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 agentmods add skills/code-yeongyu/senpi/skillnpx skills add code-yeongyu/senpi --skill skillgit clone --depth 1 https://github.com/code-yeongyu/senpiWhat 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 | $0.00063 | $0.01073 |
| Opus 5 | $0.00032 | $0.00536 |
| Sonnet 5 | $0.00013 | $0.00215 |
| Haiku 4.5 | $0.00006 | $0.00107 |
Grade A, and why
gpt-image-gen 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 2d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GPT Image Generation
How to write image prompts that come back right the first time, and how to fix them fast when they don't. Read this before your first generation call.
Which tool
When image generation tooling is present in your tool set, pick the surface that actually exists right now:
- If a native
image_generationserver tool is available, use it. The provider runs generation server-side and returns the image in the response stream. - Otherwise, call the
generate_imagetool. It sends your prompt to the configured OpenAI-compatible endpoint and saves the result to a file.
Check your current tool set before choosing. Skill visibility refreshes on reload, but tool state can change mid-session (model switch, credential change), so trust the tools you can see over what this page said at startup. If both surfaces ever appear, prefer the native server tool.
Prompt crafting
This section is the core of the skill. gpt-image models reward detail, and the single most common failure mode is a one-line prompt.
Build each prompt from six parts, in this order:
- Subject. Who or what, with concrete physical detail. "A middle-aged baker with flour on her forearms and a gray-streaked braid" beats "a baker".
- Medium and style. One style, stated plainly: "35mm film photograph", "watercolor illustration", "flat vector poster". Pick one lane.
- Composition and camera. Framing, angle, focal length or its visual equivalent. "Eye-level medium close-up, shallow depth of field, subject left of center".
- Lighting and color. Direction, quality, palette. "Soft window light from the left, warm amber tones against deep shadow".
- Mood. The emotional register: quiet, tense, celebratory, clinical.
- Background. What sits behind the subject, and how much of it is in focus.
A good prompt reads as a short paragraph, not a list and not a lone sentence. If your prompt fits on one line, it is under-specified, and the model will fill the gaps with whatever it likes.
Rendering text in the image
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.
- 2d ago First seen · 68 lines · 63 tokens per session scan A c17f229c5207
gpt-image-gen is a skill published in the GitHub repository code-yeongyu/senpi (411 stars, last pushed 2d ago), licensed MIT. It adds 63 tokens to every session and 1,073 once invoked, about $0.0003 per session on Opus 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.
Other skills, from other repositories
make-skill
Use this skill when sedimenting a session into a reusable workspace skill. Triggers when the user wants to turn the current conversation, workflow, or troubleshooting path into a SKILL.md. Phrases like 'turn this into a skill', 'remember how I did X', 'save this workflow', 'make a skill from this', and any /make-skill…
make-skill
用于把当前会话沉淀为可复用的 workspace skill。当用户希望把当前对话、工作流或排错路径写成 SKILL.md 时触发。触发表达包括「把这个变成 skill」「记住我是怎么做 X 的」「保存这个工作流」「make a skill from this」以及任何 /make-skill 调用。.
terraform-skill
Use when working with Terraform or OpenTofu - creating modules, writing tests (native test framework, Terratest), setting up CI/CD pipelines, reviewing configurations, choosing between testing approaches, debugging state issues, implementing security scanning (trivy, checkov), or making infrastructure-as-code…
docx
Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of "Word doc", "word document", ".docx", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when…
docx
当用户需要创建、读取、编辑或处理 Word 文档（.docx）时，使用此技能。触发场景包括提到“Word 文档”、“.docx”，或要求生成带目录、标题、页码、信头等格式的专业文档；也包括提取或重组 .docx 内容、插入或替换图片、在 Word 文件中查找替换、处理修订或批注，以及将内容整理为正式 Word 文档。如果用户要求生成“报告”“备忘录”“信函”“模板”等 Word / .docx 交付物，也应使用此技能。不要用于 PDF、电子表格、Google Docs，或与文档生成无关的一般编程任务。.
multi_agent_collaboration
Use this skill when another agent's expertise or context is needed, or when the user explicitly asks to involve another agent. First list agents, then use qwenpaw agents chat for two-way communication with replies.