BD stock-market-dashboard
Build a stock market dashboard as a single self-contained HTML file you can open in a browser. Generates a morning market briefing from live data: a fear-to-greed market mood gauge with its component signals, sentiment breadth, sector heat, the biggest 7-day attention shifts, an options radar, a watchlist with sentiment scores and analyst consensus, insider cluster buys, congressional trades, institutional flows, overnight story clusters, and the week's earnings calendar. No backend, no build step, no dependencies, one file. Use for "build me a stock market dashboard", "make a stock dashboard html", "morning market briefing", "watchlist dashboard", "daily market report". Read-only. No trading, no purchases, no write operations, no wallet access.
Build a stock market dashboard as a single self-contained HTML file you can open in a browser.
As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
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
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Exfiltration
net-credential-useSKILL.md:132Credential used in a network call (verify the destination is the intended service)curl -s -H "X-SentiSense-API-Key: $SENTISENSE_API_KEY" \
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5084 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 48/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 6 mutating operations with no state check
- 60Tools and files. Uses tools (bash, write, web, node) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 70Execution cost. Instruction body is 5084 tokens
- 100Steps. 39 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
- high The skill tells the model to perform an irreversible action with no human approval
- low The response is described with custom markup (3 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
- +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
- +5Description quotes 5 example trigger phrases
- +3Description length 755: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 39 items
- +4Has examples (3 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.