AA canslim-analysis
Executes a hybrid quantitative and qualitative CANSLIM analysis on US stocks using a fixed schema and a modular Python pipeline, returning a ranked shortlist.
As a process A 80/100 · Runs to the end — weak spots: inputs and preconditions
AnalyzerPDFInfrastructureSoftware developmenttype 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: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "requires" - note
frontmatter-keyunknown frontmatter key "os"
Process rating: all ten parameters 80/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 37 steps
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2170 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low 10 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)
- +1No license
- +2Single-language instructions
- +3Description length 158: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 37 items
- +3Output format is stated explicitly
- +4Has examples (7 code blocks)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.
External checks
ClawHub: clean
This is a disclosed stock-analysis skill that runs local Python, fetches public market data, and creates local JSON/PDF reports without evidence of hidden credential use, exfiltration, or destructive behavior.
LLM: benign (high) · VirusTotal: · 30 May 2026