feat: generate page description with llm script #2368
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WalkthroughAdds a CLI to generate SEO-focused page descriptions: new script Pre-merge checks (2 passed, 1 warning)❌ Failed checks (1 warning)
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ItzNotABug
requested changes
Sep 5, 2025
scripts/llm-generate-description.ts
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| previousAttempt | ||
| }: { | ||
| articleText: string; | ||
| frontmatterAttributes: Record<string, any>; |
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can we avoid any if possible? Our fm vals are mostly string, date or integers at max.
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
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Actionable comments posted: 7
🧹 Nitpick comments (3)
svelte.config.js (1)
8-25: Good centralization; consider moving schema to a standalone module to avoid loading SvelteKit config at runtime.Importing
svelte.config.jsfrom scripts will evaluate adapters and other config. ExtractingmarkdocSchemato e.g.src/markdoc/schema.jsreduces coupling and speeds CLIs.Example (new file):
// src/markdoc/schema.js import { dirname, join } from 'path'; import { fileURLToPath } from 'url'; const absolute = (p) => join(dirname(fileURLToPath(import.meta.url)), '..', '..', p); export const markdocSchema = { generateSchema: true, nodes: absolute('src/markdoc/nodes/_Module.svelte'), tags: absolute('src/markdoc/tags/_Module.svelte'), partials: absolute('src/partials'), layouts: { default: absolute('src/markdoc/layouts/Article.svelte'), article: absolute('src/markdoc/layouts/Article.svelte'), tutorial: absolute('src/markdoc/layouts/Tutorial.svelte'), post: absolute('src/markdoc/layouts/Post.svelte'), partner: absolute('src/markdoc/layouts/Partner.svelte'), author: absolute('src/markdoc/layouts/Author.svelte'), category: absolute('src/markdoc/layouts/Category.svelte'), policy: absolute('src/markdoc/layouts/Policy.svelte'), changelog: absolute('src/markdoc/layouts/Changelog.svelte'), integration: absolute('src/markdoc/layouts/Integration.svelte') } };Then:
- In
svelte.config.js:import { markdocSchema } from './src/markdoc/schema.js'- In the script:
import { markdocSchema } from '../src/markdoc/schema.js'scripts/llm-generate-description.ts (2)
139-149: Include character count in the final output to match logs/PR summary.Apply:
- console.log(`Generated description:\n\n${description}\n`); + console.log(`Generated description (${characterCount} characters):\n\n${description}\n`);
31-77: Optional: tighten the prompt for single-line meta output.Ask for a single line, no surrounding quotes, and avoid trailing period if already at limit.
Apply:
- Output must be worthy of being used as a meta description. + - Output must be a single line with no surrounding quotes. + - Prefer <= ${MAX_CHARS} characters; do not exceed. If needed, omit trailing punctuation to fit.
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pnpm-lock.yamlis excluded by!**/pnpm-lock.yaml
📒 Files selected for processing (3)
package.json(2 hunks)scripts/llm-generate-description.ts(1 hunks)svelte.config.js(1 hunks)
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- GitHub Check: tests
🔇 Additional comments (4)
svelte.config.js (1)
33-33: LGTM: reusing the centralized schema in preprocess.package.json (2)
16-16: Script entry is correct; note CLI will currently no-op due to a direct-execution check bug in the script.See suggested fix in
scripts/llm-generate-description.tsto makepnpm run generate:page-descriptionactually invokemain().
67-73: AI/LLM deps added as devDependencies — confirm install context.If this CLI is ever run in a prod/CI environment with
--prod, these won’t be installed. If that’s intended, fine; otherwise promote todependencies.scripts/llm-generate-description.ts (1)
31-33: Model ID validated
“gpt-5-mini” is supported by@ai-sdk/openaiand is the recommended compact option for short SEO copy.
scripts/llm-generate-description.ts
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Comment on lines
14
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16
| if (!process.env.OPENAI_API_KEY) { | ||
| throw new Error('OPENAI_API_KEY is not set'); | ||
| } |
There was a problem hiding this comment.
Do not throw at import-time if OPENAI_API_KEY is missing.
Top-level throw breaks importing the module (tests, tooling). Validate inside main() (or right before the LLM call) instead.
Apply:
-if (!process.env.OPENAI_API_KEY) {
- throw new Error('OPENAI_API_KEY is not set');
-}And add after the file-path check in main():
if (!filePathArg) {
throw new Error('File path is required');
}
+ if (!process.env.OPENAI_API_KEY) {
+ throw new Error('OPENAI_API_KEY is required');
+ }🤖 Prompt for AI Agents
In scripts/llm-generate-description.ts around lines 14-16, do not throw at
module import when OPENAI_API_KEY is missing; move the environment validation
into main() (or right before the LLM call) so importing the module
(tests/tooling) won't crash. Remove the top-level throw and instead add a check
after the file-path check in main(): if OPENAI_API_KEY is missing, log a clear
error and throw or exit there before any LLM calls so the module loads safely
but runtime fails fast when actually executed.
Comment on lines
18
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29
| async function generateDescription({ | ||
| articleText, | ||
| frontmatterAttributes, | ||
| previousAttempt | ||
| }: { | ||
| articleText: string; | ||
| frontmatterAttributes: Record<string, any>; | ||
| previousAttempt?: { | ||
| text: string; | ||
| characterCount: number; | ||
| }; | ||
| }) { |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Unbounded recursion risk; cap attempts.
Add an attempt counter with a sane max; fall back by truncating on last attempt.
Apply:
-async function generateDescription({
+const MAX_CHARS = 250;
+const MAX_ATTEMPTS = 3;
+
+async function generateDescription({
articleText,
frontmatterAttributes,
- previousAttempt
+ previousAttempt,
+ attempt = 1
}: {
articleText: string;
frontmatterAttributes: Record<string, any>;
previousAttempt?: {
text: string;
characterCount: number;
- };
+ };
+ attempt?: number;
}) {And update the retry block:
- if (characterCount > 250) {
- console.log(`Character count is too long (${characterCount}), generating again...`);
- return generateDescription({
+ if (characterCount > MAX_CHARS) {
+ if (attempt >= MAX_ATTEMPTS) {
+ const clipped = normalized.slice(0, MAX_CHARS);
+ return { description: clipped, characterCount: MAX_CHARS };
+ }
+ console.log(`Over limit (${characterCount} chars). Regenerating (attempt ${attempt + 1}/${MAX_ATTEMPTS})...`);
+ return generateDescription({
articleText,
frontmatterAttributes,
previousAttempt: {
- text,
- characterCount
- }
+ text: normalized,
+ characterCount
+ },
+ attempt: attempt + 1
});
}🤖 Prompt for AI Agents
In scripts/llm-generate-description.ts around lines 18-29, the
generateDescription function lacks a bounded retry/recursion mechanism; add an
attempt counter parameter (e.g., attempt = 0) and a MAX_ATTEMPTS constant (e.g.,
3-5). On each retry increment attempt and, if attempt >= MAX_ATTEMPTS, stop
recursing and fall back by truncating articleText to a safe length (or use
previousAttempt.text truncated) before making the final call; otherwise retry
normally. Update the retry block to pass attempt + 1 on recursive calls and to
perform the truncation fallback when the max is reached. Ensure types/signature
reflect the new optional attempt parameter.
scripts/llm-generate-description.ts
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Comment on lines
114
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121
| // Use JSDOM to parse the HTML and extract text content from <article> | ||
| const dom = new JSDOM(html); | ||
| const articleElement = dom.window.document.querySelector('article'); | ||
| const articleText = articleElement ? articleElement.textContent : ''; | ||
| return { | ||
| articleText, | ||
| frontmatterAttributes: frontmatter.attributes | ||
| }; |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Make article extraction resilient and normalized.
Fallback to main/body, and collapse whitespace.
Apply:
- const dom = new JSDOM(html);
- const articleElement = dom.window.document.querySelector('article');
- const articleText = articleElement ? articleElement.textContent : '';
+ const dom = new JSDOM(html);
+ const articleElement =
+ dom.window.document.querySelector('article, main') ?? dom.window.document.body;
+ const articleText = (articleElement?.textContent || '').replace(/\s+/g, ' ').trim();📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| // Use JSDOM to parse the HTML and extract text content from <article> | |
| const dom = new JSDOM(html); | |
| const articleElement = dom.window.document.querySelector('article'); | |
| const articleText = articleElement ? articleElement.textContent : ''; | |
| return { | |
| articleText, | |
| frontmatterAttributes: frontmatter.attributes | |
| }; | |
| // Use JSDOM to parse the HTML and extract text content from <article> or <main> | |
| const dom = new JSDOM(html); | |
| const articleElement = | |
| dom.window.document.querySelector('article, main') ?? dom.window.document.body; | |
| const articleText = (articleElement?.textContent || '').replace(/\s+/g, ' ').trim(); | |
| return { | |
| articleText, | |
| frontmatterAttributes: frontmatter.attributes | |
| }; |
🤖 Prompt for AI Agents
In scripts/llm-generate-description.ts around lines 114 to 121, the article
extraction should be more resilient and normalized: update the DOM query to
fallback to <main> then <body> when <article> is missing, extract textContent
from the first available element, collapse consecutive whitespace into single
spaces and trim leading/trailing whitespace, and return that normalized string
as articleText along with frontmatter.attributes.
scripts/llm-generate-description.ts
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Comment on lines
124
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137
| export async function generateDescriptionForDocsPage(filePath: string) { | ||
| const resolvedPath = path.resolve(filePath); | ||
| const { articleText, frontmatterAttributes } = await getDocPageContent(resolvedPath); | ||
|
|
||
| if (!articleText || !frontmatterAttributes) { | ||
| throw new Error('Article text or frontmatter attributes are undefined'); | ||
| } | ||
|
|
||
| const { description, characterCount } = await generateDescription({ | ||
| articleText, | ||
| frontmatterAttributes | ||
| }); | ||
| return { description, characterCount }; | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Gracefully handle empty article text; use frontmatter as fallback.
Avoid hard failure when the page has little/no static text.
Apply:
- const { articleText, frontmatterAttributes } = await getDocPageContent(resolvedPath);
-
- if (!articleText || !frontmatterAttributes) {
- throw new Error('Article text or frontmatter attributes are undefined');
- }
-
- const { description, characterCount } = await generateDescription({
- articleText,
- frontmatterAttributes
- });
+ const { articleText, frontmatterAttributes } = await getDocPageContent(resolvedPath);
+ const pageContent =
+ articleText ||
+ [frontmatterAttributes?.title, frontmatterAttributes?.description, frontmatterAttributes?.summary]
+ .filter(Boolean)
+ .join(': ');
+ if (!pageContent) {
+ throw new Error('No content available to generate a description');
+ }
+ const { description, characterCount } = await generateDescription({
+ articleText: pageContent,
+ frontmatterAttributes
+ });📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| export async function generateDescriptionForDocsPage(filePath: string) { | |
| const resolvedPath = path.resolve(filePath); | |
| const { articleText, frontmatterAttributes } = await getDocPageContent(resolvedPath); | |
| if (!articleText || !frontmatterAttributes) { | |
| throw new Error('Article text or frontmatter attributes are undefined'); | |
| } | |
| const { description, characterCount } = await generateDescription({ | |
| articleText, | |
| frontmatterAttributes | |
| }); | |
| return { description, characterCount }; | |
| } | |
| export async function generateDescriptionForDocsPage(filePath: string) { | |
| const resolvedPath = path.resolve(filePath); | |
| const { articleText, frontmatterAttributes } = await getDocPageContent(resolvedPath); | |
| const pageContent = | |
| articleText || | |
| [frontmatterAttributes?.title, frontmatterAttributes?.description, frontmatterAttributes?.summary] | |
| .filter(Boolean) | |
| .join(': '); | |
| if (!pageContent) { | |
| throw new Error('No content available to generate a description'); | |
| } | |
| const { description, characterCount } = await generateDescription({ | |
| articleText: pageContent, | |
| frontmatterAttributes | |
| }); | |
| return { description, characterCount }; | |
| } |
🤖 Prompt for AI Agents
In scripts/llm-generate-description.ts around lines 124 to 137, the function
currently throws if articleText or frontmatterAttributes are missing; change it
to gracefully handle empty article text by using frontmatter as a fallback:
check if articleText is falsy or trim().length === 0, and if so construct a
fallback source (prefer frontmatterAttributes.description, else
frontmatterAttributes.title and any summary fields joined) and pass that into
generateDescription; only throw if frontmatterAttributes is also entirely
missing and no fallback can be constructed, then return the resulting
description and characterCount as before.
scripts/llm-generate-description.ts
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Comment on lines
151
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159
| // Runs only if invoked via CLI | ||
| // @ts-ignore | ||
| const isDirect = import.meta.url === pathToFileURL(process.argv[1]).href; | ||
| if (isDirect) { | ||
| main().catch((err) => { | ||
| console.error(err); | ||
| process.exit(1); | ||
| }); | ||
| } |
There was a problem hiding this comment.
Fix direct-execution check for tsx.
With pnpm run generate:page-description (tsx), process.argv[1] is the tsx binary, so main() never runs.
Apply:
-const isDirect = import.meta.url === pathToFileURL(process.argv[1]).href;
+const scriptPath = process.argv[1]?.endsWith('tsx') ? process.argv[2] : process.argv[1];
+const isDirect =
+ !!scriptPath && import.meta.url === pathToFileURL(path.resolve(scriptPath)).href;📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| // Runs only if invoked via CLI | |
| // @ts-ignore | |
| const isDirect = import.meta.url === pathToFileURL(process.argv[1]).href; | |
| if (isDirect) { | |
| main().catch((err) => { | |
| console.error(err); | |
| process.exit(1); | |
| }); | |
| } | |
| // Runs only if invoked via CLI | |
| // @ts-ignore | |
| const scriptPath = process.argv[1]?.endsWith('tsx') ? process.argv[2] : process.argv[1]; | |
| const isDirect = | |
| !!scriptPath && import.meta.url === pathToFileURL(path.resolve(scriptPath)).href; | |
| if (isDirect) { | |
| main().catch((err) => { | |
| console.error(err); | |
| process.exit(1); | |
| }); | |
| } |
🤖 Prompt for AI Agents
In scripts/llm-generate-description.ts around lines 151–159, the
direct-execution check fails when run via tsx because process.argv[1] points to
the tsx binary rather than the script; update the check to locate the actual
script arg (search process.argv for the argument whose basename matches this
script like llm-generate-description.ts or .tsx), convert that found arg to a
file:// URL with pathToFileURL and compare it to import.meta.url; if no matching
arg is found fall back to the existing check so main() runs correctly when
invoked directly by node or tsx.
There was a problem hiding this comment.
Actionable comments posted: 1
♻️ Duplicate comments (2)
scripts/llm-generate-description.ts (2)
187-194: Fail fast with a clear error if OPENAI_API_KEY is missing.Surface a helpful message before making the API call.
if (!filePathArg) { throw new Error("File path is required. Use --help for usage information."); } + if (!process.env.OPENAI_API_KEY) { + throw new Error("OPENAI_API_KEY is required to generate a page description"); + } + const resolvedPath = path.resolve(filePathArg); const { description, characterCount, skipped } = await generateDescriptionForDocsPage(resolvedPath, { skipIfExists });
209-216: Fix CLI direct-execution check to work with pnpm/tsx/ts-node.Current endsWith check can miss when invoked via tsx, so main() may not run.
-const isDirect = - process.argv[1] && process.argv[1].endsWith("llm-generate-description.ts"); -if (isDirect) { +const argv = process.argv.slice(1); +const candidate = argv.find((a) => + /llm-generate-description\.(t|j)sx?$/i.test(a) +); +const scriptPath = candidate ? path.resolve(candidate) : undefined; +const isDirect = + !!scriptPath && import.meta.url === pathToFileURL(scriptPath).href; +if (isDirect) { main().catch((err) => { console.error(err); process.exit(1); }); }
🧹 Nitpick comments (1)
scripts/llm-generate-description.ts (1)
191-205: Optional: Add small progress logs to match the PR’s usage examples.Improves UX parity (“Generating…”, “Generated successfully (N characters)”).
- const resolvedPath = path.resolve(filePathArg); - const { description, characterCount, skipped } = + const resolvedPath = path.resolve(filePathArg); + console.log("Generating description..."); + const { description, characterCount, skipped } = await generateDescriptionForDocsPage(resolvedPath, { skipIfExists }); if (skipped) { console.log("✅ File skipped - description already exists"); return; } + console.log(`Description generated successfully (${characterCount} characters)`); console.log( `================ DESCRIPTION START (character count: ${characterCount}) =================`, );
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scripts/llm-generate-description.ts(1 hunks)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (2)
- GitHub Check: tests
- GitHub Check: build
🔇 Additional comments (1)
scripts/llm-generate-description.ts (1)
98-117: LGTM: simple, robust Markdoc frontmatter + raw body extraction.Using raw Markdoc body avoids JSDOM/renderer complexity and is fine for LLM context. Error wrapping is clear.
Comment on lines
+24
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+55
| async function generateDescription({ | ||
| articleText, | ||
| frontmatterAttributes, | ||
| }: { | ||
| articleText: string; | ||
| frontmatterAttributes: Record< | ||
| string, | ||
| string | number | boolean | Date | string[] | number[] | boolean[] | ||
| >; | ||
| }) { | ||
| const systemPrompt = `You are an expert at writing SEO-optimized page descriptions for technical documentation websites targeting senior software engineers. | ||
|
|
||
| Generate a concise, professional description (maximum 250 characters) that: | ||
| - Accurately summarizes the technical content | ||
| - Uses natural language with standard punctuation (use regular hyphens, not em dashes) | ||
| - Speaks directly to experienced developers and engineering leaders | ||
| - Includes relevant technical keywords for SEO | ||
| - Avoids AI-generated language patterns or marketing fluff | ||
| - Uses a professional, authoritative tone that resonates with senior engineers | ||
|
|
||
| The description should be suitable for use in HTML meta descriptions and social media previews.`; | ||
|
|
||
| const userPrompt = `Generate a page description for this documentation page: | ||
|
|
||
| Title: ${frontmatterAttributes?.title || "Untitled"} | ||
| Summary: ${frontmatterAttributes?.summary || "No summary provided"} | ||
|
|
||
| Content: | ||
| ${articleText} | ||
|
|
||
| Generate a description that captures the essence of this page in 250 characters or less.`; | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enforce true ≤250-character limit: normalize, count code points, and clamp after retry.
Prevents Unicode miscounts and returning >250 chars even after retry.
async function generateDescription({
articleText,
frontmatterAttributes,
}: {
articleText: string;
frontmatterAttributes: Record<
string,
string | number | boolean | Date | string[] | number[] | boolean[]
>;
}) {
- const systemPrompt = `You are an expert at writing SEO-optimized page descriptions for technical documentation websites targeting senior software engineers.
+ const MAX_CHARS = 250;
+ const systemPrompt = `You are an expert at writing SEO-optimized page descriptions for technical documentation websites targeting senior software engineers.
-Generate a concise, professional description (maximum 250 characters) that:
+Generate a concise, professional description (maximum ${MAX_CHARS} characters) that:
- Accurately summarizes the technical content
- Uses natural language with standard punctuation (use regular hyphens, not em dashes)
- Speaks directly to experienced developers and engineering leaders
- Includes relevant technical keywords for SEO
- Avoids AI-generated language patterns or marketing fluff
- Uses a professional, authoritative tone that resonates with senior engineers
The description should be suitable for use in HTML meta descriptions and social media previews.`;
const userPrompt = `Generate a page description for this documentation page:
Title: ${frontmatterAttributes?.title || "Untitled"}
Summary: ${frontmatterAttributes?.summary || "No summary provided"}
Content:
${articleText}
-Generate a description that captures the essence of this page in 250 characters or less.`;
+Generate a description that captures the essence of this page in ${MAX_CHARS} characters or less.`;
try {
const { text: description } = await generateText({
model: openai("gpt-4o-mini"),
system: systemPrompt,
prompt: userPrompt,
maxTokens: 100,
});
- const trimmedDescription = description.trim();
- const characterCount = trimmedDescription.length;
+ const normalized = description.replace(/\s+/g, " ").trim();
+ const characterCount = Array.from(normalized).length;
// If the description is too long, try again with a more specific prompt
- if (characterCount > 250) {
- const retryPrompt = `The previous description was too long (${characterCount} characters). Generate a shorter description (maximum 250 characters) for this page:
+ if (characterCount > MAX_CHARS) {
+ const retryPrompt = `The previous description was too long (${characterCount} characters). Generate a shorter description (maximum ${MAX_CHARS} characters) for this page:
Title: ${frontmatterAttributes?.title || "Untitled"}
Content: ${articleText.substring(0, 500)}...
-Make it concise and under 250 characters.`;
+Make it concise and under ${MAX_CHARS} characters.`;
const { text: retryDescription } = await generateText({
model: openai("gpt-4o-mini"),
system: systemPrompt,
prompt: retryPrompt,
maxTokens: 80,
});
- const finalDescription = retryDescription.trim();
- return {
- description: finalDescription,
- characterCount: finalDescription.length,
- };
+ let final = retryDescription.replace(/\s+/g, " ").trim();
+ let finalCount = Array.from(final).length;
+ if (finalCount > MAX_CHARS) {
+ final = Array.from(final).slice(0, MAX_CHARS).join("");
+ finalCount = MAX_CHARS;
+ }
+ return { description: final, characterCount: finalCount };
}
- return { description: trimmedDescription, characterCount };
+ return { description: normalized, characterCount };
} catch (error) {
throw new Error(
`Failed to generate description: ${error instanceof Error ? error.message : "Unknown error"}`,
);
}
}Also applies to: 56-91
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How to use this script
First, install dependencies:
Ensure you have an
OPENAI_API_KEYset.Then:
Output:
Summary by CodeRabbit