AB portrait-generator
Generate hyper-detailed AI portraits via ComfyDeploy Morfeo Portrait workflow. Control every facial feature: eyes, nose, lips, jawline, skin, hair, expression. ✅ USE WHEN: - Need a specific face with controlled features (age, ethnicity, expression, skin) - Generating model references for Morpheus campaigns - Creating diverse character portraits for content - User describes a specific person or character type ❌ DON'T USE WHEN: - Need a full-body shot → use morpheus-fashion-design - Need a person WITH a product → use morpheus-fashion-design - Need image variations → use multishot-ugc INPUT: Natural language description → mapped to structured facial parameters OUTPUT: High-resolution portrait PNG DEPLOYMENT ID: 0b82e690-9a08-4d1f-85f8-28849d16caa4
Generate hyper-detailed AI portraits via ComfyDeploy Morfeo Portrait workflow.
As a process B 67/100 · Nearly there — weak spots: running it twice, progress reporting
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 67/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 60Failures and branches. 2 branches
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 15 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2999 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
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 758: enough signal without eating the budget
- +4Structure: 53 headings
- +3Step-by-step instructions: 15 items
- +3Output format is stated explicitly
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.