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 Gayaya999/personal-ip-generator --skill personal-ip-generatorgit clone --depth 1 https://github.com/Gayaya999/personal-ip-generatorWrote 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/gayaya999/personal-ip-generator/personal-ip-generator)<a href="https://agentmods.dev/skills/gayaya999/personal-ip-generator/personal-ip-generator"><img src="https://agentmods.dev/badge/skills/gayaya999/personal-ip-generator/personal-ip-generator/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/gayaya999/personal-ip-generator/personal-ip-generator"><img src="https://agentmods.dev/badge/skills/gayaya999/personal-ip-generator/personal-ip-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00114 | $0.01855 |
| Opus 5.5 | $0.00046 | $0.00742 |
| Sonnet 5.5 | $0.00023 | $0.00371 |
| Haiku 4.5 | $0.00011 | $0.00186 |
Grade A, and why
personal-ip-generator 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 2mo 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
个人 IP 生成 Skill
Core Rule
Start with a Plan-Mode-style wizard and ask exactly one material question per assistant turn. Do not generate an image until the user has selected a preset style, the generation profile is complete, and the user has approved the final generation plan.
Treat the selected preset as the style source of truth. Separate identity features from style features before generating. Preserve the person's recognizable anchors while transferring only the preset's approved visual language.
Do not store user portraits inside this skill. Use only images supplied by the user or images the user is authorized to use. Do not infer sensitive attributes such as ethnicity, religion, health, sexuality, or politics from appearance.
Load References
- Read
references/intake-and-style.mdwhenever reference images are supplied, their roles are unclear, inputs conflict, or a text-only brief is incomplete. - Always read
references/plan-mode-wizard.mdbefore asking intake questions. - Always read
references/style-presets.mdbefore presenting or applying preset styles. - Read
references/prompt-patterns.mdbefore every image-generation call. - Read
references/qa-and-deliverables.mdbefore generating derivative views or delivering a complete package.
Style Preset Intake
When the user supplies a group of style reference images, treat them as style references unless the user explicitly labels an image as an identity source or an existing IP. Inspect the group before asking the next question and extract one shared style fingerprint; do not copy the depicted person, character, props, text, logo, watermark, or composition.
For a new preset:
- Confirm that the images are authorized style references and group the supplied images into one preset.
- Record line, shape, proportion, rendering/material, palette, lighting, texture, composition, background, sticker-outline behavior, tags, best uses, and negative constraints.
- Ask exactly one question for the user's Chinese display name, then derive a stable lowercase hyphenated preset ID.
- Ask for explicit authorization before copying the reference assets to
assets/style-presets/<preset-id>/. - Show the proposed preset entry and ask whether it should be
activeordraft; onlyactivepresets appear in user selection. - If an existing preset is materially similar, flag it and let the user choose merge or create-new. Never silently overwrite a preset.
What ships with it
22 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.
- agents/openai.yaml 322 B
- assets/style-presets/3d-feng/reference-01.png 632 KB
- assets/style-presets/3d-feng/reference-02.png 929 KB
- assets/style-presets/3d-feng/reference-03.png 1015 KB
- assets/style-presets/caihui-feng/reference-01.png 484 KB
- assets/style-presets/caihui-feng/reference-02.png 545 KB
- assets/style-presets/caihui-feng/reference-03.png 1014 KB
- assets/style-presets/figure-feng/reference-01.png 373 KB
- assets/style-presets/figure-feng/reference-02.png 352 KB
- assets/style-presets/figure-feng/reference-03.png 208 KB
- assets/style-presets/figure-feng/reference-04.png 293 KB
- assets/style-presets/illustration-feng/reference-01.png 492 KB
- assets/style-presets/illustration-feng/reference-02.png 742 KB
- assets/style-presets/illustration-feng/reference-03.png 832 KB
- assets/style-presets/illustration-feng/reference-04.png 314 KB
- assets/style-presets/xian-tiao-feng/reference-01.png 242 KB
- assets/style-presets/xian-tiao-feng/reference-02.png 147 KB
- references/intake-and-style.md 3.4 KB
- references/plan-mode-wizard.md 5.8 KB
- references/prompt-patterns.md 4.7 KB
- references/qa-and-deliverables.md 3.9 KB
- references/style-presets.md 14 KB
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.
- 2mo ago First seen · 94 lines · 114 tokens per session scan A dd1c0162b481
personal-ip-generator is a skill published in the GitHub repository Gayaya999/personal-ip-generator (163 stars, last pushed 3mo ago), licensed MIT. It adds 114 tokens to every session and 1,855 once invoked, about $0.0005 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 2026-08-30.
personal-ip-generator compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| personal-ip-generatorGayaya999/personal-ip-generator | Skill | 163 | 3mo ago | MIT | A | 114 | Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |