AC motu-color-engine
AI portrait grading, skin-tone correction, identity-preserving smoothing, mask export, approved clothing replacement, professional AI Headshots generation, and ID/passport/headshot/avatar production through the MotuArt Color Engine HTTP API. Use for retouching portraits, normalizing skin tone, exporting mattes, replacing clothing, preparing identity references and generating professional headshot candidates, cropping to ID/passport/visa specs, replacing ID-photo backgrounds, checking compliance, optimizing uploads, or creating print sheets. Triggers include portrait grading, skin tone, retouch, skin mask, outfit replacement, AI headshots, professional headshot, business portrait, corporate portrait, LinkedIn photo, ID photo, passport photo, visa photo, background swap, print sheet, 调色, 肤色, 磨皮, 蒙版, 人像调色, AI形象照, 职业形象照, 商务形象照, 企业头像, 换装, 证件照, 裁剪, 换底, 合规检查, 排版, 一寸, 二寸.
AI portrait grading, skin-tone correction, identity-preserving smoothing, mask export, approved clothing replacement, professional AI Headshots generation…
As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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: 0. 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 52/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 30Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 7 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70Execution cost. Instruction body is 4091 tokens
- 100Steps. 93 steps
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 14 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)
- +3Description length 876: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 15 headings
- +3Step-by-step instructions: 93 items
- +4Has examples (20 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 14 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.