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AB blowfish-launch

Launch tokens on Solana via the Blowfish Agent API (Meteora Dynamic Bonding Curve). Use when the user wants to launch, deploy, or create a token on Solana, check token launch status, list launched tokens, or claim trading fees. Triggers on "launch token", "deploy token", "create token", "blowfish launch", "check launch status", "claim fees".

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 526 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Launch tokens on Solana via the Blowfish Agent API (Meteora Dynamic Bonding Curve).

As a process B 72/100 · Nearly there — weak spots: result and completion, progress reporting

IntegrationInfrastructureAI 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
98/100
safety, quality, tests
Safety 60%
99
Quality 40%
96
Run on models
none yet
Process rating
B
72/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Failures and branches w 10
50
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-password-literal scripts/blowfish-launch.ts:100
      Hard-coded password / key literal (may be an example)
      const secretKey = Uint…rom(JSON.parse(process.env.WALLET_SECRET_KEY!));

    Files scanned: 3. 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 72/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 17 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 526 tokens
    • 100Running it twice. No mutating operations

    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

    • +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
    • +5Description quotes 6 example trigger phrases
    • +3Description length 343: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 17 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.