AB Kalshi Command Center
Complete Kalshi trading command interface — portfolio P&L, live market scanning with edge scoring, trade execution, and risk management through your OpenClaw agent. Built-in safety: $25 max trade, 100 contract cap, $50 daily loss cutoff. Scan 600+ markets, query positions, execute trades with configurable blocklists and retry logic. Part of the OpenClaw Prediction Market Trading Stack — pairs with Kalshalyst for intelligent execution and feeds portfolio data to Market Morning Brief.
As a process B 67/100 · Nearly there — weak spots: when it triggers, consistency, running it twice
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 67/100
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (Kalshi Command Center) differs from the folder (kalshi-command-center)
- 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
- 100Steps. 69 steps
- 100Failures and branches. 3 branches, has a failure section
- 100Execution cost. Instruction body is 3993 tokens
- 100Progress reporting. Reports progress
- 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)
- -217 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 487: enough signal without eating the budget
- +4Structure: 55 headings
- +3Step-by-step instructions: 69 items
- +3Output format is stated explicitly
- +4Has examples (32 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.