BC stock-screener
Stock screener for AI agents: filter US stocks and ETFs on the SentiSense Score, the SentiSense Rating letter grade, sentiment direction, analyst ratings and upside, technicals, momentum, price and market cap in one query, or run 28 curated screens like Crowd vs Street and Golden Cross + Bullish. Translates plain-language asks such as find oversold stocks with bullish sentiment into valid screen plans. Use for AI stock screener, stock screener with sentiment, stock screener API, ETF screener, stock scanner, find stocks by sentiment, screen stocks by rating, find A rated stocks, screen stocks by market cap, momentum screener, analyst upgrade screener, oversold stocks, 52-week low screener, unusual social volume. Read-only. No trading, no purchases, no write operations, no wallet access.
Stock screener for AI agents: filter US stocks and ETFs on the SentiSense Score, the SentiSense Rating letter grade, sentiment direction, analyst ratings and…
As a process C 53/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice
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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 7109 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "homepage" - note
frontmatter-keyunknown frontmatter key "requires" - note
frontmatter-keyunknown frontmatter key "primaryEnv"
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 10 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 7109 tokens
- 100Steps. 22 steps
- 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 11 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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 796: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 22 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.