AC stock-screener
A comprehensive growth stock screening and analysis skill. Use this skill whenever the user asks about growth stocks, stock screening, equity analysis, investment opportunities, stock picks, market trends affecting equities, sector rotation, earnings analysis, revenue growth analysis, or fundamental stock evaluation. Also trigger when the user mentions terms like "high-growth companies", "multibagger", "momentum stocks", "earnings growth", "revenue acceleration", "TAM expansion", "secular trends", "stock watchlist", "portfolio ideas", or asks questions like "what stocks should I look at", "find me growth opportunities", "which companies are benefiting from AI/cloud/EVs", or "analyze this stock's growth potential". This skill combines quantitative financial screening with qualitative trend analysis to deliver institutional-grade growth stock research.
As a process C 60/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice
How to improve
- 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
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 60/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (stock-screener) differs from the folder (stock-screener-growth)
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Failures and branches. 4 branches
- 100Steps. 142 steps
- 100Execution cost. Instruction body is 3561 tokens
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 862: 120–800 characters recommended
- +1No license
- +2Single-language instructions
- +5Description quotes 13 example trigger phrases
- +4Structure: 27 headings
- +3Step-by-step instructions: 142 items
- +3Output format is stated explicitly
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.