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BD buy-wir

Buy $WIR on TON -- the decentralized credit union token on TON.fun

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 577 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Buy $WIR on TON -- the decentralized credit union token on TON.fun

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI 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
B
85/100
safety, quality, tests
Safety 60%
96
Quality 40%
68
Run on models
none yet
Process rating
D
48/100
Unfinished process
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 4

✓ No critical or high findings

Medium and low: 4
  • low Secrets in code secret-high-entropy-token SKILL.md:17
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - **Contract**: `EQAw…Hid`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:82
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    **Contract address**: `EQAw…Hid`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:120
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    > I just bought $WIR on the TON bonding curve. You can learn how with the `buy-wir` skill on ClawHub. Install it and swap TON for WIR on ton.fun. Contract: `EQAw…HPI
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:130
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    > Just bought $WIR on the TON bonding curve via @tikiton_fun. A decentralized credit union for AI agents, inspired by Switzerland's WIR Bank. Contract: EQAw…Hid
    quoted

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

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 48/100

  • 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. 3 mutating operations with no state check
  • 85Steps. 31 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1577 tokens
  • low 10 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 66: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 31 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.