AC trade-decision-policy
Use when stock research, valuation, portfolio analytics, and risk outputs must be converted into structured Vietnam equity recommendation labels, target weights, invalidation rules, and approval-gated action drafts.
As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
GeneratorFinanceInfrastructureData and analyticstype and topics are labelled automatically from the skill text
How to improve
For the model run — optional
- 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: 3. 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 61/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 49 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 660 tokens
- 100Running it twice. No mutating operations
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 215: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 49 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.
External checks
ClawHub: clean
This finance-platform skill is broad and uses authenticated cloud services, but its access and workflows are disclosed and aligned with its stated purpose.
LLM: benign (medium) · VirusTotal: · 28 May 2026