BC last-30-days-in-markets
What happened in the stock market over the last 30 days, as one synthesized brief: the day-by-day arc of a fear-to-greed market mood index, the month's biggest AI-clustered story themes ranked by impact, which tickers and sectors dominated the news, the sentiment and smart-money signals that accumulated, where the market stands today, and the earnings ahead. Built for deep research rather than a fast summary: every claim traces to a fetched response and carries its date and its real coverage window, so the reader can check it instead of trusting a generated answer. Works for one stock too: the month's feed filtered to a ticker plus its stock insights. Use for "last 30 days in markets", "what happened in the market this month", "what did I miss in the market", "monthly market recap", "market summary last 30 days", "deep research on the stock market", "catch me up on stocks", "catch me up on NVDA". Read-only. No trading, no purchases, no write operations, no wallet access.
What happened in the stock market over the last 30 days, as one synthesized brief: the day-by-day arc of a fear-to-greed market mood index, the month's…
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
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
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medium Exfiltration
net-credential-useSKILL.md:116Credential 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 ≈ 5668 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 55/100
- 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
- 30Running it twice. 6 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
- 70Execution cost. Instruction body is 5668 tokens
- 100Steps. 38 steps
- 100Failures and branches. 2 branches, has a failure section
- 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 10 top-level sections: this looks like several domains in one skill
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
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 985: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 8 example trigger phrases
- +4Structure: 14 headings
- +3Step-by-step instructions: 38 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.