Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add lovstudio/skills --skill professional-portraitgit clone --depth 1 https://github.com/lovstudio/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lovstudio/skills/professional-portrait)<a href="https://agentmods.dev/skills/lovstudio/skills/professional-portrait"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/professional-portrait/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lovstudio/skills/professional-portrait"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/professional-portrait.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 70 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00139 | $0.01893 |
| Opus 5.5 | $0.00056 | $0.00757 |
| Sonnet 5.5 | $0.00028 | $0.00379 |
| Haiku 4.5 | $0.00014 | $0.00189 |
Grade A, and why
lov-professional-portrait scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 25d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
职业形象照 · Professional Portrait
Turn one source photo into a polished, believable professional portrait. The face should still look unmistakably like the same person; "more professional" must not become "a different, AI-perfect person."
When to Use
- A casual photo needs to become a professional headshot or profile image.
- The user wants cleaner skin, brighter facial exposure, or a more polished overall look without changing identity.
- A hat, stray hair, or distracting background should be repaired.
- The user is iterating with feedback such as "变化不明显", "更干净帅气一点", or "只提亮面部".
- The user wants a before/after or progressive comparison after the edits.
Core Principle
Identity fidelity outranks beautification. Lock the following unless the user explicitly requests a change:
- Face geometry, eyes, nose, lips, jaw, ears, and apparent age.
- Skin tone family and recognizable facial details.
- Expression, gaze, body proportions, pose, and camera perspective.
- Clothing, accessories, framing, and background.
Remove only temporary distractions by default. Preserve believable skin texture, asymmetry, and age-appropriate detail.
Workflow (MANDATORY)
Step 1: Inspect the edit target
Identify the user's source photo as the edit target, not merely a style reference.
- If the photo is a local file, view it with the runtime's image-viewing tool before editing.
- Check face visibility, lighting, sharpness, crop, background, clothing, accessories, hair boundaries, and compression artifacts.
- Never publish, upload as a public example, or add the user's portrait to a repository unless the user separately asks for that.
Step 2: Infer the smallest sufficient brief
Do not ask the user to choose from a long style menu when the request already has a clear outcome. Use these defaults:
| User intent | Default treatment |
|---|---|
| "磨皮 / 干净一点" | Light skin cleanup with visible pores |
| "提亮" | Lift face exposure and eye clarity; preserve skin tone |
| "职业照 / 形象照" | Natural retouch + balanced light + polished crop/background |
| "去帽子" | Reconstruct only the hidden hair/head region |
| "帅气 / 精神" | Improve grooming, contrast, posture impression, and catchlights without reshaping the face |
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 25d ago Changed f1dc3d79d7ff
- 1mo ago Changed 47d839ebd65d
- 1mo ago First seen · 197 lines · 139 tokens per session scan A 0f2f6fcd46e6
lov-professional-portrait is a skill published in the GitHub repository lovstudio/skills (72 stars, last pushed today), licensed MIT. It adds 139 tokens to every session and 1,893 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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