AB identify-rd-directions-rd
Convert a concrete engineering, scientific, manufacturing, or technical project requirement into evidence-backed R&D directions, including requirement analysis, bounded technical issues, research questions, tasks, targets, deliverables, patent and literature evidence, standards and engineering cases, relevant organizations, search logs, and synchronized Markdown and HTML reports. Use when a user asks what R&D directions to pursue, how to decompose a project into research routes, or needs an evidence-led R&D direction report.
Convert a concrete engineering, scientific, manufacturing, or technical project requirement into evidence-backed R&D directions, including requirement…
As a process B 68/100 · Nearly there — weak spots: result and completion, failures and branches, 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: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6147 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "copyright"
Process rating: all ten parameters 68/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 16 mutating operations with no state check
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 6147 tokens
- 100Tools and files. No external tools needed
- 100Steps. 291 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- low 29 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 530: enough signal without eating the budget
- +4Structure: 44 headings
- +3Step-by-step instructions: 291 items
- +4Has examples (1 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.