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AC shipmytoken

Launch Solana tokens on Pumpfun, manage fee sharing, claim earnings, and track portfolio. All via chat.

modbender/skill-library-mcp Agent Skills author: modbender MIT 10 files · 1 script body ≈ 3 716 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Launch Solana tokens on Pumpfun, manage fee sharing, claim earnings, and track portfolio.

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions

ProcedureTelegramInfrastructureAI and agentsWriting and documentstype 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
90/100
safety, quality, tests
Safety 60%
98
Quality 40%
78
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Tools and files w 18
60
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 src/fees.mjs:12
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      const SHIPMYTOKEN_WALLET = new PublicKey("7Z9v…a6T");
      quoted
    • low Secrets in code secret-high-entropy-token src/launch.mjs:18
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      const SHIPMYTOKEN_WALLET = new PublicKey("7Z9v…a6T");
      quoted

    Files scanned: 10. 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 62/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 73 steps, 2 vague phrases
    • 100Failures and branches. 11 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3716 tokens
    • 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 14 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)
    • +3Description length 103: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 73 items
    • +4Has examples (16 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.