AB create-patent-landscape-overview-ip
Orchestrate an evidence-backed patent-landscape program for product planning, R&D strategy, competitor intelligence, technology-route analysis, recommended patent packages, and portfolio planning. Use when a user needs search and de-noising, complete-population landscape statistics, taxonomy design, a genuine human tagging handoff, representative patent analysis, and a self-contained scientific HTML report.
Orchestrate an evidence-backed patent-landscape program for product planning, R&D strategy, competitor intelligence, technology-route analysis, recommended…
As a process B 76/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6009 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "copyright"
Process rating: all ten parameters 76/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 11 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 50When it triggers. No condition that starts the skill
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 6009 tokens
- 100Tools and files. No external tools needed
- 100Steps. 309 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- low 22 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
- +1No license
- +2Single-language instructions
- +3Description length 410: enough signal without eating the budget
- +4Structure: 66 headings
- +3Step-by-step instructions: 309 items
- +4Has examples (12 code blocks)
- +4Reference files are cited in the instructions (9 of 9)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.