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Condense

Community
notque
condense

Maximize information density: preserve all instructions, remove prose filler.

Overview

Publishernotque
Repositoryvexjoy-agent
Skill namecondense
Stars
443
Forks
50
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 notque on GitHub. Read the source before you install it.

Installation

Install the Condense 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/notque/vexjoy-agent/tree/main/skills/code-quality/condense
  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/notque/vexjoy-agent.git /tmp/vexjoy-agent
mkdir -p .claude/skills
cp -r /tmp/vexjoy-agent/skills/code-quality/condense .claude/skills/condense
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Condense 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 Condense 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 Condense 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.

Condense

Strip prose filler from .md files. Preserve every instruction. This skill practices what it preaches.

Phase 1: SCOPE

Identify targets.

  1. Single file: User names a path. Read it.
  2. Glob: User gives a pattern (agents/*.md). Expand, list matches, confirm with user.
  3. Batch (10+ files): Dispatch parallel agents, one per file.

Mechanical pre-pass (deterministic, run before LLM condensing): strip trailing whitespace and consecutive blank lines that inflate Opus token counts. The script handles the mechanical reduction so the LLM phase focuses on prose density.

bash
python3 scripts/check-whitespace.py --fix <target-file-or-dir>   # 0=clean, 1=violations fixed

Run on the scoped targets (defaults to agents/**/*.md and skills/**/*.md when no path given). Then proceed to the LLM pass on the same files.

Gate: At least one target file identified and readable; mechanical pre-pass run.


Phase 2: CONDENSE

For each file:

  1. Read the full file. Record word count.
  2. Rewrite in place applying the rules below.
  3. Record new word count.

Rules

KEEP (never cut):

  • Every instruction, rule, gate, phase, step
  • Tables, code blocks, commands, paths
  • YAML frontmatter (do not alter)
  • Structure: headers, numbered lists, phase ordering
  • Technical terms naming specific things
  • Reference loading tables
  • Error handling sections
  • Non-obvious "because X" reasoning

CUT:

  • Redundant restatements of the same rule
  • "Because X" on obvious rules
  • Motivational framing ("this will help you", "it is important to note")
  • Filler phrases: "in order to", "it should be noted that", "it is worth mentioning"
  • Examples that repeat what the phase already says
  • Paragraphs saying the same thing from different angles -- merge to one

STYLE: Short sentences. Active voice. Concrete words. If you can cut a word without losing an instruction, cut it.

DELETE TEST

Before cutting any sentence: "If I remove this, does the reader lose an instruction, rule, or decision?" No = cut. Yes = keep.

Boundaries

Do not reorganize sections, change meaning, add ideas, alter paths/commands, drop tables or code blocks, or modify YAML frontmatter values.


Phase 3: VERIFY

For each condensed file:

  1. YAML check: Confirm frontmatter parses.
    bash
    python3 -c "import yaml; yaml.safe_load(open('<file>').read().split('---')[1])"
  2. Report: Show | File | Before | After | Reduction | table with word counts.
  3. Instruction check: Grep original for key terms (phase names, gate names, commands). Confirm each appears in condensed version. If any missing, restore from original.

Gate: YAML parses. No instructions lost. Reduction reported.


Error Handling

No prose to cut: Report 0% reduction, move to next file.

Instruction removed: Re-read original, restore missing instruction, re-verify.

YAML broken: Restore original frontmatter verbatim, re-condense body only.

Non-.md file: Skip with warning.

Frequently asked questions

What does the Condense AI skill do?

Maximize information density: preserve all instructions, remove prose filler.

Why use Condense on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/notque/vexjoy-agent/tree/main/skills/code-quality/condense. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Condense?

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 Condense?

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

Is the Condense AI skill free?

Yes. It is published on GitHub by notque 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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