AA ai-headshot-studio
Transform a casual selfie into a studio-quality professional headshot for LinkedIn, resume, company website, business card, or social media. This AI headshot generator creates polished professional portraits with new backgrounds, professional attire, and studio lighting while preserving the person's identity. Generate corporate headshots, tech startup portraits, academic profile photos, medical professional images, and creative industry portraits from one selfie. Specify a professional style and industry, pair with a desired background or setting, or refine an accepted headshot toward a publish-ready result.
Transform a casual selfie into a studio-quality professional headshot for LinkedIn, resume, company website, business card, or social media.
As a process A 81/100 · Runs to the end — weak spots: inputs and preconditions, progress reporting
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 17. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 81/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Steps. 23 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2268 tokens
- 100Running it twice. Mutating operations check current state
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)
- -32 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 615: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 23 items
- +3Output format is stated explicitly
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (11 of 11)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.