligence" (this skill's primary use) from "Immunefi bug bounty submission" (differen
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:300
      High-entropy token-like string (may be an id, hash or a credential)
      spl-token display <MINT_ADDRESS> --program-id Toke…uEb

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "sources"

    Process rating: all ten parameters 51/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 13 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 43 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3579 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 541: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (13 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.