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starscout

starscout is a tool for searching through your GitHub stars using AI.

It uses vector embeddings to find relevant repositories according to the user's search query.

Requirements

  • Postgres instance with pgvector extension
  • Embedding model provider (e.g. OpenAI, Gemini, or self-hosted model)
  • GitHub OAuth app credentials

How it works

The system operates through the following steps:

  1. Creates embeddings for each starred repository using the repository name, description, and some part of the README content
  2. Stores these embeddings in a Postgres database
  3. Uses a vector search algorithm to find the top 10 repositories matching the search query
  4. Returns the results to frontend

Things to note

By default:

  • We only index repositories with at least 100 stars. It can be configured using the GITHUB_STAR_THRESHOLD environment variable in the backend.
  • We require user to provide their own API key for query and calculating embeddings if they have more than 5000 stars. This can also be configured using the API_KEY_STAR_THRESHOLD environment variable in the backend.

This project was mostly "vibe coded" by me and claude, so there must be many low hanging fruits to optimize. Feel free to open an issue or PR!

About

search through your github stars using AI

Resources

Readme
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7 stars

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0 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks"); } } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); } })(); (function(){ try { var __m = "github.com"; var __re = new RegExp('^' + "github\\.com" + '
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starscout

starscout is a tool for searching through your GitHub stars using AI.

It uses vector embeddings to find relevant repositories according to the user's search query.

Requirements

  • Postgres instance with pgvector extension
  • Embedding model provider (e.g. OpenAI, Gemini, or self-hosted model)
  • GitHub OAuth app credentials

How it works

The system operates through the following steps:

  1. Creates embeddings for each starred repository using the repository name, description, and some part of the README content
  2. Stores these embeddings in a Postgres database
  3. Uses a vector search algorithm to find the top 10 repositories matching the search query
  4. Returns the results to frontend

Things to note

By default:

  • We only index repositories with at least 100 stars. It can be configured using the GITHUB_STAR_THRESHOLD environment variable in the backend.
  • We require user to provide their own API key for query and calculating embeddings if they have more than 5000 stars. This can also be configured using the API_KEY_STAR_THRESHOLD environment variable in the backend.

This project was mostly "vibe coded" by me and claude, so there must be many low hanging fruits to optimize. Feel free to open an issue or PR!

About

search through your github stars using AI

Resources

Readme
Activity

Stars

7 stars

Watchers

0 watching

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0 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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starscout

starscout is a tool for searching through your GitHub stars using AI.

It uses vector embeddings to find relevant repositories according to the user's search query.

Requirements

  • Postgres instance with pgvector extension
  • Embedding model provider (e.g. OpenAI, Gemini, or self-hosted model)
  • GitHub OAuth app credentials

How it works

The system operates through the following steps:

  1. Creates embeddings for each starred repository using the repository name, description, and some part of the README content
  2. Stores these embeddings in a Postgres database
  3. Uses a vector search algorithm to find the top 10 repositories matching the search query
  4. Returns the results to frontend

Things to note

By default:

  • We only index repositories with at least 100 stars. It can be configured using the GITHUB_STAR_THRESHOLD environment variable in the backend.
  • We require user to provide their own API key for query and calculating embeddings if they have more than 5000 stars. This can also be configured using the API_KEY_STAR_THRESHOLD environment variable in the backend.

This project was mostly "vibe coded" by me and claude, so there must be many low hanging fruits to optimize. Feel free to open an issue or PR!

About

search through your github stars using AI

Resources

Readme
Activity

Stars

7 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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README.md
README.md

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starscout

starscout is a tool for searching through your GitHub stars using AI.

It uses vector embeddings to find relevant repositories according to the user's search query.

Requirements

  • Postgres instance with pgvector extension
  • Embedding model provider (e.g. OpenAI, Gemini, or self-hosted model)
  • GitHub OAuth app credentials

How it works

The system operates through the following steps:

  1. Creates embeddings for each starred repository using the repository name, description, and some part of the README content
  2. Stores these embeddings in a Postgres database
  3. Uses a vector search algorithm to find the top 10 repositories matching the search query
  4. Returns the results to frontend

Things to note

By default:

  • We only index repositories with at least 100 stars. It can be configured using the GITHUB_STAR_THRESHOLD environment variable in the backend.
  • We require user to provide their own API key for query and calculating embeddings if they have more than 5000 stars. This can also be configured using the API_KEY_STAR_THRESHOLD environment variable in the backend.

This project was mostly "vibe coded" by me and claude, so there must be many low hanging fruits to optimize. Feel free to open an issue or PR!

About

search through your github stars using AI

Resources

Readme
Activity

Stars

7 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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README.md

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starscout

starscout is a tool for searching through your GitHub stars using AI.

It uses vector embeddings to find relevant repositories according to the user's search query.

Requirements

  • Postgres instance with pgvector extension
  • Embedding model provider (e.g. OpenAI, Gemini, or self-hosted model)
  • GitHub OAuth app credentials

How it works

The system operates through the following steps:

  1. Creates embeddings for each starred repository using the repository name, description, and some part of the README content
  2. Stores these embeddings in a Postgres database
  3. Uses a vector search algorithm to find the top 10 repositories matching the search query
  4. Returns the results to frontend

Things to note

By default:

  • We only index repositories with at least 100 stars. It can be configured using the GITHUB_STAR_THRESHOLD environment variable in the backend.
  • We require user to provide their own API key for query and calculating embeddings if they have more than 5000 stars. This can also be configured using the API_KEY_STAR_THRESHOLD environment variable in the backend.

This project was mostly "vibe coded" by me and claude, so there must be many low hanging fruits to optimize. Feel free to open an issue or PR!

About

search through your github stars using AI

Resources

Readme
Activity

Stars

7 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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README.md
README.md

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starscout

starscout is a tool for searching through your GitHub stars using AI.

It uses vector embeddings to find relevant repositories according to the user's search query.

Requirements

  • Postgres instance with pgvector extension
  • Embedding model provider (e.g. OpenAI, Gemini, or self-hosted model)
  • GitHub OAuth app credentials

How it works

The system operates through the following steps:

  1. Creates embeddings for each starred repository using the repository name, description, and some part of the README content
  2. Stores these embeddings in a Postgres database
  3. Uses a vector search algorithm to find the top 10 repositories matching the search query
  4. Returns the results to frontend

Things to note

By default:

  • We only index repositories with at least 100 stars. It can be configured using the GITHUB_STAR_THRESHOLD environment variable in the backend.
  • We require user to provide their own API key for query and calculating embeddings if they have more than 5000 stars. This can also be configured using the API_KEY_STAR_THRESHOLD environment variable in the backend.

This project was mostly "vibe coded" by me and claude, so there must be many low hanging fruits to optimize. Feel free to open an issue or PR!

About

search through your github stars using AI

Resources

Readme
Activity

Stars

7 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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.gitignore
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README.md
README.md

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starscout

starscout is a tool for searching through your GitHub stars using AI.

It uses vector embeddings to find relevant repositories according to the user's search query.

Requirements

  • Postgres instance with pgvector extension
  • Embedding model provider (e.g. OpenAI, Gemini, or self-hosted model)
  • GitHub OAuth app credentials

How it works

The system operates through the following steps:

  1. Creates embeddings for each starred repository using the repository name, description, and some part of the README content
  2. Stores these embeddings in a Postgres database
  3. Uses a vector search algorithm to find the top 10 repositories matching the search query
  4. Returns the results to frontend

Things to note

By default:

  • We only index repositories with at least 100 stars. It can be configured using the GITHUB_STAR_THRESHOLD environment variable in the backend.
  • We require user to provide their own API key for query and calculating embeddings if they have more than 5000 stars. This can also be configured using the API_KEY_STAR_THRESHOLD environment variable in the backend.

This project was mostly "vibe coded" by me and claude, so there must be many low hanging fruits to optimize. Feel free to open an issue or PR!

About

search through your github stars using AI

Resources

Readme
Activity

Stars

7 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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frontend
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README.md
README.md

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starscout

starscout is a tool for searching through your GitHub stars using AI.

It uses vector embeddings to find relevant repositories according to the user's search query.

Requirements

  • Postgres instance with pgvector extension
  • Embedding model provider (e.g. OpenAI, Gemini, or self-hosted model)
  • GitHub OAuth app credentials

How it works

The system operates through the following steps:

  1. Creates embeddings for each starred repository using the repository name, description, and some part of the README content
  2. Stores these embeddings in a Postgres database
  3. Uses a vector search algorithm to find the top 10 repositories matching the search query
  4. Returns the results to frontend

Things to note

By default:

  • We only index repositories with at least 100 stars. It can be configured using the GITHUB_STAR_THRESHOLD environment variable in the backend.
  • We require user to provide their own API key for query and calculating embeddings if they have more than 5000 stars. This can also be configured using the API_KEY_STAR_THRESHOLD environment variable in the backend.

This project was mostly "vibe coded" by me and claude, so there must be many low hanging fruits to optimize. Feel free to open an issue or PR!

About

search through your github stars using AI

Resources

Readme
Activity

Stars

7 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages