AB ai-trading-consortium
AI-powered hedge fund that combines multi-expert trading strategies with comprehensive information gathering. Analyzes stocks through systematic data collection, analyst reports, bull/bear debates, and a council of legendary investor personas (Buffett, Graham, Lynch, Burry, Munger, Wood, Druckenmiller, Marks). Produces executive summary slides. Trigger keywords: stock analysis, trading decision, investment analysis, market evaluation, buy/sell/hold recommendation.
As a process B 65/100 · Nearly there — weak spots: running it twice, progress reporting
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 7355 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 65/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 5 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 60Failures and branches. 2 branches
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 7355 tokens
- 85Steps. 227 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 12 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)
- +1No license
- +2Single-language instructions
- +3Description length 468: enough signal without eating the budget
- +4Structure: 69 headings
- +3Step-by-step instructions: 227 items
- +3Output format is stated explicitly
- +4Has examples (10 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.