AB alphagbm-stock-analysis
AI-powered stock analysis using AlphaGBM's Five Pillars framework (Fundamental, Technical, Sentiment, Flow, Valuation) with real market data. Returns a 1-10 composite score with actionable signals. Use when: analyzing any stock ticker, evaluating buy/sell decisions, comparing stock fundamentals, assessing risk levels. Triggers on: "analyze AAPL", "what do you think about NVDA", "should I buy TSLA", "stock analysis for META", "is SPY overvalued", "risk assessment for GOOGL".
AI-powered stock analysis using AlphaGBM's Five Pillars framework (Fundamental, Technical, Sentiment, Flow, Valuation) with real market data.
As a process B 78/100 · Nearly there — weak spots: running it twice, progress reporting
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
- 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:44Credential used in a network call (verify the destination is the intended service)curl -H "Authorization: Bearer $ALPHAGBM_API_KEY" \
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "globs"
Process rating: all ten parameters 78/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 21 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1265 tokens
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)
- +1No license
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +3Description length 478: enough signal without eating the budget
- +4Structure: 21 headings
- +3Step-by-step instructions: 21 items
- +3Output format is stated explicitly
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.