AC project-skeletonization
Collaborative workflow for converting a complete business project into a reusable base skeleton. Use when the user asks to remove business features, pages, or modules; keep base framework capabilities such as common components, authentication, and deployment; or turn Spring Boot, Vue, Node.js, Python, React, Django, FastAPI, Express, or similar projects into a starter, skeleton, or template. Before modifying code, inventory the project, propose keep/remove decisions, list uncertain items, and wait for user confirmation.
Collaborative workflow for converting a complete business project into a reusable base skeleton.
As a process C 59/100 · 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: 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 59/100
- 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. 22 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 81 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2217 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
- +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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 525: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 81 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.