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Claudex Loop

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chaseai-yt
claudex-loop

Harden a plan with independent Claude/Codex review, then optionally build and cross-inspect it. Start in either Claude Code or Codex: the host plans and the other provider reviews. Use for claudex this plan, claudex-loop, or the legacy crucible trigger; not for trivial edits.

Overview

Publisherchaseai-yt
Repositoryclaudex-loop
Skill nameclaudex-loop
Stars
2.1K
Forks
206
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

    Published by chaseai-yt on GitHub. Read the source before you install it.

Installation

Install the Claudex Loop 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-loop .claude/skills/claudex-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Claudex Loop 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 Loop 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 Loop 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 Loop

The current conversation owns requirements, planning and coordination. The other provider reviews the plan. Either provider can build; the provider that did not build inspects the final code in a fresh session.

Resolve roles once

Identify the actual host from your runtime, not PATH, installed skills, model-name guesses, or the repository. Both CLIs may be installed. Use host=claude in Claude Code and host=codex in Codex. If the runtime identity is unavailable, ask which host the user is using once.

HostRequirements and planPlan reviewerDefault builderFinal inspector
Claude CodeCurrent Claude sessionCodexClaudeFresh Codex session
CodexCurrent Codex sessionClaudeCodexFresh Claude session

Honor builder=claude|codex. The inspector is always the other provider. The host remains coordinator even when the other provider builds. To swap the planner, start the conversation in the other host; do not pretend a CLI reviewer is the user's planning conversation.

Model selection is independent of provider roles. Preserve the host's selected model. Review/build CLI calls inherit their own configuration unless reviewer_model, builder_model, or inspector_model is supplied; map these to the runner's --model for that invocation. Apply an explicit *_effort similarly. Fable 5.1 and GPT-6 Astra are suitable explicit choices, not mandatory pins. A model in the host UI does not prove which model a separate CLI will use. Report requested and observed model information separately; report an unresolved CLI default honestly. Never silently fall back to another model/provider on a failure.

Read the runtime reference before launching a CLI. Resolve its runner relative to this installed SKILL.md, never relative to the project being reviewed. Use absolute paths when launching it.

If the user supplies codex_cli or claude_cli, map the selected provider's executable path to --cli. A host app can have a newer working binary than the CLI on PATH; verify the path and version without silently changing global installation or configuration.

Tunables

ArgumentDefaultMeaning
PLAN_FILE / planPLAN.mdPlan path used throughout, including the build handoff
LOG_FILE / logPLAN-REVIEW-LOG.mdAppend-only transcript
rounds / MAX_ROUNDS5Maximum completed plan-review rounds
builderhostProvider implementing the plan
researchproportionate to tasknone, web, or explicit opt-in deep
modefullfull includes recon/interview; review starts from an existing plan
inspectonoff only when the user explicitly opts out; record it
MAX_FIX_ROUNDS2Bounded build-fix attempts before reporting or taking over
MAX_INSPECTION_ROUNDS2Initial inspection plus one after fixes

Echo roles, paths, round limits, requested models and inspection opt-out before starting. Preserve existing authorization: a request to plan does not authorize building; a request to plan and implement does. Do authorized preparation before seeking any remaining sign-off.

Phase 0 — Recon

For existing projects, inspect relevant code, dependencies, callers and writers of shared state. Read existing CONTEXT.md / CONTEXT-MAP.md and relevant ADRs. For greenfield work, research prior art, a reasonable stack and concrete failure modes when useful. Respect an explicit research depth. Deep multi-agent research requires explicit opt-in and an available tool; otherwise use supported targeted research, and report the limitation. Do not require a proprietary Workflow tool or hard-code a research-agent model.

Discover relevant skills through the host's available catalog and the other provider's documented skill locations when accessible. Record only relevant proposed dependencies. Do not assume host MCP, browser, credentials or skills transfer to the other CLI. Verify required build capabilities before relying on them.

Present one assumptions ledger with source paths or research links. Ask for corrections to material uncertainties as a batch. Resolve routine reversible choices yourself when the user has authorized the work; silence is not approval of an action requiring approval.

Phase 1 — Settle requirements

Maintain a short visible decision map. Ask only about unresolved decisions that change the outcome. For each consequential question, give the recommendation, why it matters, and the cost of guessing wrong. Batch independent questions; ask dependent ones sequentially. If the code can answer, inspect it instead. Offer “accept all remaining recommendations” when a long decision list would slow the user down.

Respect existing glossary definitions; resolve ambiguous domain language. Maintain glossary-only context lazily using CONTEXT-FORMAT.md. Record an ADR only for expensive-to-reverse, non-obvious trade-offs using ADR-FORMAT.md.

Write the resolved PLAN_FILE with:

  • Goal and observable acceptance criteria.
  • Concrete approach, key decisions, trade-offs and non-goals.
  • Confirmed assumptions with sources and remaining risks.
  • Relevant toolchain requirements per provider, if any.
  • Verification: exact proof command(s), expected results, and manual/visual checks when needed.

Derive proof commands from the repository when possible. Ask only when what counts as success remains unclear. Start the append-only LOG_FILE with roles, model requests, scope, authorization and round limits. Keep run diagnostics outside the checkout.

With mode=review, load the supplied plan, fill only material gaps with the user as needed, and proceed directly to review; do not restart a requirements interview.

Phase 2 — Independent plan review

Use the shared runner in review mode with the actual --host, resolved --plan and optional model/effort. First round creates a session. Further rounds use --resume <previous-successful-result.json> with the same provider/model/effort and a host-authored --feedback file containing dispositions. Never use a guessed session id, --last, or a build session as a reviewer.

Each successful response contains a verdict, evidence-backed findings, actual coverage and limitations. Preserve the entire response and runner result path in LOG_FILE.

  • APPROVED: no unresolved material defects. Approval is bound to the exact plan path and SHA256. Present remaining low-priority advice and limitations; zero findings is valid and is not proof of exhaustive correctness.
  • REVISE: the host arbitrates each finding. Implement warranted plan changes; reject unsupported suggestions with reasons. Record dispositions and send the revised plan to the same reviewer. Avoid relitigating resolved points without new evidence.
  • BLOCKED / failed process / malformed result: never count this as approval. Explain the actual missing evidence or operational failure. Do not burn remaining rounds on blind retries or switch providers silently.

Stop at MAX_ROUNDS. Present unresolved findings and the host's position instead of manufacturing convergence. A changed plan requires another review. Before building, run the approval check on the final plan. If the user explicitly chooses to proceed without independent approval, record that override and use the standalone unreviewed-spec path; never label it approved.

Phase 3 — Build and inspect

Present the reviewed plan, improvements and remaining limits. If implementation is not already authorized, ask for that final decision. Use the selected builder, defaulting to the host. Read the build reference.

The host can implement directly with its normal tools. For a different builder, use the shared runner's build mode. Either path must capture the pre-build commit, preserve unrelated user work, and carry the same resolved plan and verification contract.

Run the agreed proof checks, inspect all changed files and review the result through the other provider in a fresh inspect session. Supply the pre-build commit and builder identity. Reinspection after accepted fixes also uses a fresh session. Log findings and dispositions; rerun affected proof checks after fixes.

If the coordinator takes over coding, it has become a builder. Require a fresh other-provider inspection of its changes; never describe the earlier inspection as covering later edits. If both providers contributed code, record authorship and have each inspect the other's changes; do not claim any model independently reviewed code it authored. If the inspection budget is exhausted, report remaining findings and unreviewed edits explicitly for the user's decision.

Present the final diff, proof results, inspection coverage, unresolved findings, deviations and rounds used. Honor existing commit/push authorization; otherwise leave the concrete diff ready for sign-off. External publication is never implied merely by running the loop.

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 Claudex Loop AI skill do?

Harden a plan with independent Claude/Codex review, then optionally build and cross-inspect it. Start in either Claude Code or Codex: the host plans and the other provider reviews. Use for claudex this plan, claudex-loop, or the legacy crucible trigger; not for trivial edits.

Why use Claudex Loop on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chaseai-yt/claudex-loop/tree/main/skills/claudex-loop. 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 Claudex Loop?

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 Loop?

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

Is the Claudex Loop 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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