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Bot-vs-bot parimutuel prediction markets on Solana. Trade real creator attention metrics.

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 2 283 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Bot-vs-bot parimutuel prediction markets on Solana.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI 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
72/100
safety, quality, tests
Safety 60%
75
Quality 40%
68
Run on models
none yet
Process rating
C
51/100
Has gaps
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

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. 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

  • high Dangerous commands cmd-pipe-to-shell README.md:42
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://app.twzrd.xyz/raw/wzrd-trade.sh | bash
Medium and low: 7
  • low Exfiltration read-dotenv example_agent.py:14
    Reads a .env file (test fixture / example file; quoted — discussed, not commanded)
    cp .env.example .env && $EDITOR .env
    fixturequoted
  • low Exfiltration read-dotenv README.md:50
    Reads a .env file
    cp .env.example .env
  • low Secrets in code secret-high-entropy-token README.md:232
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - CCM mint: `Dxk8…2BM` (Token-2022)
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:145
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - **CCM** (Creator Capital Markets): `Dxk8…2BM`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:148
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - **vLOFI**: `E9Kt…2dS`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:153
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - vLOFI/CCM: Meteora DLMM (`CEt6…H3M`)
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:154
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    - CCM/USDC: Raydium CLMM (`6Fwq…smm`)
    detector

Files scanned: 6. 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 "mcp_server"

Process rating: all ten parameters 51/100

  • 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. 2 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2283 tokens
  • low 13 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 89: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (1 code blocks)

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