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Proactive Explorer

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cnfjlhj
proactive-explorer

Use when missing context may be recoverable from local code, files, logs, or environment evidence before asking the user or making assumptions.

Overview

Publishercnfjlhj
Repositoryai-collab-playbook
Skill nameproactive-explorer
Stars
455
Forks
44
Bundled files
Instructions only
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 cnfjlhj on GitHub. Read the source before you install it.

Installation

Install the Proactive Explorer 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/cnfjlhj/ai-collab-playbook/tree/main/skills/full/proactive-explorer
  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/cnfjlhj/ai-collab-playbook.git /tmp/ai-collab-playbook
mkdir -p .claude/skills
cp -r /tmp/ai-collab-playbook/skills/full/proactive-explorer .claude/skills/proactive-explorer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Proactive Explorer 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 Proactive Explorer 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 Proactive Explorer 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.

Proactive Explorer

Overview

Use this skill to reduce avoidable user questions.

Core principle: search local context before asking the user to restate information the system can gather on its own.

Rules

  • Start with local exploration when the request is ambiguous but likely answerable from the workspace.
  • Prefer fast local tools first: rg, targeted file reads, and focused shell commands.
  • Ask the user only for true product decisions, missing credentials, private preferences, or unresolved contradictions.
  • Stop exploring once you have enough evidence to act. Do not turn simple tasks into a ceremony.
  • Before asking a question, summarize what you already checked and what gap remains.

When to Use

Use when any of the following are true:

  • a missing implementation detail may be recoverable from code, config, docs, tests, or logs
  • you can likely resolve an ambiguous file, function, dependency, or workflow reference locally
  • a bug report or execution choice should be narrowed with workspace evidence before you ask
  • the user message is compressed, but the missing structure is probably already in local context

Do not use when:

  • the answer depends on a decision only the user can make
  • the missing fact is external, time-sensitive, or account-specific
  • the task is already explicit enough to act without more search

Exploration Order

  1. Read local rules and nearby docs such as AGENTS.md, project docs, and relevant config.
  2. Search filenames, code, tests, and logs with rg and targeted reads.
  3. Inspect runtime state with shell commands when needed.
  4. Use web search only when the missing fact is outside the workspace.
  5. Ask the user only for the remaining gap.

Output Pattern

Before asking a clarifying question, be able to state:

  • what you searched
  • what you found
  • what remains unknown
  • why that unknown blocks the next step

Common Mistakes

  • asking for information that local search would reveal in seconds
  • exploring indefinitely after the uncertainty is already low
  • using web search before exhausting local evidence
  • asking broad questions instead of one concrete unresolved decision

Quick Reference

  • Need implementation context: search code and tests first.
  • Need runtime state: inspect logs, processes, ports, and environment.
  • Need design intent: read local rules, docs, and nearby examples.
  • Need outside facts: search the web after local evidence is exhausted.

Frequently asked questions

What does the Proactive Explorer AI skill do?

Use when missing context may be recoverable from local code, files, logs, or environment evidence before asking the user or making assumptions.

Why use Proactive Explorer on TypingMind?

Because you install it once and use it with any model. Proactive Explorer 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 Proactive Explorer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cnfjlhj/ai-collab-playbook/tree/main/skills/full/proactive-explorer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Proactive Explorer?

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 Proactive Explorer?

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

Is the Proactive Explorer AI skill free?

It is published on GitHub by cnfjlhj. 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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