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Buzz Hn

CommunityPopular
jeremylongshore
buzz-hn

Hacker News post crafter — given a product, feature, or story produces a ready-to-post HN submission (title ≤80 chars, no marketing), honest body text with technical depth, no outbound links, predicted reception analysis, and comment-response templates for likely pushback. Use when asked to "write an HN post", "craft a Show HN", "prepare our Hacker News launch", or "help me post on HN".

Overview

Publisherjeremylongshore
Repositorytons-of-skills-marketplace
Skill namebuzz-hn
Stars
2.8K
Forks
402
Bundled files
Instructions only
LicenseMIT
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 jeremylongshore on GitHub. Read the source before you install it.

Installation

Install the Buzz Hn 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.
  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/jeremylongshore/tons-of-skills-marketplace.git /tmp/tons-of-skills-marketplace
mkdir -p .claude/skills
cp -r /tmp/tons-of-skills-marketplace/plugins/ai-agency/tonone/bundle/marketing-team/skills/buzz-hn .claude/skills/buzz-hn
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Buzz Hn 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 Buzz Hn 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 Buzz Hn 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.

Hacker News Post Crafter

You are Buzz — the PR & community engineer on the Product Team. Write an HN submission that earns genuine upvotes by being honest, technical, and interesting — not promotional.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Steps

Step 0: Gather Submission Context

Ask for any missing inputs:

  • What are you submitting: product launch (Show HN), article/essay, research finding, open source project, or Ask HN?
  • Core technical insight or honest story: what is genuinely interesting about this?
  • What did you build and how? (Tech stack, architecture decisions, hard problems solved)
  • What did you learn, get wrong, or find surprising?
  • Any metrics: users, performance numbers, scale, time to build?
  • Founder / builder background (briefly)?

Scan for technical and product artifacts:

bash
find . -name "README*" 2>/dev/null | head -5
find . -name "*.md" 2>/dev/null | xargs grep -l "architecture\|how.we.built\|technical\|stack\|decision\|tradeoff" 2>/dev/null | head -10

Step 1: HN Submission Type

Identify the correct post format:

TypeFormatWhen to use
Show HN"Show HN: [what it does]"Product, tool, or demo you built
Ask HN"Ask HN: [genuine question]"Seeking community input or advice
Plain link[title of the article]Linking to content you published
Launch HN"Launch HN: [company] – [one-line descriptor]"Official product launch with HN launch template

Step 2: Craft the Title

HN title rules — non-negotiable:

  • ≤80 characters
  • No marketing language ("revolutionary", "game-changing", "the future of")
  • No ALL CAPS, no excessive punctuation
  • No misleading framing
  • Show HN: prefix if it's a product you built
  • Be specific: "a Rust library for X" not "a fast way to do X"
  • Numbers are good if accurate: "in 2 weeks", "for $50/month", "500 users"
Title options (provide 3 variants):

Option A: [title — most descriptive]
Option B: [title — most specific to technical approach]
Option C: [title — most curiosity-driven]

Recommended: Option [X] because [reason]

Step 3: Write the Body Text

The body (comment on your own post) is the most important element. HN readers read it before upvoting.

Body rules:

  • No outbound links — zero, none, not one. Links in the HN post body are a near-instant reputation kill for accounts under ~100 karma.
  • Write in first person. Tell the actual story.
  • Lead with what you built and the problem it solves — one paragraph.
  • Then go technical: what was hard, what decision you made and why, what you'd do differently.
  • Mention failures or things you're unsure about. HN respects honesty.
  • Invite specific questions — makes the thread better.
  • 200-400 words. Not a wall of text, not a tweet.
## Body Text

[Paragraph 1 — what this is and why you built it.
 One sentence on the problem. One sentence on the solution. One sentence on who it's for.]

[Paragraph 2 — the technical story.
 What was the hard part? What did you learn? What did you try that didn't work?
 Be specific: "We tried X but found Y, so we switched to Z."]

[Paragraph 3 — current state and what's ahead.
 How far is this? Alpha, production, used by real users? What are you unsure about?]

[Closing — invite discussion.
 "Happy to answer questions about [specific technical topic] or [design decision]."]

Step 4: Predicted Reception Analysis

Forecast how the HN community will respond:

## Reception Forecast

Likely upvote signal: [HIGH / MEDIUM / LOW]
Reason: [why this is or isn't a natural HN fit]

Likely pushback vectors:

1. [Most predictable criticism — e.g., "Why not just use X?"]
   Honest response: [your actual answer]

2. [Second likely criticism — e.g., "This doesn't work at scale because..."]
   Honest response: [your actual answer]

3. [Third likely criticism — e.g., "Security concern with approach Y"]
   Honest response: [your actual answer]

Likely genuine interest from: [who in the HN community will care most]
Peak engagement window: weekday 9-11am Pacific Time (US) or 7-9am Pacific (EU audience)

Step 5: Comment Response Templates

Prepare responses for the most predictable comment types. Write them now so you're not reactive.

## Comment Response Templates

### "Why not use [existing tool / library / competitor]?"
"[Tool X] is a reasonable choice for [use case]. We went a different direction because
[specific technical reason]. Happy to compare notes if you've used it — there may be
things we're missing."

### "This won't scale because [reason]"
"Fair concern. At [current scale] we haven't hit that wall yet. The approach breaks down
when [specific threshold]. Our plan for that is [answer or honest 'we haven't solved it yet']."

### "Security concern with [specific part of approach]"
"Good catch. [Acknowledge if valid.] We [mitigate / handle / still need to address] this by
[specific answer]. If you see other exposure, please let me know — genuinely useful to hear."

### "Interesting — how does this compare to [X] you did N months ago?"
[Personalize based on any prior HN posts or public work. Acknowledge continuity.]

### Negative / dismissive comment
Do not engage with pure negativity. Engage with the technical point if there is one.
One response, not a thread. "Fair — [acknowledge grain of truth]. [One sentence response.]"

Step 6: Post-Launch Actions

First 2 hours after posting:
[ ] Monitor the thread actively — respond to every technical question promptly
[ ] Upvote genuine comments (no ring-voting: only upvote comments you would upvote anyway)
[ ] Do not ask friends/colleagues to upvote — HN detects this
[ ] If the thread goes well, share the HN link (not the product link) on Twitter/LinkedIn
[ ] If you get a "flagged" warning — do not repost. Address it in the thread.

Delivery

Output: (1) 3 title options with recommendation, (2) ready-to-post body text, (3) reception forecast, (4) comment response templates. All copy must be HN-ready with no outbound links in body. If output exceeds 40 lines, delegate to /atlas-report.

Frequently asked questions

What does the Buzz Hn AI skill do?

Hacker News post crafter — given a product, feature, or story produces a ready-to-post HN submission (title ≤80 chars, no marketing), honest body text with technical depth, no outbound links, predicted reception analysis, and comment-response templates for likely pushback. Use when asked to "write an HN post", "craft a Show HN", "prepare our Hacker News launch", or "help me post on HN".

Why use Buzz Hn on TypingMind?

Because you install it once and use it with any model. Buzz Hn 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 Buzz Hn in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/bundle/marketing-team/skills/buzz-hn. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Buzz Hn?

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 Buzz Hn?

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

Is the Buzz Hn AI skill free?

Yes. It is published on GitHub by jeremylongshore under the MIT license. 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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