BD us-stock-analyst
Professional US stock analysis with financial data, news, social sentiment, and multi-model AI. Comprehensive reports at $0.02-0.10 per analysis.
As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
AnalyzerInfrastructureData and analyticstype and topics are labelled automatically from the skill text
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 41/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 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
- 40Consistency. Frontmatter name (us-stock-analyst) differs from the folder (openclaw-aisa-us-stock-analyst)
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 100Steps. 27 steps
- 100Execution cost. Instruction body is 2488 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
- low 13 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
- -31 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 145: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 27 items
- +4Has examples (27 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.
External checks
ClawHub: clean
This is a coherent stock-analysis skill that openly uses AIsa APIs and local report files, with privacy considerations users should understand before use.
LLM: benign (high) · VirusTotal: benign · 28 May 2026