mirror of
https://github.com/JimLiu/baoyu-skills.git
synced 2026-07-12 22:09:48 +08:00
Compare commits
11 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 7d03685ade | |||
| b305c386bc | |||
| 240dd7d314 | |||
| b02ceacfd9 | |||
| fdf9007e2c | |||
| 08cee885d3 | |||
| 3bd5fdeb1b | |||
| e737c4a611 | |||
| 2eec4f3639 | |||
| e5912018f3 | |||
| b1f568d03d |
@@ -6,7 +6,7 @@
|
||||
},
|
||||
"metadata": {
|
||||
"description": "Skills shared by Baoyu for improving daily work efficiency",
|
||||
"version": "1.35.0"
|
||||
"version": "1.37.1"
|
||||
},
|
||||
"plugins": [
|
||||
{
|
||||
|
||||
@@ -2,6 +2,27 @@
|
||||
|
||||
English | [中文](./CHANGELOG.zh.md)
|
||||
|
||||
## 1.37.1 - 2026-02-27
|
||||
|
||||
### Fixes
|
||||
- `baoyu-danger-gemini-web`: sync model headers with upstream and update model list (by @xkcoding)
|
||||
|
||||
## 1.37.0 - 2026-02-27
|
||||
|
||||
### Features
|
||||
- `baoyu-danger-x-to-markdown`: add inline link rendering for X article content, mapping LINK/MEDIA entities to markdown links
|
||||
- `baoyu-danger-x-to-markdown`: use content-based slug in output directory path for meaningful folder names
|
||||
- `baoyu-danger-x-to-markdown`: add atomic media queue for blocks without direct media references
|
||||
|
||||
## 1.36.0 - 2026-02-27
|
||||
|
||||
### Features
|
||||
- `baoyu-image-gen`: add `gemini-3.1-flash-image-preview` model support for Google multimodal image generation
|
||||
- `baoyu-image-gen`: improve first-time setup with blocking preferences flow and guided configuration
|
||||
|
||||
### Fixes
|
||||
- `baoyu-image-gen`: use curl fallback for Google API when HTTP proxy is detected (by @liye71023326)
|
||||
|
||||
## 1.35.0 - 2026-02-24
|
||||
|
||||
### Features
|
||||
|
||||
@@ -2,6 +2,27 @@
|
||||
|
||||
[English](./CHANGELOG.md) | 中文
|
||||
|
||||
## 1.37.1 - 2026-02-27
|
||||
|
||||
### 修复
|
||||
- `baoyu-danger-gemini-web`:同步上游模型请求头并更新模型列表 (by @xkcoding)
|
||||
|
||||
## 1.37.0 - 2026-02-27
|
||||
|
||||
### 新功能
|
||||
- `baoyu-danger-x-to-markdown`:支持 X 文章内联链接渲染,将 LINK/MEDIA 实体映射为 Markdown 链接
|
||||
- `baoyu-danger-x-to-markdown`:输出目录使用基于内容的 slug,生成更有意义的文件夹名称
|
||||
- `baoyu-danger-x-to-markdown`:新增 atomic 媒体队列,支持无直接媒体引用的区块
|
||||
|
||||
## 1.36.0 - 2026-02-27
|
||||
|
||||
### 新功能
|
||||
- `baoyu-image-gen`:新增 `gemini-3.1-flash-image-preview` Google 多模态图片生成模型支持
|
||||
- `baoyu-image-gen`:优化首次使用引导流程,支持阻塞式偏好配置
|
||||
|
||||
### 修复
|
||||
- `baoyu-image-gen`:检测到 HTTP 代理时自动回退使用 curl 调用 Google API (by @liye71023326)
|
||||
|
||||
## 1.35.0 - 2026-02-24
|
||||
|
||||
### 新功能
|
||||
|
||||
@@ -76,7 +76,7 @@ test -f "$HOME/.baoyu-skills/baoyu-danger-gemini-web/EXTEND.md" && echo "user"
|
||||
```bash
|
||||
# Text generation
|
||||
npx -y bun ${SKILL_DIR}/scripts/main.ts "Your prompt"
|
||||
npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "Your prompt" --model gemini-2.5-pro
|
||||
npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "Your prompt" --model gemini-3-flash
|
||||
|
||||
# Image generation
|
||||
npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "A cute cat" --image cat.png
|
||||
@@ -100,7 +100,7 @@ npx -y bun ${SKILL_DIR}/scripts/main.ts "Hello" --json
|
||||
|--------|-------------|
|
||||
| `--prompt`, `-p` | Prompt text |
|
||||
| `--promptfiles` | Read prompt from files (concatenated) |
|
||||
| `--model`, `-m` | Model: gemini-3-pro (default), gemini-2.5-pro, gemini-2.5-flash |
|
||||
| `--model`, `-m` | Model: gemini-3-pro (default), gemini-3-flash, gemini-3-flash-thinking, gemini-3.1-pro-preview |
|
||||
| `--image [path]` | Generate image (default: generated.png) |
|
||||
| `--reference`, `--ref` | Reference images for vision input |
|
||||
| `--sessionId` | Session ID for multi-turn conversation |
|
||||
@@ -114,9 +114,10 @@ npx -y bun ${SKILL_DIR}/scripts/main.ts "Hello" --json
|
||||
|
||||
| Model | Description |
|
||||
|-------|-------------|
|
||||
| `gemini-3-pro` | Default, latest |
|
||||
| `gemini-2.5-pro` | Previous pro |
|
||||
| `gemini-2.5-flash` | Fast, lightweight |
|
||||
| `gemini-3-pro` | Default, latest 3.0 Pro |
|
||||
| `gemini-3-flash` | Fast, lightweight 3.0 Flash |
|
||||
| `gemini-3-flash-thinking` | 3.0 Flash with thinking |
|
||||
| `gemini-3.1-pro-preview` | 3.1 Pro preview (empty header, auto-routed) |
|
||||
|
||||
## Authentication
|
||||
|
||||
|
||||
@@ -47,17 +47,22 @@ export class Model {
|
||||
static readonly UNSPECIFIED = new Model('unspecified', {}, false);
|
||||
static readonly G_3_0_PRO = new Model(
|
||||
'gemini-3.0-pro',
|
||||
{ 'x-goog-ext-525001261-jspb': '[1,null,null,null,"9d8ca3786ebdfbea",null,null,0,[4]]' },
|
||||
{ 'x-goog-ext-525001261-jspb': '[1,null,null,null,"9d8ca3786ebdfbea",null,null,0,[4],null,null,1]' },
|
||||
false,
|
||||
);
|
||||
static readonly G_2_5_PRO = new Model(
|
||||
'gemini-2.5-pro',
|
||||
{ 'x-goog-ext-525001261-jspb': '[1,null,null,null,"4af6c7f5da75d65d",null,null,0,[4]]' },
|
||||
static readonly G_3_0_FLASH = new Model(
|
||||
'gemini-3.0-flash',
|
||||
{ 'x-goog-ext-525001261-jspb': '[1,null,null,null,"fbb127bbb056c959",null,null,0,[4],null,null,1]' },
|
||||
false,
|
||||
);
|
||||
static readonly G_2_5_FLASH = new Model(
|
||||
'gemini-2.5-flash',
|
||||
{ 'x-goog-ext-525001261-jspb': '[1,null,null,null,"9ec249fc9ad08861",null,null,0,[4]]' },
|
||||
static readonly G_3_0_FLASH_THINKING = new Model(
|
||||
'gemini-3.0-flash-thinking',
|
||||
{ 'x-goog-ext-525001261-jspb': '[1,null,null,null,"5bf011840784117a",null,null,0,[4],null,null,1]' },
|
||||
false,
|
||||
);
|
||||
static readonly G_3_1_PRO_PREVIEW = new Model(
|
||||
'gemini-3.1-pro-preview',
|
||||
{},
|
||||
false,
|
||||
);
|
||||
|
||||
@@ -68,12 +73,12 @@ export class Model {
|
||||
) {}
|
||||
|
||||
static from_name(name: string): Model {
|
||||
for (const model of [Model.UNSPECIFIED, Model.G_3_0_PRO, Model.G_2_5_PRO, Model.G_2_5_FLASH]) {
|
||||
for (const model of [Model.UNSPECIFIED, Model.G_3_0_PRO, Model.G_3_0_FLASH, Model.G_3_0_FLASH_THINKING, Model.G_3_1_PRO_PREVIEW]) {
|
||||
if (model.model_name === name) return model;
|
||||
}
|
||||
|
||||
throw new Error(
|
||||
`Unknown model name: ${name}. Available models: ${[Model.UNSPECIFIED, Model.G_3_0_PRO, Model.G_2_5_PRO, Model.G_2_5_FLASH]
|
||||
`Unknown model name: ${name}. Available models: ${[Model.UNSPECIFIED, Model.G_3_0_PRO, Model.G_3_0_FLASH, Model.G_3_0_FLASH_THINKING, Model.G_3_1_PRO_PREVIEW]
|
||||
.map((m) => m.model_name)
|
||||
.join(', ')}`,
|
||||
);
|
||||
|
||||
@@ -73,7 +73,7 @@ Multi-turn conversation (agent generates unique sessionId):
|
||||
Options:
|
||||
-p, --prompt <text> Prompt text
|
||||
--promptfiles <files...> Read prompt from one or more files (concatenated in order)
|
||||
-m, --model <id> gemini-3-pro | gemini-2.5-pro | gemini-2.5-flash (default: gemini-3-pro)
|
||||
-m, --model <id> gemini-3-pro | gemini-3-flash | gemini-3-flash-thinking | gemini-3.1-pro-preview (default: gemini-3-pro)
|
||||
--json Output JSON
|
||||
--image [path] Generate an image and save it (default: ./generated.png)
|
||||
--reference <files...> Reference images for vision input
|
||||
@@ -227,8 +227,11 @@ function resolveModel(id: string): Model {
|
||||
const k = id.trim();
|
||||
if (k === 'gemini-3-pro') return Model.G_3_0_PRO;
|
||||
if (k === 'gemini-3.0-pro') return Model.G_3_0_PRO;
|
||||
if (k === 'gemini-2.5-pro') return Model.G_2_5_PRO;
|
||||
if (k === 'gemini-2.5-flash') return Model.G_2_5_FLASH;
|
||||
if (k === 'gemini-3-flash') return Model.G_3_0_FLASH;
|
||||
if (k === 'gemini-3.0-flash') return Model.G_3_0_FLASH;
|
||||
if (k === 'gemini-3-flash-thinking') return Model.G_3_0_FLASH_THINKING;
|
||||
if (k === 'gemini-3.0-flash-thinking') return Model.G_3_0_FLASH_THINKING;
|
||||
if (k === 'gemini-3.1-pro-preview') return Model.G_3_1_PRO_PREVIEW;
|
||||
return Model.from_name(k);
|
||||
}
|
||||
|
||||
|
||||
@@ -2,7 +2,7 @@ import fs from "node:fs";
|
||||
import path from "node:path";
|
||||
import readline from "node:readline";
|
||||
import process from "node:process";
|
||||
import { mkdir, readFile, rename, writeFile } from "node:fs/promises";
|
||||
import { mkdir, readFile, writeFile } from "node:fs/promises";
|
||||
|
||||
import { fetchXArticle } from "./graphql.js";
|
||||
import { formatArticleMarkdown } from "./markdown.js";
|
||||
@@ -182,36 +182,32 @@ function sanitizeSlug(input: string): string {
|
||||
.slice(0, 120);
|
||||
}
|
||||
|
||||
function formatBackupTimestamp(date: Date = new Date()): string {
|
||||
const pad2 = (n: number) => String(n).padStart(2, "0");
|
||||
return `${date.getFullYear()}${pad2(date.getMonth() + 1)}${pad2(date.getDate())}-${pad2(date.getHours())}${pad2(
|
||||
date.getMinutes()
|
||||
)}${pad2(date.getSeconds())}`;
|
||||
}
|
||||
|
||||
async function backupDirIfExists(dir: string, log: (message: string) => void): Promise<void> {
|
||||
try {
|
||||
if (!fs.existsSync(dir)) return;
|
||||
const stat = fs.statSync(dir);
|
||||
if (!stat.isDirectory()) return;
|
||||
const backup = `${dir}-backup-${formatBackupTimestamp()}`;
|
||||
await rename(dir, backup);
|
||||
log(`[x-to-markdown] Existing directory moved to: ${backup}`);
|
||||
} catch (error) {
|
||||
throw new Error(
|
||||
`Failed to backup existing directory (${dir}): ${error instanceof Error ? error.message : String(error ?? "")}`
|
||||
);
|
||||
function extractContentSlug(markdown: string): string {
|
||||
const headingMatch = markdown.match(/^#\s+(.+)$/m);
|
||||
if (headingMatch?.[1]) {
|
||||
return sanitizeSlug(headingMatch[1].slice(0, 60)).toLowerCase();
|
||||
}
|
||||
}
|
||||
|
||||
function resolveDefaultOutputDir(slug: string): string {
|
||||
return path.resolve(process.cwd(), "x-to-markdown", slug);
|
||||
const lines = markdown.split("\n");
|
||||
let inFrontmatter = false;
|
||||
for (const line of lines) {
|
||||
if (line === "---") {
|
||||
inFrontmatter = !inFrontmatter;
|
||||
continue;
|
||||
}
|
||||
if (inFrontmatter) continue;
|
||||
const trimmed = line.trim();
|
||||
if (trimmed) {
|
||||
return sanitizeSlug(trimmed.slice(0, 60)).toLowerCase();
|
||||
}
|
||||
}
|
||||
return "untitled";
|
||||
}
|
||||
|
||||
async function resolveOutputPath(
|
||||
normalizedUrl: string,
|
||||
kind: "tweet" | "article",
|
||||
argsOutput: string | null,
|
||||
contentSlug: string,
|
||||
log: (message: string) => void
|
||||
): Promise<{ outputDir: string; markdownPath: string; slug: string }> {
|
||||
const articleId = kind === "article" ? parseArticleId(normalizedUrl) : null;
|
||||
@@ -222,15 +218,14 @@ async function resolveOutputPath(
|
||||
const idPart = articleId ?? tweetId ?? String(Date.now());
|
||||
const slug = userSlug ?? idPart;
|
||||
|
||||
const defaultFileName = kind === "article" ? `${idPart}.md` : `${idPart}.md`;
|
||||
const defaultFileName = `${idPart}.md`;
|
||||
|
||||
if (argsOutput) {
|
||||
const wantsDir = argsOutput.endsWith("/") || argsOutput.endsWith("\\");
|
||||
const resolved = path.resolve(argsOutput);
|
||||
try {
|
||||
if (wantsDir || (fs.existsSync(resolved) && fs.statSync(resolved).isDirectory())) {
|
||||
const outputDir = path.join(resolved, slug);
|
||||
await backupDirIfExists(outputDir, log);
|
||||
const outputDir = path.join(resolved, slug, contentSlug);
|
||||
await mkdir(outputDir, { recursive: true });
|
||||
return { outputDir, markdownPath: path.join(outputDir, defaultFileName), slug };
|
||||
}
|
||||
@@ -243,8 +238,7 @@ async function resolveOutputPath(
|
||||
return { outputDir, markdownPath: resolved, slug };
|
||||
}
|
||||
|
||||
const outputDir = resolveDefaultOutputDir(slug);
|
||||
await backupDirIfExists(outputDir, log);
|
||||
const outputDir = path.resolve(process.cwd(), "x-to-markdown", slug, contentSlug);
|
||||
await mkdir(outputDir, { recursive: true });
|
||||
return { outputDir, markdownPath: path.join(outputDir, defaultFileName), slug };
|
||||
}
|
||||
@@ -394,13 +388,15 @@ async function main(): Promise<void> {
|
||||
}
|
||||
|
||||
const kind = articleId ? ("article" as const) : ("tweet" as const);
|
||||
const { outputDir, markdownPath, slug } = await resolveOutputPath(normalizedUrl, kind, args.output, log);
|
||||
|
||||
let markdown =
|
||||
kind === "article" && articleId
|
||||
? await convertArticleToMarkdown(normalizedUrl, articleId, log)
|
||||
: await tweetToMarkdown(normalizedUrl, { log });
|
||||
|
||||
const contentSlug = extractContentSlug(markdown);
|
||||
const { outputDir, markdownPath, slug } = await resolveOutputPath(normalizedUrl, kind, args.output, contentSlug, log);
|
||||
|
||||
let mediaResult: LocalizeMarkdownMediaResult | null = null;
|
||||
|
||||
if (args.downloadMedia) {
|
||||
|
||||
@@ -117,11 +117,114 @@ function resolveEntityMediaLines(
|
||||
return lines;
|
||||
}
|
||||
|
||||
function buildMediaLinkMap(
|
||||
entityMap: ArticleContentState["entityMap"] | undefined
|
||||
): Map<number, string> {
|
||||
const map = new Map<number, string>();
|
||||
if (!entityMap) return map;
|
||||
|
||||
const mediaEntries: { idx: number; key: number }[] = [];
|
||||
const linkEntries: { key: number; url: string }[] = [];
|
||||
|
||||
for (const [idx, entry] of Object.entries(entityMap)) {
|
||||
const value = entry?.value;
|
||||
if (!value) continue;
|
||||
const key = parseInt(entry?.key ?? "", 10);
|
||||
if (isNaN(key)) continue;
|
||||
|
||||
if (value.type === "MEDIA" || value.type === "IMAGE") {
|
||||
mediaEntries.push({ idx: Number(idx), key });
|
||||
} else if (value.type === "LINK" && typeof value.data?.url === "string") {
|
||||
linkEntries.push({ key, url: value.data.url });
|
||||
}
|
||||
}
|
||||
|
||||
if (mediaEntries.length === 0 || linkEntries.length === 0) return map;
|
||||
|
||||
mediaEntries.sort((a, b) => a.key - b.key);
|
||||
linkEntries.sort((a, b) => a.key - b.key);
|
||||
|
||||
const pool = [...linkEntries];
|
||||
for (const media of mediaEntries) {
|
||||
if (pool.length === 0) break;
|
||||
let linkIdx = pool.findIndex((l) => l.key > media.key);
|
||||
if (linkIdx === -1) linkIdx = 0;
|
||||
const link = pool.splice(linkIdx, 1)[0]!;
|
||||
map.set(media.idx, link.url);
|
||||
}
|
||||
|
||||
return map;
|
||||
}
|
||||
|
||||
function renderInlineLinks(
|
||||
text: string,
|
||||
entityRanges: Array<{ key?: number; offset?: number; length?: number }>,
|
||||
entityMap: ArticleContentState["entityMap"] | undefined,
|
||||
mediaLinkMap: Map<number, string>
|
||||
): string {
|
||||
if (!entityMap || entityRanges.length === 0) return text;
|
||||
|
||||
const valid = entityRanges.filter(
|
||||
(r) =>
|
||||
typeof r.key === "number" &&
|
||||
typeof r.offset === "number" &&
|
||||
typeof r.length === "number" &&
|
||||
r.length > 0
|
||||
);
|
||||
if (valid.length === 0) return text;
|
||||
|
||||
const sorted = [...valid].sort((a, b) => (b.offset ?? 0) - (a.offset ?? 0));
|
||||
|
||||
let result = text;
|
||||
for (const range of sorted) {
|
||||
const offset = range.offset!;
|
||||
const length = range.length!;
|
||||
const key = range.key!;
|
||||
|
||||
const entry = entityMap[String(key)];
|
||||
const value = entry?.value;
|
||||
if (!value) continue;
|
||||
|
||||
let url: string | undefined;
|
||||
if (value.type === "LINK" && typeof value.data?.url === "string") {
|
||||
url = value.data.url;
|
||||
} else if (value.type === "MEDIA" || value.type === "IMAGE") {
|
||||
url = mediaLinkMap.get(key);
|
||||
}
|
||||
|
||||
if (!url) continue;
|
||||
|
||||
const linkText = result.slice(offset, offset + length);
|
||||
result =
|
||||
result.slice(0, offset) +
|
||||
`[${linkText}](${url})` +
|
||||
result.slice(offset + length);
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
function buildAtomicMediaQueue(
|
||||
article: ArticleEntity,
|
||||
usedUrls: Set<string>
|
||||
): string[] {
|
||||
const queue: string[] = [];
|
||||
for (const entity of article.media_entities ?? []) {
|
||||
const url = resolveMediaUrl(entity?.media_info);
|
||||
if (url && !usedUrls.has(url)) {
|
||||
queue.push(url);
|
||||
}
|
||||
}
|
||||
return queue;
|
||||
}
|
||||
|
||||
function renderContentBlocks(
|
||||
blocks: ArticleBlock[],
|
||||
entityMap: ArticleContentState["entityMap"] | undefined,
|
||||
mediaById: Map<string, string>,
|
||||
usedUrls: Set<string>
|
||||
usedUrls: Set<string>,
|
||||
atomicMediaQueue: string[],
|
||||
mediaLinkMap: Map<number, string>
|
||||
): string[] {
|
||||
const lines: string[] = [];
|
||||
let previousKind: "list" | "quote" | "heading" | "text" | "code" | "media" | null = null;
|
||||
@@ -157,7 +260,12 @@ function renderContentBlocks(
|
||||
|
||||
for (const block of blocks) {
|
||||
const type = typeof block?.type === "string" ? block.type : "unstyled";
|
||||
const text = typeof block?.text === "string" ? block.text : "";
|
||||
const rawText = typeof block?.text === "string" ? block.text : "";
|
||||
const ranges = Array.isArray(block.entityRanges) ? block.entityRanges : [];
|
||||
const text =
|
||||
type !== "atomic" && type !== "code-block"
|
||||
? renderInlineLinks(rawText, ranges, entityMap, mediaLinkMap)
|
||||
: rawText;
|
||||
|
||||
if (type === "code-block") {
|
||||
if (!inCodeBlock) {
|
||||
@@ -185,6 +293,12 @@ function renderContentBlocks(
|
||||
const mediaLines = collectMediaLines(block);
|
||||
if (mediaLines.length > 0) {
|
||||
pushBlock(mediaLines, "media");
|
||||
} else if (atomicMediaQueue.length > 0) {
|
||||
const url = atomicMediaQueue.shift()!;
|
||||
if (!usedUrls.has(url)) {
|
||||
usedUrls.add(url);
|
||||
pushBlock([``], "media");
|
||||
}
|
||||
}
|
||||
continue;
|
||||
}
|
||||
@@ -243,11 +357,6 @@ function renderContentBlocks(
|
||||
pushBlock([text], "text");
|
||||
break;
|
||||
}
|
||||
|
||||
const trailingMediaLines = collectMediaLines(block);
|
||||
if (trailingMediaLines.length > 0) {
|
||||
pushBlock(trailingMediaLines, "media");
|
||||
}
|
||||
}
|
||||
|
||||
if (inCodeBlock) {
|
||||
@@ -284,7 +393,9 @@ export function formatArticleMarkdown(article: unknown): FormatArticleResult {
|
||||
const blocks = candidate.content_state?.blocks;
|
||||
const entityMap = candidate.content_state?.entityMap;
|
||||
if (Array.isArray(blocks) && blocks.length > 0) {
|
||||
const rendered = renderContentBlocks(blocks, entityMap, mediaById, usedUrls);
|
||||
const atomicMediaQueue = buildAtomicMediaQueue(candidate, usedUrls);
|
||||
const mediaLinkMap = buildMediaLinkMap(entityMap);
|
||||
const rendered = renderContentBlocks(blocks, entityMap, mediaById, usedUrls, atomicMediaQueue, mediaLinkMap);
|
||||
if (rendered.length > 0) {
|
||||
if (lines.length > 0) lines.push("");
|
||||
lines.push(...rendered);
|
||||
|
||||
@@ -13,33 +13,28 @@ Official API-based image generation. Supports OpenAI, Google, DashScope (阿里
|
||||
1. `SKILL_DIR` = this SKILL.md file's directory
|
||||
2. Script path = `${SKILL_DIR}/scripts/main.ts`
|
||||
|
||||
## Preferences (EXTEND.md)
|
||||
## Step 0: Load Preferences ⛔ BLOCKING
|
||||
|
||||
Use Bash to check EXTEND.md existence (priority order):
|
||||
**CRITICAL**: This step MUST complete BEFORE any image generation. Do NOT skip or defer.
|
||||
|
||||
Check EXTEND.md existence (priority: project → user):
|
||||
|
||||
```bash
|
||||
# Check project-level first
|
||||
test -f .baoyu-skills/baoyu-image-gen/EXTEND.md && echo "project"
|
||||
|
||||
# Then user-level (cross-platform: $HOME works on macOS/Linux/WSL)
|
||||
test -f "$HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md" && echo "user"
|
||||
```
|
||||
|
||||
┌──────────────────────────────────────────────────┬───────────────────┐
|
||||
│ Path │ Location │
|
||||
├──────────────────────────────────────────────────┼───────────────────┤
|
||||
│ .baoyu-skills/baoyu-image-gen/EXTEND.md │ Project directory │
|
||||
├──────────────────────────────────────────────────┼───────────────────┤
|
||||
│ $HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md │ User home │
|
||||
└──────────────────────────────────────────────────┴───────────────────┘
|
||||
| Result | Action |
|
||||
|--------|--------|
|
||||
| Found | Load, parse, apply settings. If `default_model.[provider]` is null → ask model only (Flow 2) |
|
||||
| Not found | ⛔ Run first-time setup ([references/config/first-time-setup.md](references/config/first-time-setup.md)) → Save EXTEND.md → Then continue |
|
||||
|
||||
┌───────────┬───────────────────────────────────────────────────────────────────────────┐
|
||||
│ Result │ Action │
|
||||
├───────────┼───────────────────────────────────────────────────────────────────────────┤
|
||||
│ Found │ Read, parse, apply settings │
|
||||
├───────────┼───────────────────────────────────────────────────────────────────────────┤
|
||||
│ Not found │ Use defaults │
|
||||
└───────────┴───────────────────────────────────────────────────────────────────────────┘
|
||||
**CRITICAL**: If not found, complete the full setup (provider + model + quality + save location) using AskUserQuestion BEFORE generating any images. Generation is BLOCKED until EXTEND.md is created.
|
||||
|
||||
| Path | Location |
|
||||
|------|----------|
|
||||
| `.baoyu-skills/baoyu-image-gen/EXTEND.md` | Project directory |
|
||||
| `$HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md` | User home |
|
||||
|
||||
**EXTEND.md Supports**: Default provider | Default quality | Default aspect ratio | Default image size | Default models
|
||||
|
||||
@@ -87,12 +82,12 @@ npx -y bun ${SKILL_DIR}/scripts/main.ts --prompt "A cat" --image out.png --provi
|
||||
| `--promptfiles <files...>` | Read prompt from files (concatenated) |
|
||||
| `--image <path>` | Output image path (required) |
|
||||
| `--provider google\|openai\|dashscope\|replicate` | Force provider (default: google) |
|
||||
| `--model <id>`, `-m` | Model ID (`--ref` with OpenAI requires GPT Image model, e.g. `gpt-image-1.5`) |
|
||||
| `--model <id>`, `-m` | Model ID (Google: `gemini-3-pro-image-preview`, `gemini-3.1-flash-image-preview`; OpenAI: `gpt-image-1.5`) |
|
||||
| `--ar <ratio>` | Aspect ratio (e.g., `16:9`, `1:1`, `4:3`) |
|
||||
| `--size <WxH>` | Size (e.g., `1024x1024`) |
|
||||
| `--quality normal\|2k` | Quality preset (default: 2k) |
|
||||
| `--imageSize 1K\|2K\|4K` | Image size for Google (default: from quality) |
|
||||
| `--ref <files...>` | Reference images. Supported by Google multimodal and OpenAI edits (GPT Image models). If provider omitted: Google first, then OpenAI |
|
||||
| `--ref <files...>` | Reference images. Supported by Google multimodal (`gemini-3-pro-image-preview`, `gemini-3-flash-preview`, `gemini-3.1-flash-image-preview`) and OpenAI edits (GPT Image models). If provider omitted: Google first, then OpenAI |
|
||||
| `--n <count>` | Number of images |
|
||||
| `--json` | JSON output |
|
||||
|
||||
@@ -194,7 +189,7 @@ Supported: `1:1`, `16:9`, `9:16`, `4:3`, `3:4`, `2.35:1`
|
||||
- Missing API key → error with setup instructions
|
||||
- Generation failure → auto-retry once
|
||||
- Invalid aspect ratio → warning, proceed with default
|
||||
- Reference images with unsupported provider/model → error with fix hint (switch to Google multimodal or OpenAI GPT Image edits)
|
||||
- Reference images with unsupported provider/model → error with fix hint (switch to Google multimodal: `gemini-3-pro-image-preview`, `gemini-3.1-flash-image-preview`; or OpenAI GPT Image edits)
|
||||
|
||||
## Extension Support
|
||||
|
||||
|
||||
@@ -0,0 +1,197 @@
|
||||
---
|
||||
name: first-time-setup
|
||||
description: First-time setup and default model selection flow for baoyu-image-gen
|
||||
---
|
||||
|
||||
# First-Time Setup
|
||||
|
||||
## Overview
|
||||
|
||||
Triggered when:
|
||||
1. No EXTEND.md found → full setup (provider + model + preferences)
|
||||
2. EXTEND.md found but `default_model.[provider]` is null → model selection only
|
||||
|
||||
## Setup Flow
|
||||
|
||||
```
|
||||
No EXTEND.md found EXTEND.md found, model null
|
||||
│ │
|
||||
▼ ▼
|
||||
┌─────────────────────┐ ┌──────────────────────┐
|
||||
│ AskUserQuestion │ │ AskUserQuestion │
|
||||
│ (full setup) │ │ (model only) │
|
||||
└─────────────────────┘ └──────────────────────┘
|
||||
│ │
|
||||
▼ ▼
|
||||
┌─────────────────────┐ ┌──────────────────────┐
|
||||
│ Create EXTEND.md │ │ Update EXTEND.md │
|
||||
└─────────────────────┘ └──────────────────────┘
|
||||
│ │
|
||||
▼ ▼
|
||||
Continue Continue
|
||||
```
|
||||
|
||||
## Flow 1: No EXTEND.md (Full Setup)
|
||||
|
||||
**Language**: Use user's input language or saved language preference.
|
||||
|
||||
Use AskUserQuestion with ALL questions in ONE call:
|
||||
|
||||
### Question 1: Default Provider
|
||||
|
||||
```yaml
|
||||
header: "Provider"
|
||||
question: "Default image generation provider?"
|
||||
options:
|
||||
- label: "Google (Recommended)"
|
||||
description: "Gemini multimodal - high quality, reference images, flexible sizes"
|
||||
- label: "OpenAI"
|
||||
description: "GPT Image - consistent quality, reliable output"
|
||||
- label: "DashScope"
|
||||
description: "Alibaba Cloud - z-image-turbo, good for Chinese content"
|
||||
- label: "Replicate"
|
||||
description: "Community models - nano-banana-pro, flexible model selection"
|
||||
```
|
||||
|
||||
### Question 2: Default Google Model
|
||||
|
||||
Only show if user selected Google or auto-detect (no explicit provider).
|
||||
|
||||
```yaml
|
||||
header: "Google Model"
|
||||
question: "Default Google image generation model?"
|
||||
options:
|
||||
- label: "gemini-3-pro-image-preview (Recommended)"
|
||||
description: "Highest quality, best for production use"
|
||||
- label: "gemini-3.1-flash-image-preview"
|
||||
description: "Fast generation, good quality, lower cost"
|
||||
- label: "gemini-3-flash-preview"
|
||||
description: "Fast generation, balanced quality and speed"
|
||||
```
|
||||
|
||||
### Question 3: Default Quality
|
||||
|
||||
```yaml
|
||||
header: "Quality"
|
||||
question: "Default image quality?"
|
||||
options:
|
||||
- label: "2k (Recommended)"
|
||||
description: "2048px - covers, illustrations, infographics"
|
||||
- label: "normal"
|
||||
description: "1024px - quick previews, drafts"
|
||||
```
|
||||
|
||||
### Question 4: Save Location
|
||||
|
||||
```yaml
|
||||
header: "Save"
|
||||
question: "Where to save preferences?"
|
||||
options:
|
||||
- label: "Project (Recommended)"
|
||||
description: ".baoyu-skills/ (this project only)"
|
||||
- label: "User"
|
||||
description: "~/.baoyu-skills/ (all projects)"
|
||||
```
|
||||
|
||||
### Save Locations
|
||||
|
||||
| Choice | Path | Scope |
|
||||
|--------|------|-------|
|
||||
| Project | `.baoyu-skills/baoyu-image-gen/EXTEND.md` | Current project |
|
||||
| User | `$HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md` | All projects |
|
||||
|
||||
### EXTEND.md Template
|
||||
|
||||
```yaml
|
||||
---
|
||||
version: 1
|
||||
default_provider: [selected provider or null]
|
||||
default_quality: [selected quality]
|
||||
default_aspect_ratio: null
|
||||
default_image_size: null
|
||||
default_model:
|
||||
google: [selected google model or null]
|
||||
openai: null
|
||||
dashscope: null
|
||||
replicate: null
|
||||
---
|
||||
```
|
||||
|
||||
## Flow 2: EXTEND.md Exists, Model Null
|
||||
|
||||
When EXTEND.md exists but `default_model.[current_provider]` is null, ask ONLY the model question for the current provider.
|
||||
|
||||
### Google Model Selection
|
||||
|
||||
```yaml
|
||||
header: "Google Model"
|
||||
question: "Choose a default Google image generation model?"
|
||||
options:
|
||||
- label: "gemini-3-pro-image-preview (Recommended)"
|
||||
description: "Highest quality, best for production use"
|
||||
- label: "gemini-3.1-flash-image-preview"
|
||||
description: "Fast generation, good quality, lower cost"
|
||||
- label: "gemini-3-flash-preview"
|
||||
description: "Fast generation, balanced quality and speed"
|
||||
```
|
||||
|
||||
### OpenAI Model Selection
|
||||
|
||||
```yaml
|
||||
header: "OpenAI Model"
|
||||
question: "Choose a default OpenAI image generation model?"
|
||||
options:
|
||||
- label: "gpt-image-1.5 (Recommended)"
|
||||
description: "Latest GPT Image model, high quality"
|
||||
- label: "gpt-image-1"
|
||||
description: "Previous generation GPT Image model"
|
||||
```
|
||||
|
||||
### DashScope Model Selection
|
||||
|
||||
```yaml
|
||||
header: "DashScope Model"
|
||||
question: "Choose a default DashScope image generation model?"
|
||||
options:
|
||||
- label: "z-image-turbo (Recommended)"
|
||||
description: "Fast generation, good quality"
|
||||
- label: "z-image-ultra"
|
||||
description: "Higher quality, slower generation"
|
||||
```
|
||||
|
||||
### Replicate Model Selection
|
||||
|
||||
```yaml
|
||||
header: "Replicate Model"
|
||||
question: "Choose a default Replicate image generation model?"
|
||||
options:
|
||||
- label: "google/nano-banana-pro (Recommended)"
|
||||
description: "Google's fast image model on Replicate"
|
||||
- label: "google/nano-banana"
|
||||
description: "Google's base image model on Replicate"
|
||||
```
|
||||
|
||||
### Update EXTEND.md
|
||||
|
||||
After user selects a model:
|
||||
|
||||
1. Read existing EXTEND.md
|
||||
2. If `default_model:` section exists → update the provider-specific key
|
||||
3. If `default_model:` section missing → add the full section:
|
||||
|
||||
```yaml
|
||||
default_model:
|
||||
google: [value or null]
|
||||
openai: [value or null]
|
||||
dashscope: [value or null]
|
||||
replicate: [value or null]
|
||||
```
|
||||
|
||||
Only set the selected provider's model; leave others as their current value or null.
|
||||
|
||||
## After Setup
|
||||
|
||||
1. Create directory if needed
|
||||
2. Write/update EXTEND.md with frontmatter
|
||||
3. Confirm: "Preferences saved to [path]"
|
||||
4. Continue with image generation
|
||||
@@ -20,7 +20,7 @@ default_aspect_ratio: null # "16:9"|"1:1"|"4:3"|"3:4"|"2.35:1"|null
|
||||
default_image_size: null # 1K|2K|4K|null (Google only, overrides quality)
|
||||
|
||||
default_model:
|
||||
google: null # e.g., "gemini-3-pro-image-preview"
|
||||
google: null # e.g., "gemini-3-pro-image-preview", "gemini-3.1-flash-image-preview"
|
||||
openai: null # e.g., "gpt-image-1.5"
|
||||
dashscope: null # e.g., "z-image-turbo"
|
||||
replicate: null # e.g., "google/nano-banana-pro"
|
||||
|
||||
@@ -1,9 +1,17 @@
|
||||
import path from "node:path";
|
||||
import { readFile } from "node:fs/promises";
|
||||
import { execSync } from "node:child_process";
|
||||
import type { CliArgs } from "../types";
|
||||
|
||||
const GOOGLE_MULTIMODAL_MODELS = ["gemini-3-pro-image-preview", "gemini-3-flash-preview"];
|
||||
const GOOGLE_IMAGEN_MODELS = ["imagen-3.0-generate-002", "imagen-3.0-generate-001"];
|
||||
const GOOGLE_MULTIMODAL_MODELS = [
|
||||
"gemini-3-pro-image-preview",
|
||||
"gemini-3-flash-preview",
|
||||
"gemini-3.1-flash-image-preview",
|
||||
];
|
||||
const GOOGLE_IMAGEN_MODELS = [
|
||||
"imagen-3.0-generate-002",
|
||||
"imagen-3.0-generate-001",
|
||||
];
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return process.env.GOOGLE_IMAGE_MODEL || "gemini-3-pro-image-preview";
|
||||
@@ -33,7 +41,8 @@ function getGoogleImageSize(args: CliArgs): "1K" | "2K" | "4K" {
|
||||
}
|
||||
|
||||
function getGoogleBaseUrl(): string {
|
||||
const base = process.env.GOOGLE_BASE_URL || "https://generativelanguage.googleapis.com";
|
||||
const base =
|
||||
process.env.GOOGLE_BASE_URL || "https://generativelanguage.googleapis.com";
|
||||
return base.replace(/\/+$/g, "");
|
||||
}
|
||||
|
||||
@@ -49,11 +58,46 @@ function toModelPath(model: string): string {
|
||||
return `models/${modelId}`;
|
||||
}
|
||||
|
||||
async function postGoogleJson<T>(pathname: string, body: unknown): Promise<T> {
|
||||
const apiKey = getGoogleApiKey();
|
||||
if (!apiKey) throw new Error("GOOGLE_API_KEY or GEMINI_API_KEY is required");
|
||||
function getHttpProxy(): string | null {
|
||||
return (
|
||||
process.env.https_proxy ||
|
||||
process.env.HTTPS_PROXY ||
|
||||
process.env.http_proxy ||
|
||||
process.env.HTTP_PROXY ||
|
||||
process.env.ALL_PROXY ||
|
||||
null
|
||||
);
|
||||
}
|
||||
|
||||
const res = await fetch(buildGoogleUrl(pathname), {
|
||||
async function postGoogleJsonViaCurl<T>(
|
||||
url: string,
|
||||
apiKey: string,
|
||||
body: unknown,
|
||||
): Promise<T> {
|
||||
const proxy = getHttpProxy();
|
||||
const bodyStr = JSON.stringify(body);
|
||||
const proxyArgs = proxy ? `-x "${proxy}"` : "";
|
||||
|
||||
const result = execSync(
|
||||
`curl -s --connect-timeout 30 --max-time 300 ${proxyArgs} "${url}" -H "Content-Type: application/json" -H "x-goog-api-key: ${apiKey}" -d @-`,
|
||||
{ input: bodyStr, maxBuffer: 100 * 1024 * 1024, timeout: 310000 },
|
||||
);
|
||||
|
||||
const parsed = JSON.parse(result.toString()) as any;
|
||||
if (parsed.error) {
|
||||
throw new Error(
|
||||
`Google API error (${parsed.error.code}): ${parsed.error.message}`,
|
||||
);
|
||||
}
|
||||
return parsed as T;
|
||||
}
|
||||
|
||||
async function postGoogleJsonViaFetch<T>(
|
||||
url: string,
|
||||
apiKey: string,
|
||||
body: unknown,
|
||||
): Promise<T> {
|
||||
const res = await fetch(url, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
@@ -70,7 +114,30 @@ async function postGoogleJson<T>(pathname: string, body: unknown): Promise<T> {
|
||||
return (await res.json()) as T;
|
||||
}
|
||||
|
||||
function buildPromptWithAspect(prompt: string, ar: string | null, quality: CliArgs["quality"]): string {
|
||||
async function postGoogleJson<T>(pathname: string, body: unknown): Promise<T> {
|
||||
const apiKey = getGoogleApiKey();
|
||||
if (!apiKey) throw new Error("GOOGLE_API_KEY or GEMINI_API_KEY is required");
|
||||
|
||||
const url = buildGoogleUrl(pathname);
|
||||
const proxy = getHttpProxy();
|
||||
|
||||
// When an HTTP proxy is detected, use curl instead of fetch.
|
||||
// Bun's fetch has a known issue where long-lived connections through
|
||||
// HTTP proxies get their sockets closed unexpectedly, causing image
|
||||
// generation requests to fail with "socket connection was closed
|
||||
// unexpectedly". Using curl as the HTTP client works around this.
|
||||
if (proxy) {
|
||||
return postGoogleJsonViaCurl<T>(url, apiKey, body);
|
||||
}
|
||||
|
||||
return postGoogleJsonViaFetch<T>(url, apiKey, body);
|
||||
}
|
||||
|
||||
function buildPromptWithAspect(
|
||||
prompt: string,
|
||||
ar: string | null,
|
||||
quality: CliArgs["quality"],
|
||||
): string {
|
||||
let result = prompt;
|
||||
if (ar) {
|
||||
result += ` Aspect ratio: ${ar}.`;
|
||||
@@ -86,7 +153,9 @@ function addAspectRatioToPrompt(prompt: string, ar: string | null): string {
|
||||
return `${prompt} Aspect ratio: ${ar}.`;
|
||||
}
|
||||
|
||||
async function readImageAsBase64(p: string): Promise<{ data: string; mimeType: string }> {
|
||||
async function readImageAsBase64(
|
||||
p: string,
|
||||
): Promise<{ data: string; mimeType: string }> {
|
||||
const buf = await readFile(p);
|
||||
const ext = path.extname(p).toLowerCase();
|
||||
let mimeType = "image/png";
|
||||
@@ -97,7 +166,9 @@ async function readImageAsBase64(p: string): Promise<{ data: string; mimeType: s
|
||||
}
|
||||
|
||||
function extractInlineImageData(response: {
|
||||
candidates?: Array<{ content?: { parts?: Array<{ inlineData?: { data?: string } }> } }>;
|
||||
candidates?: Array<{
|
||||
content?: { parts?: Array<{ inlineData?: { data?: string } }> };
|
||||
}>;
|
||||
}): string | null {
|
||||
for (const candidate of response.candidates || []) {
|
||||
for (const part of candidate.content?.parts || []) {
|
||||
@@ -112,16 +183,21 @@ function extractPredictedImageData(response: {
|
||||
predictions?: Array<any>;
|
||||
generatedImages?: Array<any>;
|
||||
}): string | null {
|
||||
const candidates = [...(response.predictions || []), ...(response.generatedImages || [])];
|
||||
const candidates = [
|
||||
...(response.predictions || []),
|
||||
...(response.generatedImages || []),
|
||||
];
|
||||
for (const candidate of candidates) {
|
||||
if (!candidate || typeof candidate !== "object") continue;
|
||||
if (typeof candidate.imageBytes === "string") return candidate.imageBytes;
|
||||
if (typeof candidate.bytesBase64Encoded === "string") return candidate.bytesBase64Encoded;
|
||||
if (typeof candidate.bytesBase64Encoded === "string")
|
||||
return candidate.bytesBase64Encoded;
|
||||
if (typeof candidate.data === "string") return candidate.data;
|
||||
const image = candidate.image;
|
||||
if (image && typeof image === "object") {
|
||||
if (typeof image.imageBytes === "string") return image.imageBytes;
|
||||
if (typeof image.bytesBase64Encoded === "string") return image.bytesBase64Encoded;
|
||||
if (typeof image.bytesBase64Encoded === "string")
|
||||
return image.bytesBase64Encoded;
|
||||
if (typeof image.data === "string") return image.data;
|
||||
}
|
||||
}
|
||||
@@ -131,10 +207,13 @@ function extractPredictedImageData(response: {
|
||||
async function generateWithGemini(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs
|
||||
args: CliArgs,
|
||||
): Promise<Uint8Array> {
|
||||
const promptWithAspect = addAspectRatioToPrompt(prompt, args.aspectRatio);
|
||||
const parts: Array<{ text?: string; inlineData?: { data: string; mimeType: string } }> = [];
|
||||
const parts: Array<{
|
||||
text?: string;
|
||||
inlineData?: { data: string; mimeType: string };
|
||||
}> = [];
|
||||
for (const refPath of args.referenceImages) {
|
||||
const { data, mimeType } = await readImageAsBase64(refPath);
|
||||
parts.push({ inlineData: { data, mimeType } });
|
||||
@@ -147,7 +226,9 @@ async function generateWithGemini(
|
||||
|
||||
console.log("Generating image with Gemini...", imageConfig);
|
||||
const response = await postGoogleJson<{
|
||||
candidates?: Array<{ content?: { parts?: Array<{ inlineData?: { data?: string } }> } }>;
|
||||
candidates?: Array<{
|
||||
content?: { parts?: Array<{ inlineData?: { data?: string } }> };
|
||||
}>;
|
||||
}>(`${toModelPath(model)}:generateContent`, {
|
||||
contents: [
|
||||
{
|
||||
@@ -171,12 +252,18 @@ async function generateWithGemini(
|
||||
async function generateWithImagen(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs
|
||||
args: CliArgs,
|
||||
): Promise<Uint8Array> {
|
||||
const fullPrompt = buildPromptWithAspect(prompt, args.aspectRatio, args.quality);
|
||||
const fullPrompt = buildPromptWithAspect(
|
||||
prompt,
|
||||
args.aspectRatio,
|
||||
args.quality,
|
||||
);
|
||||
const imageSize = getGoogleImageSize(args);
|
||||
if (imageSize === "4K") {
|
||||
console.error("Warning: Imagen models do not support 4K imageSize, using 2K instead.");
|
||||
console.error(
|
||||
"Warning: Imagen models do not support 4K imageSize, using 2K instead.",
|
||||
);
|
||||
}
|
||||
|
||||
const parameters: Record<string, unknown> = {
|
||||
@@ -212,12 +299,12 @@ async function generateWithImagen(
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs
|
||||
args: CliArgs,
|
||||
): Promise<Uint8Array> {
|
||||
if (isGoogleImagen(model)) {
|
||||
if (args.referenceImages.length > 0) {
|
||||
throw new Error(
|
||||
"Reference images are not supported with Imagen models. Use gemini-3-pro-image-preview or gemini-3-flash-preview."
|
||||
"Reference images are not supported with Imagen models. Use gemini-3-pro-image-preview, gemini-3-flash-preview, or gemini-3.1-flash-image-preview.",
|
||||
);
|
||||
}
|
||||
return generateWithImagen(prompt, model, args);
|
||||
@@ -225,7 +312,7 @@ export async function generateImage(
|
||||
|
||||
if (!isGoogleMultimodal(model) && args.referenceImages.length > 0) {
|
||||
throw new Error(
|
||||
"Reference images are only supported with Gemini multimodal models. Use gemini-3-pro-image-preview or gemini-3-flash-preview."
|
||||
"Reference images are only supported with Gemini multimodal models. Use gemini-3-pro-image-preview, gemini-3-flash-preview, or gemini-3.1-flash-image-preview.",
|
||||
);
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user