AB classify-technical-evidence-ip
Build, refine, govern, and apply an evidence-based taxonomy to patents, scientific literature, product records, technical intelligence, customer requirements, and other structured text. Use for open label discovery, semi-open or closed-set classification, label definitions and decision rules, pilot labeling, full CSV/XLSX labeling, adjudication queues, taxonomy backlogs, quality assurance, and selective PatSnap MCP evidence enrichment.
Build, refine, govern, and apply an evidence-based taxonomy to patents, scientific literature, product records, technical intelligence, customer requirements…
As a process B 66/100 · Nearly there — weak spots: inputs and preconditions, running it twice
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 · 0
✓ No critical or high findings
Files scanned: 23. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "copyright"
Process rating: all ten parameters 66/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 11 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 85Steps. 200 steps, 1 vague phrases
- 100Result and completion. Output format and completion criterion are stated
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3906 tokens
- 100Progress reporting. Reports progress
- low 11 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)
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 439: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 200 items
- +3Output format is stated explicitly
- +4Reference files are cited in the instructions (6 of 7)
- +3All 5 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.