BB scan-emerging-ai-technologies-rd
Route a technology-intelligence request to a technology landscape, invention-mining workflow, or current-intelligence briefing, and produce an evidence-backed English HTML report. Use for emerging AI, semiconductor, display, electronics, or other fast-moving technology domains when the user needs route evolution, competitor positioning, invention opportunities, patent mining, or multi-source monitoring.
Route a technology-intelligence request to a technology landscape, invention-mining workflow, or current-intelligence briefing, and produce an evidence-backed…
As a process B 69/100 · Nearly there — weak spots: failures and branches, execution cost, running it twice
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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 8295 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "copyright"
Process rating: all ten parameters 69/100
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 14 mutating operations with no state check
- 40Execution cost. Instruction body is 8295 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 471 steps, 3 vague phrases
- 100Result and completion. Output format and completion criterion are stated
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- low 17 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 406: enough signal without eating the budget
- +4Structure: 68 headings
- +3Step-by-step instructions: 471 items
- +3Output format is stated explicitly
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.