BB referee-response
Organizes and formats an author’s response to peer review. Extracts every distinct point from referee reports and the editor’s letter, tags severity and type, maps dependencies to sequence revisions, flags potential pushback, and builds a numbered comment → response → location table with substantive responses left to the author. It formats and checks the response without writing scientific content. Use when the user has reports, a revise-and-resubmit decision, or an editor’s letter and says "respond to reviewers", "plan the revision", "map the referee comments", "draft the response letter", or "check I addressed everything". The reviewer-side twin is journal-review. Prose polish goes to sci-edit.
Organizes and formats an author’s response to peer review.
As a process B 70/100 · Nearly there — weak spots: result and completion, failures and branches, 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: 1. 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 70/100
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1431 tokens
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 5 example trigger phrases
- +3Description length 705: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 20 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.