文档

Skin Analysis Expert

试用

Analyze a person's skin from a single selfie using YouCam (Perfect Corp) AI. Returns 16 skin-condition scores plus skin type, as a readable report. Use when the user wants a skin analysis / skin report / skin score — incl. "幫我看膚質", "膚況分析", "how's my skin", "skin report". Do NOT use for skin-tone/facial attributes (that's facial-consultant), makeup, or hair.

它能做什么

Analyze a person's skin from a single selfie using YouCam (Perfect Corp) AI. Returns 16 skin-condition scores plus skin type, as a readable report. Use when the user wants a skin analysis / skin report / skin score — incl. "幫我看膚質", "膚況分析", "how's my skin", "skin report". Do NOT use for skin-tone/facial attributes (that's facial-consultant), makeup, or hair.

技能文档

Skin Analysis Expert

An analysis skill that calls one API (skin-analysis). There is no run.py: you (the agent) run the flow described here through the shared engine youcam_core.py.

When to trigger

When the user wants to understand their skin condition / get a skin report or score. If they want skin tone, facial attributes, makeup, or hair, hand off to the matching skill.

Get the latest API spec (do this every run)

Read this documentation section and open its latest version subpage to get the current endpoint / parameters / response fields: https://docs.perfectcorp.com/reference/ai_skin_analysis.md (e.g. the latest may be .../ai_skin_analysis/v2.1. If it is newer than api-fallback.yaml, pass --version when calling.)

Prerequisites

  1. A clear, front-facing, single-person selfie with even lighting (jpg/jpeg/png).
  2. Run python scripts/youcam_core.py validate-key (0 credits) to confirm the key. If it is not set, tell the user to get one at and set the YOUCAM_API_KEY env var, or create credentials.json in the skill root (see credentials.example.json for the format).
  3. Before running, check the cost and tell the user how many credits it will use: python scripts/youcam_core.py cost --feature skin-analysis (skin-analysis is tiered by SD/HD and number of concerns.)

Pre-run questionnaire (ask all 3 before running)

Ask the user these 3 questions before calling the API. Collect all answers first, then run once.

Q1 — Analysis tier

Would you like HD (high-detail, regional breakdowns) or SD (standard) analysis?

Q2 — Concerns to analyse

Which skin concerns would you like to check? Choose any combination, or say "all" for the full set.

#ConcernHD actionSD action
1Spots (age spots)hd_age_spotage_spot
2Wrinkleshd_wrinklewrinkle
3Texturehd_texturetexture
4Dark circleshd_dark_circledark_circle_v2
5Rednesshd_rednessredness
6Poreshd_porepore
7Acnehd_acneacne
8Oilinesshd_oilinessoiliness
9Hydrationhd_moisturemoisture
10Firmnesshd_firmnessfirmness
11Droopy upper eyelidhd_droopy_upper_eyeliddroopy_upper_eyelid
12Droopy lower eyelidhd_droopy_lower_eyeliddroopy_lower_eyelid
13Eye bagshd_eye_bageye_bag
14Radiancehd_radianceradiance
15Tear troughhd_tear_troughtear_trough
16Skin typehd_skin_typeskin_type

Note: HD and SD actions cannot be mixed in a single call.

Q3 — Image output style

How would you like the result images?

  • Overlay — the detection mask blended onto your photo (returns .jpg)
  • Mask — the raw detection mask only (returns .png, default)

Run

Always use format=json. Build the command from the user's answers to Q1–Q3:

python scripts/youcam_core.py run --feature skin-analysis --src_file  \
    --param format=json \
    --param dst_actions='[]' \
    --param miniserver_args='{"enable_mask_overlay": }'

Example (HD, all concerns, overlay):

python scripts/youcam_core.py run --feature skin-analysis --src_file photo.jpg \
    --param format=json \
    --param dst_actions='["hd_wrinkle","hd_pore","hd_texture","hd_acne","hd_redness","hd_oiliness","hd_moisture","hd_radiance","hd_firmness","hd_dark_circle","hd_eye_bag","hd_tear_trough","hd_droopy_upper_eyelid","hd_droopy_lower_eyelid","hd_age_spot","hd_skin_type"]' \
    --param miniserver_args='{"enable_mask_overlay": true}'

It returns scores. Interpret them with references/interpretation.md (note the direction of ui_score vs raw_score) and assemble the report with references/output-format.md.

Output rules

  • Follow the structure in references/output-format.md exactly:
    1. 3 lowest-scoring concerns (worst first) — with scores, star ratings, actionable advice, and their images.
    2. 3 highest-scoring concerns (best first) — with scores, star ratings, positive note, and their images.
    3. Overall results — text only: skin type, full concern score table, and overall impression paragraph. No images.
    4. Closing disclaimer (required, verbatim from output-format.md).
  • Report scores as returned (ui_score only); never invent conditions not present in the response.
  • If the API returns an error code (e.g. photo not compliant), give retake guidance instead of forcing a result.

相关技能

Analyze skin tone and facial attributes from a single selfie using YouCam (Perfect Corp) AI. Returns skin/eye/eyebrow/lip/hair colors plus facial feature shapes and golden-ratio proportions, as a readable report. Use for "測膚色", "臉型分析", "facial attributes". Do NOT use for skin-condition scoring (that's skin-analysis-expert), makeup, or hair try-on.

Diagnose hair-health metrics — density, type, frizziness, length — from a single selfie using YouCam (Perfect Corp) AI, and return one combined report. Use for "頭髮體檢", "髮質分析", "hair diagnostics", "hair health report". Do NOT use for hairstyle/color try-on (that's hair-color-style-advisor), skin, makeup, or clothes.

Recommend and preview makeup looks based on the user's face shape and skin tone, using YouCam (Perfect Corp) AI. Analyzes the selfie, suggests suitable looks, then renders a virtual makeup try-on image. Use for "推薦妝容", "幫我上妝", "makeup look", "virtual makeup". Do NOT use for skin-condition scoring, hair, or clothes.

1 次安装

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Analyzes an uploaded photograph, provides detailed feedback on exposure, focus, composition, lighting, and suggests concrete camera settings and shooting technique. Generates a simulated improved image based on the recommendations.