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AC tokenized-agents

Build payment flows for Pump Tokenized Agents using @pump-fun/agent-payments-sdk. Use when accepting payments, building accept-payment transactions, integrating Solana wallets, or verifying that a user has paid an invoice on-chain.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 4 files body ≈ 4 496 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Build payment flows for Pump Tokenized Agents using @pump-fun/agent-payments-sdk.

As a process C 64/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

IntegrationAI and agentstype 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
A
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
88
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Consistency w 8
40
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token SKILL.md:56
      High-entropy token-like string (may be an id, hash or a credential)
      # USDC: EPjF…t1v
    • low Secrets in code secret-high-entropy-token SKILL.md:57
      High-entropy token-like string (may be an id, hash or a credential)
      # SOL (wrapped): So11…112

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 64/100

    • 0Result and completion. Does not say what the result is
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (tokenized-agents) differs from the folder (pump-fun-tokenized-agents)
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4496 tokens
    • 85Steps. 32 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100Failures and branches. 1 branches, has a failure section
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 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 231: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (14 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.