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Anarlog Workflow

Organization
modem-dev
anarlog-workflow

Execute Anarlog work immediately while recording issues, decisions, progress, and lessons in Linear. Use for Anarlog repository or Anarlog desktop, web, mobile, and API work, including related worktrees and ANLG issues. Explicit brainstorming stays discussion-first. Do not use for unrelated repositories or meeting-data queries.

Overview

Publishermodem-dev
Repositoryossrules
Skill nameanarlog-workflow
Stars
65
Forks
2
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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 modem-dev on GitHub. Read the source before you install it.

Installation

Install the Anarlog Workflow 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/modem-dev/ossrules/tree/main/public/files/anarlog/.agents/skills/anarlog-workflow
  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/modem-dev/ossrules.git /tmp/ossrules
mkdir -p .claude/skills
cp -r /tmp/ossrules/public/files/anarlog/.agents/skills/anarlog-workflow .claude/skills/anarlog-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Anarlog Workflow 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 Anarlog Workflow 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 Anarlog Workflow 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.

Anarlog workflow

Apply to Anarlog repository or Anarlog desktop, web, mobile, and API work. Apply the same workflow in other checkouts and worktrees.

Linear is the system of record. Chat and the repo are not the archive.

Start work immediately

  1. Default to execution. When the user requests a change, reports a bug, or scopes work, start investigating and implementing immediately and carry it through to completion. Recording it in Linear is part of the work, not the deliverable or a reason to stop.
  2. Only use a discussion-first workflow when the user explicitly asks for brainstorming, exploration of ideas, or planning without implementation. Answer informational questions directly.
  3. Search Linear issues, documents, and comments for the topic, IDs (ANLG-123), and nearby decisions alongside execution. Read matching issues/docs and the team's Agent lessons early enough to inform relevant implementation decisions. Do not make this pass a prerequisite for starting useful work.
  4. Reuse an existing issue when one fits. Create one only when nothing covers the work, on the right team, attached to the existing Linear project when one already tracks that surface. Keep the issue current as work proceeds; do not stop after creating or updating it.
  5. If Linear is unavailable, continue authorized work and report the recording gap. Existing approval requirements for external actions and shared history still apply.

Record everything

  • Work lives on a Linear issue. If the user states a decision, files a bug, or scopes new work, write it to Linear in the same turn.
  • Progress, decisions, blockers, and handoffs go on that issue as comments.
  • Durable specs, research, and product context go in Linear documents on the team or project.
  • Link the issue in commits/PRs when one exists (ANLG-123).

Record lessons as you go

A lesson is anything the next agent would otherwise rediscover: a failed approach, a non-obvious constraint, an architectural decision, a prod/debug gotcha, or a corrected assumption.

  • Write it immediately. Do not wait for a wrap-up.
  • Ticket-specific: comment on the issue under ## Lesson.
  • Reusable across tickets: append to that team's Agent lessons document (create it if missing). Newest first: date, one-line title, what we learned, what to do next time.
  • Do not dump routine status into lessons. Do not leave important context only in the chat.

Frequently asked questions

What does the Anarlog Workflow AI skill do?

Execute Anarlog work immediately while recording issues, decisions, progress, and lessons in Linear. Use for Anarlog repository or Anarlog desktop, web, mobile, and API work, including related worktrees and ANLG issues. Explicit brainstorming stays discussion-first. Do not use for unrelated repositories or meeting-data queries.

Why use Anarlog Workflow on TypingMind?

Because you install it once and use it with any model. Anarlog Workflow 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 Anarlog Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/modem-dev/ossrules/tree/main/public/files/anarlog/.agents/skills/anarlog-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Anarlog Workflow?

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 Anarlog Workflow?

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

Is the Anarlog Workflow AI skill free?

Yes. It is published on GitHub by modem-dev 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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