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Package Release Sniffer

Community
JasonxzWen
package-release-sniffer

Load when tracking newly published package or model-package releases across package registries and release feeds for AI/developer-tool monitoring; do not load for ordinary docs lookup, broad GitHub trend scanning, or implementing package clients.

Overview

PublisherJasonxzWen
Repositoryharness-hub
Skill namepackage-release-sniffer
Stars
71
Forks
0
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 JasonxzWen on GitHub. Read the source before you install it.

Installation

Install the Package Release Sniffer 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/JasonxzWen/harness-hub.git /tmp/harness-hub
mkdir -p .claude/skills
cp -r /tmp/harness-hub/skills/package-release-sniffer .claude/skills/package-release-sniffer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Package Release Sniffer 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 Package Release Sniffer 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 Package Release Sniffer 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.

Package Release Sniffer

Use this skill to find newly published packages or meaningful package releases before they become general news.

Workflow

  1. Define the package ecosystem and freshness window before searching.
  2. Check primary release surfaces first: registry package pages, registry APIs, release feeds, GitHub Releases, changelogs, model cards, and maintainer announcement posts that link to the package.
  3. Record the package name, ecosystem, version, publish time, source URL, maintainer or organization, license signal, install surface, and why the release matters.
  4. Prefer packages with concrete shipped artifacts: new package, major/minor version, security fix, SDK/API capability, model/runtime integration, benchmarked performance change, or developer workflow impact.
  5. De-duplicate aliases across registry pages, GitHub repositories, model cards, and maintainer posts.
  6. Mark uncertainty when publish time, package identity, maintainership, license, or artifact availability is unclear.

Source Priority

Use primary sources before secondary mentions:

  • npm package pages and npm registry metadata
  • PyPI project pages and release history
  • GitHub Releases, tags, changelogs, and repository package metadata
  • Hugging Face model, dataset, Space, and library release pages when they behave like package artifacts
  • crates.io, Go package/module pages, Maven Central, NuGet, Docker Hub, or similar registries when relevant
  • Maintainer blogs or docs only when they link back to the package or release artifact

Keep

  • First public release of an AI or developer-tool package
  • New major or minor release with a clear capability change
  • SDK, CLI, MCP server/client, inference runtime, agent framework, eval, data pipeline, observability, RAG, vector database, or deployment package
  • Security, compatibility, performance, or migration-impact release
  • Package release that is not yet visible in broader trend/news sources

Drop

  • Media-only claims without a package or release source
  • Repository commits without a packaged artifact
  • Awesome lists, prompt dumps, demo-only repos, and placeholders
  • Releases with unclear maintainership, no usable artifact, or no relevant AI/developer-tool signal
  • Ordinary patch releases unless they affect security, compatibility, or widely used workflows

Output Shape

Return concise candidates:

json
{
  "package": "",
  "ecosystem": "npm | pypi | github-release | hugging-face | crates | go | maven | nuget | docker | other",
  "version": "",
  "published_at": "YYYY-MM-DDTHH:mm:ssZ",
  "source_url": "",
  "maintainer": "",
  "signal": "new_package | major_release | minor_release | security | performance | compatibility | ecosystem",
  "summary": "",
  "why_it_matters": "",
  "confidence": "high | medium | low"
}

Boundaries

  • Use documentation-lookup when the task is to read current docs for an already chosen package.
  • Use source-post when a confirmed release must become a publishable article.
  • Use implementation workflows when the user wants a registry client, scraper, monitor, or scheduled job built.
  • Do not create accounts, subscribe to feeds, change automation, or publish packages.

Frequently asked questions

What does the Package Release Sniffer AI skill do?

Load when tracking newly published package or model-package releases across package registries and release feeds for AI/developer-tool monitoring; do not load for ordinary docs lookup, broad GitHub trend scanning, or implementing package clients.

Why use Package Release Sniffer on TypingMind?

Because you install it once and use it with any model. Package Release Sniffer 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 Package Release Sniffer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JasonxzWen/harness-hub/tree/main/skills/package-release-sniffer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Package Release Sniffer?

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 Package Release Sniffer?

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

Is the Package Release Sniffer AI skill free?

Yes. It is published on GitHub by JasonxzWen 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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