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Claudex Route

CommunityPopular
chaseai-yt
claudex-route

Recommend a model and a scoped handoff for a task in Claude Code or Codex. Use when choosing who should handle a task, seeking a second opinion, getting unstuck, or delegating focused work; execute one handoff when requested.

Overview

Publisherchaseai-yt
Repositoryclaudex-loop
Skill nameclaudex-route
Stars
2.1K
Forks
206
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 chaseai-yt on GitHub. Read the source before you install it.

Installation

Install the Claudex Route 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/chaseai-yt/claudex-loop.git /tmp/claudex-loop
mkdir -p .claude/skills
cp -r /tmp/claudex-loop/skills/claudex-route .claude/skills/claudex-route
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Claudex Route 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 Claudex Route 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 Claudex Route 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.

Claudex Route

Choose a useful next step for the task: keep it with the current agent, obtain a second opinion, investigate a blocker, or delegate a bounded piece of work. Give a brief recommendation before any requested execution. This skill is self-contained and does not start Claudex Loop, require a formal plan, or create review logs.

Understand the job

Use the current conversation and just enough relevant project context to identify the host, current model when known, desired outcome, and constraints. Consider ambiguity, code dependencies, verification difficulty, context size, and cost or speed preferences. A short prompt can describe a difficult task. Preserve explicit model and provider choices; do not silently replace them with your preferred pairing.

Treat a request for advice as recommendation-only. A request to route and perform work authorizes the scoped handoff within the user's existing permissions. The skill invocation by itself does not authorize implementation. Ask a question only when a missing fact materially changes the recommendation or authorized work; otherwise state your assumption.

Choose the role before the model

SituationUseful next step
Straightforward task, sufficient context, no reason to delegateStay with the current agent; avoid handoff overhead
A consequential or ambiguous plan is readyAsk another provider to challenge requirements, assumptions, and acceptance criteria before building
An implementation is readyAsk another provider to inspect relevant changes against requirements, including test validity
Repeated attempts have failedGive another provider the reproduction, evidence, and failed approaches; ask for a testable alternative explanation
A separable task has clear inputs and acceptance checksDelegate that piece to a suitable smaller model and inspect its result
The user wants repeated planning, revision, building, and independent inspectionRecommend Claudex Loop; load that separate skill only if the user requests that workflow

The current agent keeps the user's requirements and coordinates the work. A different provider offers another perspective, not guaranteed correctness. A cheaper delegate does not need to be a peer reviewer of the entire architecture.

Select a practical candidate

Start with models available in the user's environment. The following are dated starting points (September 2026), not a permanent leaderboard:

  • GPT-5.6 Luna (gpt-5.6-luna): consider for narrow, repetitive tasks with explicit checks, such as fixtures, extraction, documentation updates, or an isolated helper.
  • GPT-5.6 Terra (gpt-5.6-terra): consider for bounded coding or investigation requiring more judgment and context than the Luna task above.
  • GPT-6 Astra (gpt-6-astra) and Claude Fable 5.1 (claude-fable-5-1): candidates for ambiguous work, difficult debugging, or a substantial independent review. From Codex, Fable is a candidate for cross-provider review; from Claude Code, Astra is a candidate.
  • Claude Sonnet, Opus, or another available model: retain as options when their task fit, existing context, account access, or the user's preference favors them. Cross-provider delegation is optional.

Use local model listings and CLI status/help when accessible without launching a model task. Distinguish listed, authenticated, and proven runnable: none alone establishes the others. If access or the active model is unknown, make the recommendation conditional and explain what needs checking. Do not launch paid comparison calls just to choose a model.

For price-sensitive choices or comparative claims, consult current official OpenAI model information and Anthropic model information. Check Codex usage guidance when using subscription allowances. API token prices are different from subscription usage; task costs also include context transfer, reasoning, retries, and host verification. Do not call Terra cheaper than Sonnet, claim Luna is universally stronger, or promise savings without relevant evidence. If sources cannot be checked, omit numeric claims and label the cost assumption.

Return a short routing brief

Normally use fewer than 200 words:

  • Recommendation: stay here or use a named provider/model for a specific role.
  • Why: one or two reasons tied to this task, plus a material uncertainty if present.
  • Handoff: the bounded assignment, relevant context/files, expected result, permitted actions, and how to check success. If staying here, give the immediate next step instead.

Offer at most one alternative when it helps a real tradeoff. Do not interview the user, generate a catalog of models, or start a plan-review loop. Do not claim to have changed the current session's model; a child CLI invocation is a separate session.

Execute one handoff when requested

Use the selected provider's CLI from the correct project directory, explicitly selecting the recommended or requested model for that call. Check the actual binary's version and help; consult the relevant official Codex non-interactive guide or Claude programmatic guide if needed. Avoid changing global defaults, installing software, or switching models silently to make a call succeed.

Pass a self-contained brief with the goal, relevant requirements and files, constraints, expected output, and verification. For debugging, include failed attempts. For code inspection, identify the comparison baseline and relevant committed, staged, unstaged, and untracked changes. The child does not inherit the conversation. Send prompt text through stdin or a safely handled file; never interpolate arbitrary prompts into shell commands.

For review or diagnosis, use supported read-only project tools and restrict external write-capable tools too; a prompt saying "read-only" is not enforcement. If those restrictions cannot be established, return the prepared handoff and explain the limitation. For authorized edits, use scoped write permissions and preserve existing user changes; use an isolated worktree when concurrent edits would conflict. Never bypass permissions. The host pauses edits to the delegate's files while it works.

Use a fresh session for the one-off handoff, a bounded timeout, and separate stdout/stderr artifacts in a unique temporary directory outside the project. Keep the user informed during long calls. Read the completed result and exit status; an empty response, timeout, or permission failure is not success. Stop and report a failed handoff rather than automatically retrying, escalating to a larger model, or starting another round. If a timed-out process may still run, resolve its status before restarting work on the same files.

Assess findings against evidence, inspect any edits, and run appropriate checks within existing authorization. Report the outcome, checks actually run, and remaining uncertainty. Distinguish the model requested from the model observed; do not invent an observed identity. If a correction is outside the requested work, recommend it without expanding scope. Finish after this handoff and host verification; further delegation follows the user's request.

Frequently asked questions

What does the Claudex Route AI skill do?

Recommend a model and a scoped handoff for a task in Claude Code or Codex. Use when choosing who should handle a task, seeking a second opinion, getting unstuck, or delegating focused work; execute one handoff when requested.

Why use Claudex Route on TypingMind?

Because you install it once and use it with any model. Claudex Route 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 Claudex Route in TypingMind?

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

Which AI models can use Claudex Route?

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 Claudex Route?

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

Is the Claudex Route AI skill free?

It is published on GitHub by chaseai-yt. 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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