Skill Article logo

Skill Article

Organization
aeonfun
skill-article

Turn any skill in this instance into a publish-ready launch article - proof-stat headline, one contrarian thesis, mechanics, war stories from real run history, a mental-model reframe, and the full SKILL.md embedded verbatim so readers can steal it. Optional Higgsfield title banner with --banner.

Overview

Publisheraeonfun
Repositoryaeon
Skill nameskill-article
Stars
771
Forks
280
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 aeonfun on GitHub. Read the source before you install it.

Installation

Install the Skill Article 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/aeonfun/aeon/tree/main/skills/skill-article
  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/aeonfun/aeon.git /tmp/aeon
mkdir -p .claude/skills
cp -r /tmp/aeon/skills/skill-article .claude/skills/skill-article
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Article 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 Skill Article 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 Skill Article 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.

${var} — Selector: <skill-name> [--brand <handle>] [--banner].

  • <skill-name> → announce that skill (must exist under skills/<skill-name>/).
  • empty → pick the most article-worthy skill from the last 14 days of memory/logs/: prefer a skill that shipped recently, produced verified output, or hit a milestone. If nothing qualifies, log SKILL_ARTICLE_NO_TARGET, send no notification, and exit clean.
  • --brand <handle> anywhere → byline/handle for the outreach footer (default: this instance's public identity from soul/, else the repo's org).
  • --banner anywhere → also generate one 16:9 title banner through the Higgsfield MCP (spends credits; see Step 5). Off by default - a blank run never spends.

Examples: "aeon-update", "rug-scan --brand @myproject --banner".

Today is ${today}. Write a launch article for one skill, modeled on the security-industry "skill announcement" format: the article sells the insight the skill encodes, not the file - and then gives the file away.

Shared preamble

  1. Read memory/MEMORY.md and the last 14 days of memory/logs/ - know what this skill has actually done.
  2. If a soul/ directory exists, read soul/SOUL.md and soul/STYLE.md for voice. Default voice: terse, declarative, builder-wrote-this-fast.
  3. Read the target's full skills/<name>/SKILL.md. Every claim in the article must trace to a line in it.

Step 1 — Mine the track record (real numbers only)

Hunt for countable proof before writing a word:

  • memory/logs/ entries under the skill's ### <name> headings: runs, OK/error statuses, notable outcomes.
  • output/ files the skill produced; PRs or issues it opened (gh pr list --search).
  • git log -- skills/<name>/ for ship date and iteration history.

A real stat ("84 verified findings", "31 runs, 0 false alarms") becomes the headline. If no real count exists, the headline uses the mechanism instead. Never invent a number - a fabricated stat is the one thing that kills this format.

Step 2 — Find the thesis

Answer: what does this skill see that the default workflow throws away? That gap is the thesis.

Write 3 candidate contrast pairs (two-sided sentences like "production code shows what the system does; tests show what its developers thought it did") and keep the sharpest. Everything in the article hangs on this one insight.

Step 3 — Collect war stories

1-3 concrete moments where the skill caught or produced something a naive approach would miss - pulled from memory/logs/ and output/, anonymized where needed, mechanism kept visible.

If the skill has no run history yet, write "how it plays out" scenarios and label them as scenarios. Never dress a hypothetical as a case study.

Step 4 — Write the article

Structure, in order:

  1. Headline: <Skill Name>: <proof stat or mechanism claim>. Bold claim, not a setup.
  2. Provenance (2 short paragraphs): what it is, where it runs, ship date, track record if real.
  3. Thesis section with a punchy heading (a claim, never a label): what everyone else's workflow misses.
  4. Mechanics (2-4 short paragraphs) built around the contrast pair.
  5. War stories: one mini-heading per story, 2-4 sentences each, ending with the generalized pattern ("The same blind spot appears when...").
  6. Reframe section: the mental-model shift the reader keeps even if they never run the skill.
  7. Ship it: where it lives + one-line CTA (star the upstream repo).
  8. CODENAME: <Skill Name>: the target SKILL.md embedded verbatim in a fenced block (use a 4-backtick fence - skill files contain 3-backtick fences). Do not paraphrase or trim it.
  9. Outreach footer: 1-2 sentences, DMs-open style, pointing at the brand handle.

Voice rules (the anti-tells)

  • Short declarative sentences. Cut every sentence that only sets up the next one.
  • Banned: "revolutionary", "game-changing", "unleash", "delve", "Let's dive in".
  • No parallel-structure trios, no "not X, but Y" scaffolding.
  • Section headings are claims or scenes ("There is an alpha in the test folder"), never labels ("Overview").
  • Verify the repo/link in the CTA exists this run before naming it.
  • Never reference private forks or internal accounts - describe this instance generically and point at the upstream/public repo.

Step 5 — Title banner (only with --banner)

Skip this step entirely unless --banner was passed. When it was:

  1. Check the connection first. If no mcp__higgsfield__* tool is callable, the banner is skipped, not the article: note banner: HIGGS_NOT_CONNECTED for the log, point the operator at the dashboard → MCP → Connect Higgsfield in the notify, and continue to Step 6. Same on 401/stale auth (banner: HIGGS_AUTH_STALE) or insufficient credits (banner: HIGGS_NO_CREDITS). The article never fails because the banner did.
  2. Build the prompt from the thesis, not the feature list. The banner is a visual metaphor for the Step 2 contrast pair - the scene that makes the insight visible. Image models garble long text, so the only text allowed in the prompt is the skill's short name (or none); the headline lives in the article, not the pixels. Match soul/ aesthetic if it defines one; otherwise: clean, high-contrast, one focal object, no collage.
  3. Generate exactly one image, --ar 16:9, following the higgsfield skill's spend rules: one generation per run, one retry at most on a transient error, never re-submit a job that already succeeded. Poll to completion with a bounded number of polls; on timeout record banner: HIGGS_FAILED and move on.
  4. Capture the asset URL verbatim. Never fabricate one. Banner asset URLs may be time-limited signed URLs - flag that in the notify so the operator saves it.

Embed the result at the top of the article file, next to the alts block:

markdown
<!-- banner: <asset-url> · model: <model> · job: <id> -->

Step 6 — Deliver

  1. Write the article to output/skill-articles/${today}-<skill-name>.md (shell redirection; mkdir -p first). Include an <!-- alts --> comment block at the top with 2 alternative headline + thesis-heading pairs so the operator can iterate without a rewrite.

  2. Notify with ./notify -f /tmp/skill-article-notify.md (write the body under /tmp/): the headline, the thesis in one line, whether the stats are real or the headline fell back to mechanism, the banner URL when one was generated (with a save-it note if the URL is signed/expiring), and a clickable link built from the run's environment:

    bash
    ARTICLE_URL="${GITHUB_SERVER_URL:-https://github.com}/${GITHUB_REPOSITORY}/blob/main/output/skill-articles/<file>.md"
  3. Log to memory/logs/${today}.md under a ### skill-article heading: target skill, headline, thesis pair, stats-real-or-mechanism, output path, and - when --banner was passed - a banner: line (HIGGS_OK <url> | HIGGS_NOT_CONNECTED | HIGGS_AUTH_STALE | HIGGS_NO_CREDITS | HIGGS_FAILED | skipped). Status codes: SKILL_ARTICLE_OK on success, SKILL_ARTICLE_NO_TARGET when ${var} was empty and nothing article-worthy exists, SKILL_ARTICLE_NOT_FOUND when the named skill has no SKILL.md.

Limits

  • This writes the article; it does not post it. Publishing is the operator's call (or a posting skill's, explicitly chained).
  • The banner is opt-in and spends real Higgsfield credits - one image per run, hard cap, and its failure never blocks the article.
  • Track-record mining is only as good as memory/logs/ - a skill that runs but never logs will read as unproven, and the article will say so rather than guess.
  • One skill per run. Announcing a pack is a different article; run once per skill instead.

Frequently asked questions

What does the Skill Article AI skill do?

Turn any skill in this instance into a publish-ready launch article - proof-stat headline, one contrarian thesis, mechanics, war stories from real run history, a mental-model reframe, and the full SKILL.md embedded verbatim so readers can steal it. Optional Higgsfield title banner with --banner.

Why use Skill Article on TypingMind?

Because you install it once and use it with any model. Skill Article 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 Skill Article in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aeonfun/aeon/tree/main/skills/skill-article. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Skill Article?

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 Skill Article?

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

Is the Skill Article AI skill free?

Yes. It is published on GitHub by aeonfun 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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