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Research Lead Sidecar

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cnfjlhj
research-lead-sidecar

Use when the user wants multi-agent division of labor for research-led work and the lead should stay on the critical path while 1-2 bounded sidecars handle low-coupling tasks. Do not use this for tiny tasks, fully sequential debugging, or overlapping refactors.

Overview

Publishercnfjlhj
Repositoryai-collab-playbook
Skill nameresearch-lead-sidecar
Stars
455
Forks
44
Bundled files
9
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.

  • 9 bundled files

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

  • Open source

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

Installation

Install the Research Lead Sidecar 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/research-lead-sidecar
  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/research-lead-sidecar .claude/skills/research-lead-sidecar
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Lead Sidecar 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 Research Lead Sidecar 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 Research Lead Sidecar 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.

Research Lead Sidecar

Overview

Use this skill for research-led work where implementation and evidence gathering are coupled, but the main agent should remain the lead. It keeps the critical path local while bounded sidecars handle non-blocking scout, worker, verifier, or writer tasks.

After the user approves an execution contract, use this as the default delegation shape: one lead owns framing, synthesis, and user-facing updates while 1-2 bounded sidecars help without taking over the mission.

Rules

  • Keep the lead responsible for framing, synthesis, and final decisions.
  • If the sidecar plan has not been approved yet, do not launch sidecars. First encode the roles and scopes in the execution contract.
  • Delegate only tasks that are non-blocking, bounded, inspectable, and recoverable.
  • Prefer 1-2 sidecars with clear roles instead of spawning a crowd by default.
  • The lead should keep moving on the critical path instead of waiting idly for sidecars.
  • Allow write sidecars only when ownership and file scope are explicit and disjoint. If scopes overlap, keep sidecars read-only.
  • Use milestone verification and durable workspace state only when the task span justifies them.
  • Each sidecar should return something the lead can inspect quickly: a patch, result summary, failure analysis, evidence table, or focused recommendation.
  • The lead owns integration: accept, adapt, or reject sidecar results instead of delegating away final judgment.

When to Use

Use when:

  • the user explicitly wants 多智能体分工, research-led multi-agent work, or long-task governance with a lead agent
  • there is one critical path plus a small number of low-coupling side tasks
  • sidecars can return artifacts the lead can inspect quickly
  • low-touch continuous execution is desired, but full handoff would be too lossy

Do not use when:

  • the task is tiny, fully sequential, or blocked on each previous result
  • several agents would need to touch the same hot files or same narrow context
  • handoff cost is higher than simply doing the work locally

Execution Contract

When using this skill under an approved execution contract, keep the protocol aligned:

  • During alignment:
    • say why lead-sidecar is the chosen lane
    • name the likely sidecar roles
    • state what remains on the lead's critical path
    • wait for approval before spawning
  • After approval:
    • the lead may spawn bounded sidecars without asking again for each ordinary delegation
    • the lead should continue on the critical path while sidecars run
    • sidecar outputs should be integrated at checkpoint boundaries, not left dangling
  • During continuation:
    • brief follow-ups such as 继续 should resume the active lead-plus-sidecar lane
    • do not spawn duplicate sidecars if a still-relevant one is already active

Checkpoint Contract

At each meaningful checkpoint, the lead should be able to state:

  • current mission / phase
  • current critical-path task
  • active sidecars and their scopes
  • what has returned and what still matters
  • the next integration step

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 Research Lead Sidecar AI skill do?

Use when the user wants multi-agent division of labor for research-led work and the lead should stay on the critical path while 1-2 bounded sidecars handle low-coupling tasks. Do not use this for tiny tasks, fully sequential debugging, or overlapping refactors.

Why use Research Lead Sidecar on TypingMind?

Because you install it once and use it with any model. Research Lead Sidecar 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 Research Lead Sidecar in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cnfjlhj/ai-collab-playbook/tree/main/skills/full/research-lead-sidecar. 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 Research Lead Sidecar?

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 Research Lead Sidecar?

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

Is the Research Lead Sidecar 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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