AC options-payoff
Generate an interactive options payoff curve chart with dynamic parameter controls. Use this skill whenever the user shares an options position screenshot, describes an options strategy, or asks to visualize how an options trade makes or loses money. Triggers include: any mention of butterfly, spread (vertical/calendar/diagonal/ratio), straddle, strangle, condor, covered call, protective put, iron condor, or any multi-leg options structure. Also triggers when a user pastes strike prices, premiums, expiry dates, or says things like "show me the payoff", "draw the P&L curve", "what does this trade look like", or uploads a screenshot from a broker (IBKR, TastyTrade, Robinhood, etc). Always use this skill even if the user only provides partial info — extract what you can and use defaults for the rest.
As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 57/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 70When it triggers. States when to use, but not when not to
- 85Steps. 29 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1475 tokens
- 100Running it twice. No mutating operations
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 808: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +4Structure: 17 headings
- +3Step-by-step instructions: 29 items
- +4Has examples (7 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.