AB event-countdown-pro
Set up a daily countdown reminder for a stock's next price-moving corporate event — earnings releases, AGMs, product launches, investor days, dividend dates, FDA decisions, or any event likely to impact the stock price. Resolves company name to ticker, discovers the next significant event from yfinance and web search, and creates a daily cron reminder via remind-me that runs stock-price-checker-pro and stock-fundamentals every day starting 1–2 weeks before the event. Triggers: earnings reminder, earnings countdown, AGM reminder, AGM countdown, event countdown, corporate event reminder, product launch countdown, investor day reminder, notify me before earnings, remind me about earnings, earnings alert.
Set up a daily countdown reminder for a stock's next price-moving corporate event — earnings releases, AGMs, product launches, investor days, dividend dates…
As a process B 65/100 · Nearly there — weak spots: inputs and preconditions
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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 65/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Failures and branches. 8 branches
- 70Execution cost. Instruction body is 4900 tokens
- 100Steps. 68 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low The response is described with custom markup (48 tags): a typed call is more reliable
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)
- -215 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 710: enough signal without eating the budget
- +4Structure: 25 headings
- +3Step-by-step instructions: 68 items
- +3Output format is stated explicitly
- +4Has examples (10 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.