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Daily Hunt

Community
MagicCube
daily-hunt

Daily tech product and open-source discovery digest. Fetches today's Product Hunt leaderboard and GitHub Trending top 10, then curates a personalized briefing in the user's preferred language using their memory/profile to highlight items most relevant to them. Use this skill whenever the user says "daily hunt", "今日猎奇", "tech digest", "daily digest", "what's trending", "今天有什么新产品", "PH today", "show me today's launches", "GitHub trending", "今日趋势", "morning briefing", "tech briefing", "daily brief", or any combination asking about Product Hunt + GitHub Trending together. Also trigger when the user asks about new product launches, trending repos, or wants a curated tech news digest. Even casual phrasing like "what's new in tech today" or "有什么好玩的项目" should trigger this skill.

Overview

PublisherMagicCube
Repositoryagentara
Skill namedaily-hunt
Stars
516
Forks
58
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by MagicCube on GitHub. Read the source before you install it.

Installation

Install the Daily Hunt AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/MagicCube/agentara/tree/main/user-home/.claude/skills/daily-hunt
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/MagicCube/agentara.git /tmp/agentara
mkdir -p .claude/skills
cp -r /tmp/agentara/user-home/.claude/skills/daily-hunt .claude/skills/daily-hunt
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Daily Hunt in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Daily Hunt on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Daily Hunt is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Daily Hunt 🏹

A personalized daily tech discovery digest combining Product Hunt launches and GitHub Trending repositories, tailored to the user's interests and expertise.

Workflow

Step 1: Determine Date & Data Sources

  1. Note today's date.
  2. Product Hunt launches happen Mon–Fri (Pacific Time). If today is Saturday/Sunday, use the most recent Friday's leaderboard instead and note this in the output.
  3. Construct the PH leaderboard URL: https://www.producthunt.com/leaderboard/daily/{YYYY}/{M}/{D}

Step 2: Fetch Product Hunt Data

Use web_search and web_fetch to gather PH daily leaderboard data:

  1. Primary: Fetch https://www.producthunt.com/leaderboard/daily/{YYYY}/{M}/{D}
  2. Backup: Search hunted.space history {month} {year} for structured launch data with upvotes
  3. Backup 2: Fetch https://hunted.space/stats for recent daily stats
  4. Try to get at least the top 5-10 launches with:
    • Product name
    • Tagline / one-line description
    • Category tags
    • Upvote count (if available)
    • Comment count (if available)
  5. For each product, construct its PH link: https://www.producthunt.com/posts/{product-slug}
    • If the exact slug is unknown, link to the daily leaderboard page instead

Step 3: Fetch GitHub Trending Data

  1. Primary: Use web_fetch on https://github.com/trending to get today's trending repos
  2. Extract top 10 repositories with:
    • Repository full name (owner/repo)
    • Description
    • Primary language
    • Star count (total)
    • Fork count
    • Stars gained today
  3. Construct links: https://github.com/{owner}/{repo}

Step 4: Personalize & Curate

Cross-reference the fetched data with the user's profile from memory (userMemories):

  1. Identify relevance signals — Check each item against the user's:

    • Tech stack (languages, frameworks, tools)
    • Work domain (e.g., medical tech, AI agents, developer tools)
    • Side projects and interests (e.g., agent architecture, AIGC, photography)
    • Favorite topics (e.g., open source, LangChain, MCP, TypeScript)
  2. Mark noteworthy items with emoji indicators:

    • ⭐ — Directly relevant to the user's tech stack or active projects
    • 🔥 — Exceptionally hot / high engagement / viral
    • 👀 — Interesting for the user's broader interests or domain
    • No emoji — Standard inclusion, still worth knowing about
  3. Preserve original ranking — Keep the exact order from the source (PH leaderboard rank, GitHub Trending page order). Do NOT re-sort by engagement or relevance. Only add emoji indicators (⭐🔥👀) to mark noteworthy items in-place.

Step 5: Format Output

Use the user's preferred language (check memory — if Chinese-speaking, use Chinese with full-width punctuation).

Output Structure

Use list format (not tables) for all items — this renders correctly on mobile and in IM apps.

# Daily Hunt 🏹 — {date}

> {One-sentence personalized summary of today's highlights}

## Product Hunt 热门发布

1. ⭐ **[Name](link)**
   tagline
   → personalized reason why it matters

2. **[Name](link)**
   tagline
   → reason

...

---

## GitHub Trending Top 10

1. 🔥 **[owner/repo](link)** · Python · ⭐31.4k · 🍴2.1k · +1,122 今日
   description

2. ⭐ **[owner/repo](link)** · TypeScript · ⭐8.9k · 🍴430 · +300 今日
   description

...

## 📌 昕哥精选

{2-3 sentences highlighting the most relevant items for this specific user,
explaining WHY these matter for their work/projects. Reference specific
projects or interests from memory.}

Key Rules

  • Use lists, not tables — tables break on mobile and in most IM apps
  • Product Hunt items MUST include: name, link, tagline, personalized "why care" note (prefixed with →)
  • GitHub items MUST include: full repo name, link, language, star count, fork count, today's stars, description
  • "Today's stars" (stars gained today) should be shown when available from the trending page
  • If data for a source is partially unavailable, still show what you can and note the gap
  • The "Personal Picks" section at the end is the differentiator — make it genuinely insightful
  • Do NOT pad with generic descriptions — be specific about why each item matters to THIS user
  • Use Chinese full-width punctuation (,、:!) for Chinese content
  • First-level headings use # not ##
  • Keep the "why care" note concise (one sentence max)

Frequently asked questions

What does the Daily Hunt AI skill do?

Daily tech product and open-source discovery digest. Fetches today's Product Hunt leaderboard and GitHub Trending top 10, then curates a personalized briefing in the user's preferred language using their memory/profile to highlight items most relevant to them. Use this skill whenever the user says "daily hunt", "今日猎奇", "tech digest", "daily digest", "what's trending", "今天有什么新产品", "PH today", "show me today's launches", "GitHub trending", "今日趋势", "morning briefing", "tech briefing", "daily brief", or any combination asking about Product Hunt + GitHub Trending together. Also trigger when the...

Why use Daily Hunt on TypingMind?

Because you install it once and use it with any model. Daily Hunt is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Daily Hunt in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MagicCube/agentara/tree/main/user-home/.claude/skills/daily-hunt. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Daily Hunt?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Daily Hunt?

As many as you like. As long as a model supports skills, you can use Daily Hunt with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Daily Hunt AI skill free?

It is published on GitHub by MagicCube. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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