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Launch and trade AI agent tokens on ClawLaunch bonding curve (Base). Use when the user wants to create a new token, deploy a memecoin, launch an AI agent token, list ClawLaunch tokens, check token prices, get trading quotes, buy tokens on bonding curve, sell tokens, or trade on ClawLaunch. Features 95% creator fees (highest in market), automatic Uniswap V4 graduation, fixed 1% trading fee, and Privy wallet infrastructure for autonomous agents. Supports Base Mainnet and Base Sepolia testnet.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 7 files · 1 script body ≈ 5 710 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Launch and trade AI agent tokens on ClawLaunch bonding curve (Base).

As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
94
Quality 40%
81
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 6

✓ No critical or high findings

Medium and low: 6
  • low Secrets in code secret-high-entropy-token references/api-docs.md:250
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    address: '0xEC…d1A',  // v5 Factory
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:730
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | AgentRegistry | `0x7a…683` |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:731
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | LPLocker | `0xf8…121` |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:732
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | TokenDeployer | `0x0A…387` |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:733
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | AgentLaunchFactory | `0xb3…c6b` |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:734
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | ClawBridge | `0x56…068` |
    table

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5710 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 13 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 5710 tokens
  • 100Steps. 86 steps
  • 100Consistency. Name and required fields are in place
  • low 16 top-level sections: this looks like several domains in one skill

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 495: enough signal without eating the budget
  • +4Structure: 42 headings
  • +3Step-by-step instructions: 86 items
  • +4Has examples (24 code blocks)
  • +4Reference files are cited in the instructions (1 of 4)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.