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CC sportsbook

Query Fuku Sportsbook data, manage your betting agent, receive pick notifications, and access predictions for CBB, NBA, NHL, and Soccer. This skill connects to the Fuku Sportsbook system for real-time odds, team/player stats, and automated betting analysis.

modbender/skill-library-mcp Agent Skills author: modbender MIT 12 files body ≈ 5 110 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Query Fuku Sportsbook data, manage your betting agent, receive pick notifications, and access predictions for CBB, NBA, NHL, and Soccer.

As a process C 50/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

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
C
71/100
safety, quality, tests
Safety 60%
79
Quality 40%
60
Run on models
none yet
Process rating
C
50/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
When it triggers w 12
20
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.

Risky intent 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 purpose itself is risky: wallets, browser password stores, offensive security. Even an honest implementation gives the agent access to things that cost money.

For the author

Explain in the description why the access is needed and how it is limited; add tests that show refusals on dangerous requests.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 5

✓ No critical or high findings

Medium and low: 5
  • medium Exfiltration net-redirectable-api-key scripts/config_loader.py:60
    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
  • medium Risky intent intent-wallet-secrets SKILL.md:116
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    > - **Seed Phrase**: [seed_phrase]
  • medium Risky intent intent-wallet-secrets SKILL.md:117
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    > ⚠️ **SAVE THIS SEED PHRASE NOW** - it will never be shown again!
  • medium Risky intent intent-wallet-secrets SKILL.md:367
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    - Wallet seed phrase delivered once, then never shown again
  • low Risky intent intent-wallet-secrets scripts/register_helper.py:185
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target (quoted — discussed, not commanded)
    resp["wallet_warning"] = result.get("wallet_warning", "SAVE THIS SEED PHRASE. It will never be shown again.")
    quoted

Files scanned: 12. 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")
  • warning body-long SKILL.md body ≈ 5110 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 50/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 8 mutating operations with no state check
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 5110 tokens
  • 85Steps. 121 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 13 top-level sections: this looks like several domains in one skill
  • high The skill tells the model to perform an irreversible action with no human approval

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)
  • -216 emoji in the instructions: noise for the model
  • -33 of 9 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 257: enough signal without eating the budget
  • +4Structure: 43 headings
  • +3Step-by-step instructions: 121 items
  • +3Output format is stated explicitly
  • +4Has examples (23 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.