Workflow From Chats logo

Workflow From Chats

OrganizationPopular
cursor
workflow-from-chats

Extract durable working preferences from recent Cursor chats and convert them into skills, rules, or workflow docs. Use when asked to learn preferences, mine feedback, personalize workflows, or generate team/person-specific agent guidance.

Overview

Publishercursor
Repositoryplugins
Skill nameworkflow-from-chats
Stars
8K
Forks
728
Bundled files
Instructions only
Links
Skill sourceRepository
  • 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 cursor on GitHub. Read the source before you install it.

Installation

Install the Workflow From Chats 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/cursor/plugins.git /tmp/plugins
mkdir -p .claude/skills
cp -r /tmp/plugins/cursor-team-kit/skills/workflow-from-chats .claude/skills/workflow-from-chats
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Workflow From Chats 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 Workflow From Chats 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 Workflow From Chats 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.

Workflow From Chats

Infer durable working preferences from recent chats. Do not summarize chats; extract reusable workflow guidance.

Scope

  • Default to the last 7 days unless the user asks for a different window.
  • Read parent transcripts and relevant subagent transcripts. Use subagent content as evidence, but cite only parent conversations.
  • Do not expose local transcript paths, secrets, customer data, private chat content, or credentials.

Workflow

  1. State the target workflow or preference surface in one paragraph.
  2. Build an internal transcript inventory: title/topic, parent conversation ID, approximate date, completion state, relevant subagents, and why it may contain preference evidence.
  3. Scan for explicit preferences, corrections, and workflow markers such as "I prefer", "always", "never", "not what I asked", "stop", "review", "PR", "CI", "logs", and "skill".
  4. Extract preference atoms: trigger, workflow step, decision rule, quality bar, stop condition, evidence, and confidence.
  5. Rate confidence as strong, medium, weak, or contradicted.
  6. Cluster by workflow shape rather than transcript: shipping, review, simplification, debugging, capture, communication, delegation, or validation.
  7. Choose the artifact: new skill, skill edit, rule, workflow doc, or no artifact.
  8. Draft only the reusable guidance. Filter anecdotes that will not help future tasks.

Confidence

  • Strong: explicit user preference, workflow-changing correction, repeated parent-chat pattern, or direct request to encode behavior.
  • Medium: accepted workflow, repeated tool/model/validation preference, or subagent consensus that the parent used successfully.
  • Weak: agent-chosen behavior with no user feedback, one ambiguous transcript, or a likely task-specific correction.
  • Contradicted: evidence points in incompatible directions; ask the user before writing files.

Artifact Choice

  • Skill: recurring multi-step workflow with clear triggers.
  • Rule: general behavior that should apply broadly.
  • Workflow doc: useful context that is not reliably triggerable.
  • No artifact: situational, stale, or low-confidence observation.

Output

Return a concise synthesis first:

  • Target workflow.
  • Evidence corpus with parent conversation citations only.
  • Preference profile.
  • Adopt, consider, dismissed.
  • Proposed artifacts.
  • Open questions only if they block writing.

Frequently asked questions

What does the Workflow From Chats AI skill do?

Extract durable working preferences from recent Cursor chats and convert them into skills, rules, or workflow docs. Use when asked to learn preferences, mine feedback, personalize workflows, or generate team/person-specific agent guidance.

Why use Workflow From Chats on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cursor/plugins/tree/main/cursor-team-kit/skills/workflow-from-chats. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Workflow From Chats?

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 Workflow From Chats?

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

Is the Workflow From Chats AI skill free?

It is published on GitHub by cursor. Check the repository for licensing terms. 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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