SKILLEMALL.ai

BB Kalshalyst

Contrarian prediction market scanner that finds mispricings on Kalshi using Claude Sonnet analysis, Brier score calibration, and Kelly Criterion position sizing. Five-phase pipeline: fetch markets, classify, estimate contrarian probabilities, calculate edge, and alert. Tracks estimate accuracy over time so you know when to trust the signal. Part of the OpenClaw Prediction Market Trading Stack — feeds edge data to Market Morning Brief and pairs with Kalshi Command Center for execution.

ClawHub Agent Skills author: kingmadellc v1.1.5 MIT-0 18 files body ≈ 6 003 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 70/100 · Nearly there — weak spots: when it triggers, running it twice

ProcedureInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
74
Run on models
none yet
Process rating
B
70/100
Nearly there
When it triggers w 12
20
Running it twice w 4
30
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-cron-mention SKILL.md:473
    Mentions editing / listing crontab (documentation of a security skill)
    # Add to crontab -e:
    security skill

Files scanned: 18. 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 body-long SKILL.md body ≈ 6003 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 70/100

  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 10 mutating operations with no state check
  • 60Tools and files. Uses tools (web, git, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 6003 tokens
  • 100Steps. 150 steps
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • low 20 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)
  • -39 of 10 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 489: enough signal without eating the budget
  • +4Structure: 56 headings
  • +3Step-by-step instructions: 150 items
  • +3Output format is stated explicitly
  • +4Has examples (26 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: suspicious
This looks like a real Kalshi trading tool, but it needs Review because it can trade and cancel orders despite being partly described as a scanner.
LLM: suspicious (high) · VirusTotal: · 29 May 2026