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BC Optionns 🎯

Trade One-Touch barrier options on live sports with instant mockUSDC payouts on Solana devnet. Built for agents who never sleep.

modbender/skill-library-mcp Agent Skills author: modbender MIT 9 files · 1 script body ≈ 3 899 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Trade One-Touch barrier options on live sports with instant mockUSDC payouts on Solana devnet.

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

IntegrationAI and agentsSoftware developmenttype 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
82/100
safety, quality, tests
Safety 60%
88
Quality 40%
72
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Consistency w 8
40
When it triggers w 12
50
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

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 · 8

✓ No critical or high findings

Medium and low: 8
  • medium Exfiltration net-redirectable-api-key scripts/strategy.py:25
    Helper sends the API key to a host configured by an environment variable — the key can be redirected to another server
    API key + configurable base URL from environment
  • low Secrets in code secret-high-entropy-token references/api.md:221
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "blockhash": "FwRY…LFC",
    detector
  • low Secrets in code secret-high-entropy-token references/api.md:432
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    **Program ID:** `7kHC…MSn`
    quoted
  • low Secrets in code secret-high-entropy-token scripts/optionns.sh:269
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    optnUSDC_MINT="DNaY…gJh"
    quoted
  • low Exfiltration net-credential-use scripts/optionns.sh:586
    Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host; quoted — discussed, not commanded)
    RPC_URL="${SOLANA_RPC_URL:-${CRED_RPC:-https://api.devnet.solana.com}}"
    vendor-hostquoted
  • low Secrets in code secret-high-entropy-token scripts/strategy.py:57
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    PROGRAM_ID = '7kHC…MSn'
    quoted
  • low Secrets in code secret-high-entropy-token scripts/strategy.py:58
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    TOKEN_PROGRAM_ID = 'Toke…5DA'
    quoted
  • low Secrets in code secret-high-entropy-token skill.json:113
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "program_id": "7kHC…MSn",
    quoted

Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 62/100

  • 0Result and completion. Does not say what the result is
  • 40Consistency. Frontmatter name (Optionns 🎯) differs from the folder (sports)
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 55 steps, 2 vague phrases
  • 100Failures and branches. 1 branches, has a failure section
  • 100Execution cost. Instruction body is 3899 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 17 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
  • -212 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 128: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 55 items
  • +4Has examples (14 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.