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BF prediction-market-arbitrageur

Meta-skill for orchestrating topic-monitor, polymarket-odds, and simmer-weather to detect potential news-vs-market mispricing in prediction markets. Use when users want a clear, step-by-step LM workflow for monitoring breaking signals, reading current Polymarket probabilities, computing confidence/price deltas, and producing alert-first arbitrage decisions.

modbender/skill-library-mcp Claude Code author: modbender MIT 2 files body ≈ 1 762 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Meta-skill for orchestrating topic-monitor, polymarket-odds, and simmer-weather to detect potential news-vs-market mispricing in prediction markets.

As a process F 57/100 · Will not run — References files that are not bundled: scripts/manage_topics.py, scripts/monitor.py

AnalyzerInfrastructureSoftware 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
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
F
57/100
Will not run
References files that are not bundled: scripts/manage_topics.py, scripts/monitor.py
Tools and files w 18
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/manage_topics.py
  • warning missing-ref reference to a missing file: scripts/monitor.py
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 57/100

Will not run. References files that are not bundled: scripts/manage_topics.py, scripts/monitor.py
  • 0Tools and files. 2 referenced file(s) missing: scripts/manage_topics.py, scripts/monitor.py
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (prediction-market-arbitrageur) differs from the folder (prediction-market-arbitrage)
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 59 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Execution cost. Instruction body is 1762 tokens
  • 100Running it twice. No mutating operations
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • 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)
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 359: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 59 items
  • +3Output format is stated explicitly
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.