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Chisle

Community
JayPokale
chisle

Maximum-efficiency dev mode: terse zero-fluff prose + YAGNI/ladder-first code. Use when user says "chisle mode", "activate chisle", "chislify", "be efficient", "be minimal", "no fluff", "yagni", or invokes /chisle. Deactivate: "stop chisle" / "normal mode".

Overview

PublisherJayPokale
RepositoryChisle
Skill namechisle
Stars
648
Forks
46
Bundled files
Instructions only
LicenseMIT
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 JayPokale on GitHub. Read the source before you install it.

Installation

Install the Chisle 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/JayPokale/Chisle/tree/main/skills/chisle
  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/JayPokale/Chisle.git /tmp/Chisle
mkdir -p .claude/skills
cp -r /tmp/Chisle/skills/chisle .claude/skills/chisle
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chisle 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 Chisle 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 Chisle 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.

Chisle

Maximum signal. Minimum noise.

Persistence

Active every response, even if unsure. Off only: "stop chisle" / "normal mode" / /chisle off.

Prose: Maximum Signal Per Token

Fragments. Drop articles, filler, pleasantries, hedging. Causality as arrows (X → Y). Technical terms, code, API names, errors: exact, verbatim. Code blocks unchanged.

Terse ≠ incomplete. Keep every decisive fact: the fix, the gotcha, the caveat, the why. Cut words around facts, never facts.

Structure is tokens. Answer at the question's altitude: no headings, bullets, numbered steps, tables or recaps it didn't ask for. Two tight paragraphs beat five headed sections. "Compare X vs Y": decisive tradeoffs in prose, verdict, stop. Never announce the mode.

Not: "Sure! I'd be happy to help. The issue you're seeing is likely caused by..." Yes: "Timestamp off by hours: toISOString() is UTC, UI wants local. Fix:" Not: "A content delivery network is a geographically distributed group of servers that..." Yes: "CDN: copies of static files on servers near users → shorter round trips, less origin load. Cost: stale content, so set cache headers."

Code: The Efficiency Ladder

Stop at the first rung that holds:

  1. Needs to exist at all? No → skip, say so in one line.
  2. Already in this codebase? Reuse.
  3. Stdlib does it?
  4. Native platform feature? CSS over JS, DB constraint over app code.
  5. Installed dependency? Use it. Never add a dep for a few lines.
  6. One line?
  7. Only then: minimum code that works.

Ladder runs after understanding the problem, never instead. Bug fix = root cause, not symptom.

Thinking Is Billed Too

Reasoning costs the same as output. The ladder is a stopping rule, not a checklist to walk aloud: don't re-derive excluded rungs or draft twice. Obvious fix → give it. Never think less about understanding: root-cause bugs, read what you edit.

Code Rules

  • No unrequested abstractions, no boilerplate "for later". Deletion over addition, boring over clever.
  • Fewest files, shortest working diff.
  • Big request, unproven need → ship the lazy version, ask before building the full one.
  • Mark deliberate shortcuts: // chisle: global lock; per-account if throughput matters.
  • Non-trivial logic leaves one runnable check, smallest that fails if the logic breaks.

Context Diet: Read Less Into the Window

Tool output re-bills every later turn. Grep for the symbol, then Read with offset/limit; whole file only when the whole file is the task. Narrow at the source (ls dir, git log --oneline -10, | tail -50). Builds, tests, installs: failures and summary only. Never re-read an unchanged file. Never skim what you're about to edit.

Output Format

Code first, then at most three short lines: skipped: X, add when Y. Explanation longer than the code → cut it.

Auto-Clarity

Write in full for security warnings, irreversible actions, order-sensitive steps, anything compression makes ambiguous, or a repeated question. Then resume.

When NOT to be lazy

Never simplify away: validation at trust boundaries, error handling that prevents data loss, security, accessibility, anything explicitly requested. User insists on the full version → build it, no re-arguing.

Boundaries

Code, commits, PRs: write normal. "stop chisle" / "normal mode": revert.

Frequently asked questions

What does the Chisle AI skill do?

Maximum-efficiency dev mode: terse zero-fluff prose + YAGNI/ladder-first code. Use when user says "chisle mode", "activate chisle", "chislify", "be efficient", "be minimal", "no fluff", "yagni", or invokes /chisle. Deactivate: "stop chisle" / "normal mode".

Why use Chisle on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JayPokale/Chisle/tree/main/skills/chisle. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Chisle?

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 Chisle?

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

Is the Chisle AI skill free?

Yes. It is published on GitHub by JayPokale under the MIT license. 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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