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Source Post

Community
JasonxzWen
source-post

Load when turning an external article, blog, release note, interview, or report into a source-backed public post with Chinese fidelity, media references, an effective-interact summary layer, and project-iteration review; do not load for ordinary summaries, full copyrighted reposts, or production site design.

Overview

PublisherJasonxzWen
Repositoryharness-hub
Skill namesource-post
Stars
71
Forks
0
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Source Post 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/source-post .claude/skills/source-post
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Source Post 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 Source Post 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 Source Post 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.

Source Post

Use this skill when source material must become a publishable source post, not just a chat summary.

Workflow

  1. Capture the source before writing: title, author, date, canonical URL, access date, article structure, tables, footnotes, glossary, links, images, charts, video, downloads, and known access limits.
  2. Set the fidelity boundary. Preserve claims, structure, numbers, terms, links, and media references. Do not publish a full copyrighted repost or full translation unless the user owns the rights or provides licensed source text.
  3. Define the reader job before drafting: what should a human understand in the first minute, what structure helps them, and what decision or project implication they should leave with.
  4. Write the reader-facing blog before the evidence apparatus. Lead with a short article narrative, then the simplest source structure needed for orientation, then analysis. Put source ledger, copyright boundary, and validation details after the reader understands the source.
  5. Use effective-interact only as a secondary summary or visualization layer. Do not use a raw generated interaction report as the blog body.
  6. Add a project-iteration review: what the source confirms, what should change in this repo, what was already changed, what may be redundant, what should stay out of scope, and what needs a later SDD/spec.
  7. Validate the generated post with scripts/validate-source-post.mjs, the normal source-post validation, a rendered screenshot, and repo gates proportionate to changed files.

Reader-First Shape

Prefer normal article flow over dashboard flow. The default public post shape is:

  1. One-minute answer: one short paragraph saying what the source is and why this repo cares.
  2. Source structure: one tree, outline, or compact table that lets the reader orient quickly.
  3. Core ideas: prose bullets that explain the source's argument or system.
  4. Local iteration: what this repo already changed or should change because of the source.
  5. Reflection: explicit verdict on effectiveness, redundancy, and next optimization.
  6. Sources and validation: attribution, copyright boundary, source ledger, and generated artifacts.

Use a table only when rows are genuinely comparable. If a table cell becomes a paragraph, rewrite that section as prose. Use at most one early visual structure before the article explains why it matters.

Output Contract

  • The blog must have a readable article layout, not raw evidence/code blocks.
  • A reader should be able to answer these in 60 seconds: what is the source, how is it organized, what did this repo change, and was that change worth it?
  • Source metadata, copyright limits, and evidence ledgers must not be the lead unless the user's main request is source audit or licensing.
  • Original media must remain visible or linked with alt text, caption, and source URL.
  • Source claims, project judgments, assumptions, and copyright limits must be labeled.
  • The effective-interact artifact must be linked or embedded as a summary layer after the fidelity layer.
  • The post must include an iteration decision even when the decision is "no repo change."
  • If the user asks for reflection, include direct labels: effective, redundant risk, and next optimization.

Progressive Loading

  • Read references/workflow.md for the detailed step sequence and acceptance checklist.
  • Read references/copyright-and-fidelity.md before publishing translated or paraphrased third-party material.
  • Use assets/source-post-checklist.md as the authoring checklist.
  • Run scripts/validate-source-post.mjs <post-dir> before handoff.

Gotchas

  • A source-backed public post is not the same thing as an effective-interact report. Effective-interact lowers summary and decision cost; it does not replace the translated or reconstructed source layer.
  • Do not mistake high information density for readability. Dense pages still need a human reading order: answer, structure, implications, reflection, sources.
  • Do not lead with authorization mechanics, source-ledger implementation, or generated-component details when the reader asked to understand the source.
  • Do not turn article sections into repeated summary-cards or data-table blocks just because the generator supports them.
  • Browser screenshots are part of acceptance for public HTML. A passing JSON validator does not prove the page is readable.
  • If the primary site blocks automated fetches, use an indexed or browser-readable copy only after recording the access limitation and checking the canonical URL.
  • Keep UTF-8 end to end. Mojibake in a public post is a failed delivery, even if tests pass.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Source Post AI skill do?

Load when turning an external article, blog, release note, interview, or report into a source-backed public post with Chinese fidelity, media references, an effective-interact summary layer, and project-iteration review; do not load for ordinary summaries, full copyrighted reposts, or production site design.

Why use Source Post on TypingMind?

Because you install it once and use it with any model. Source Post 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 Source Post in TypingMind?

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

Which AI models can use Source Post?

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 Source Post?

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

Is the Source Post 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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