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Qwenchance

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thananon
qwenchance

Keeps a long Claude Code task on-track — breaks out of looping/circular thinking, watches the context budget, bounds internal reasoning, and triggers a clean handoff before the window fills. Use when the model is repeating steps, re-reading the same files, second-guessing in circles, stuck or spinning, or running a long multi-step task at risk of exhausting context. Also use when the user says it is "looping", "going in circles", "stuck", "repeating itself", or asks for a handoff before running out of context.

Overview

Publisherthananon
Repository9arm-skills
Skill nameqwenchance
Stars
3.2K
Forks
424
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 thananon on GitHub. Read the source before you install it.

Installation

Install the Qwenchance 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/thananon/9arm-skills.git /tmp/9arm-skills
mkdir -p .claude/skills
cp -r /tmp/9arm-skills/skills/productivity/qwenchance .claude/skills/qwenchance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qwenchance 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 Qwenchance 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 Qwenchance 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.

Staying on Track

Long, multi-step work fails three ways: looping, over-thinking, and running out of context. Run the checklist below before each step. When a trigger fires, do the matching action — don't deliberate about it.

Before each step — run this

CheckTrigger fires when...Do this
Looping?You're about to repeat an action (see signals below)Break the loop — pick one fix below
Over-thinking?You've reasoned past ~1000 words without actingStop. Act on your current best decision, or ask the user one question
Context tight?A low-context reminder appeared, or 2+ budget signals holdFinish this step, then hand off

If nothing fires, take the step.

1. Loops — detect and break

A step is a loop if any of these is true:

  • You're re-reading a file you already read this session (and it has not changed since).
  • You're re-running a command/tool with the same args, expecting the same result.
  • You're returning to a hypothesis you already tried and dropped.
  • You're "reconsidering from the start" with no new evidence.
  • The last 2 steps gained no new information.

Re-reading a file you just edited is NOT a loop — that's verifying.

When a loop fires, stop and do exactly one:

  1. State the blocker in one sentence and ask the user a specific question.
  2. Write what you know vs. don't know, then take a different action than last time.
  3. Looped 2+ times on the same sub-problem? Declare it unsolved-for-now; move on or hand off.

Never repeat a failed action hoping for a different result.

Retry cap: never run the same failing command a 3rd time. Can't get something working (a command, a test runner, an import) after ~3 attempts — even varied ones — STOP and ask the user; don't grind through more variations.

Don't edit blind — it's the top loop source. Read enough to know the change is correct before editing. After each edit, verify it (read the diff / run it / run the test) before the next step. One edit → one check.

2. Thinking — keep it bounded

Cap reasoning at ~1000 words per step. Past that, you're deliberating instead of acting.

  • Decide → act → observe. Don't re-derive a decision you already made.
  • Can't decide in ~1000 words? The task is underspecified — ask the user one sharp question.
  • Don't restate the whole problem to yourself. Reference what you concluded; don't rebuild it.

3. Context budget — count signals, don't estimate

Authoritative: A <system-reminder> about low context / approaching auto-compaction. → Hand off now (section 4). Don't start new work.

Otherwise, count how many of these are true right now:

  • 20+ assistant turns into the task.
  • Read 5+ files, or any one huge file/log/dump.
  • Long tool outputs you keep scrolling back to.
  • 3+ plan steps still left.

Count the boxes that are true, then map the count to an action:

  • Count is 0 or 1 → CONTINUE working normally.
  • Count is 2, 3, or 4 → HAND OFF — finish the current step, then go to section 4.

Count first, then decide — don't judge by feel. A higher count means more context pressure, not less. Being on the last step or "almost done" does not lower the count or cancel a HAND OFF.

Before any expensive step (large read, new subtask, long generation), ask: "Room to finish this AND hand off after?" If the count says HAND OFF, finish the current atomic unit, then hand off — don't start the next.

4. Hand off cleanly

When context is tight or the user asks:

  1. Land durable artifacts first — save the file, commit, write the result. Nothing lost.
  2. Invoke the handoff skill to compact the conversation. Don't hand-write the handoff.
  3. Tell the user plainly: "Context is getting tight — handing off now; start a fresh session (/clear)."

Frequently asked questions

What does the Qwenchance AI skill do?

Keeps a long Claude Code task on-track — breaks out of looping/circular thinking, watches the context budget, bounds internal reasoning, and triggers a clean handoff before the window fills. Use when the model is repeating steps, re-reading the same files, second-guessing in circles, stuck or spinning, or running a long multi-step task at risk of exhausting context. Also use when the user says it is "looping", "going in circles", "stuck", "repeating itself", or asks for a handoff before running out of context.

Why use Qwenchance on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/thananon/9arm-skills/tree/main/skills/productivity/qwenchance. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Qwenchance?

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 Qwenchance?

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

Is the Qwenchance AI skill free?

It is published on GitHub by thananon. 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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