AC product-rnd
End-to-end Product Innovation R&D workflow — inspiration gathering, research, and professional report generation. Use whenever the user wants to create a product innovation or R&D report, develop a new product concept, conduct NPD research, or generate a structured output for a product or investor audience. Also triggers on "product innovation", "new product development", "product concept", "packaging design brief", or any request to generate a report around a product idea.
As a process C 58/100 · Has gaps — weak spots: result and completion, 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 58/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 9 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Failures and branches. 8 branches
- 70Execution cost. Instruction body is 4824 tokens
- 100Steps. 129 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (3 tags): a typed call is more reliable
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +5Description quotes 4 example trigger phrases
- +3Description length 478: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 129 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.