DC expected-move-visualizer
Expected move visualizer for stocks and ETFs: turn implied volatility into a self-contained HTML chart showing the modeled 30, 60 and 90 day expected move cone around the current price, skewed by 25-delta put and call demand, with IV rank context and the next earnings date marked inside the cone. Renders offline from a bound data snapshot, no live call at view time. Use for expected move calculator, how much is this stock expected to move, implied volatility chart, options expected move, IV rank, earnings move visualizer, straddle move estimate. Read-only. No trading, no purchases, no write operations, no wallet access.
Expected move visualizer for stocks and ETFs: turn implied volatility into a self-contained HTML chart showing the modeled 30, 60 and 90 day expected move…
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.
Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- 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 · 2
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high Concealment
en-hide-from-userSKILL.md:179Instruction to hide actions from the user- When the next report is unknown because the free calendar covers one week, say exactly that. Never tell the user nothing is scheduled, and say a report inside the window cannot be ruled out.
Medium and low: 1
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medium Exfiltration
net-credential-useSKILL.md:120Credential used in a network call (verify the destination is the intended service)curl -H "X-SentiSense-API-Key: $SENTISENSE_API_KEY" \
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5407 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 56/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 5407 tokens
- 100Steps. 40 steps
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 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 627: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 40 items
- +4Has examples (3 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.