Compact Docs Writer logo

Compact Docs Writer

Organization
eai-org
compact-docs-writer

Draft, rewrite, or refine a doc for maximum token economy without losing any rule or intent. Use for docs kept in version control and regularly re-read by agents; skip throwaway docs like plans.

Overview

Publishereai-org
Repositoryagent-toolkit
Skill namecompact-docs-writer
Stars
80
Forks
18
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by eai-org on GitHub. Read the source before you install it.

Installation

Install the Compact Docs Writer 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/eai-org/agent-toolkit/tree/main/skills/compact-docs-writer
  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/eai-org/agent-toolkit.git /tmp/agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit/skills/compact-docs-writer .claude/skills/compact-docs-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Compact Docs Writer 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 Compact Docs Writer 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 Compact Docs Writer 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.

Compact docs writer

Rewrite a doc — or draft a new one — for token economy: carry all its rules and intent in the least text possible, because the doc loads into agent context and is paid for on every read. Then prove nothing was lost: present the change with a word delta measured from the files, and apply only on approval.

Core principle

Write each piece of information with the least text that still preserves every rule, constraint, edge case, and intent. Two directions, equally binding:

  • Cut duplication, filler, and anything restatable more briefly.
  • Never drop text whose removal loses information or instruction, just to be shorter.

Recurring reflex: "Can this exact rule be said in fewer words?" — if yes, do it.

The no-op test licenses one more deletion. Ask of each sentence, in isolation: "does it change the agent's behaviour versus its default?" If not, it's a no-op — the agent already acts this way, so removing it loses nothing: delete the whole sentence rather than trimming words from it.

Compaction counts words and information density, not whitespace. Blank lines between distinct chunks cost effectively nothing and aid the human reader, so keep them where they help; never collapse a long passage into one dense block to look shorter. The same economy runs both ways: a human-readability gain that is free or near-free in tokens — a blank line, a line break, a semicolon-chained enumeration rendered as a bullet list — is always applied, never skipped to look compact.

Structure follows the same economy: co-locate a concept — its rule, exceptions, and caveats under one heading, never scattered — so a reader who jumps to one part gets the others with it.

Leading words

When one concept keeps getting restated, collapse it into a single leading word the model already carries from pretraining, and reuse that word wherever the concept applies: it anchors the same behaviour in one token and reads sharper than any paraphrase. The collapse still obeys the core principle — the word must carry every constraint it replaces, and whatever it doesn't carry stays spelled out: "fast, low-overhead feedback" collapses into a tight loop, but a "deterministic" requirement isn't inside tight, so it survives as its own word. Hunt for these collapses in every pass.

Workflow

  1. Compact. Rewrite the target to meet the core principle in one pass — a first draft already meets the standard; don't ship a loose draft expecting a later pass to tighten it. If a chunk is needed only in a sub-case and is big enough to tax every read, you may suggest extracting it into a referenced file — never force it; the enforced standard is compact text, not splitting.
  2. Self-review before presenting (terse yes/no):
    • Every original rule, instruction, edge case, and intent still present?
    • Every surviving rule in the fewest words — tight phrasing, not just free of redundancy? Answer by drafting a shorter rival phrasing for each new or rewritten sentence, not by re-reading: an unchallenged yes is a rubber stamp.
    • Removal audit against the rendered diff, not memory: read every removed line — and every reordered or merged one, which count as removals — and confirm each drops only duplication, filler, or a verified no-op, never a load-bearing rule, instruction, edge case, or nuance. After a merge, re-verify the result still carries every item from both sources.
    • Addition audit against the same rendered diff: every added sentence must pass the no-op test — cut any that fail.
    • scripts/check-doc.sh passes on the draft, run with CHECK_DOC_AS=<target path> while it sits in scratch? Wrap and link conventions a script can hold; fix every failure.
  3. Present & confirm. Show the change as a unified diff inside a fenced diff code block — every removed line prefixed -, every added line +, so they render red/green — and when a long line changes by only a few words, add a word-level view ([-removed-]{+added+}) pinpointing them. Include a word/token delta measured from the files, never estimated: write the not-yet-applied draft to a scratch file (in the session's temp/scratch dir, never the working tree) and wc -w it against the original. Label it not yet applied and awaiting approval; apply only on approval; after applying, say so plainly. Ask for approval in the presentation text or in a later turn, never via a question tool call in the same turn: text emitted before a tool call may not be displayed, so the question would land without the draft.

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 Compact Docs Writer AI skill do?

Draft, rewrite, or refine a doc for maximum token economy without losing any rule or intent. Use for docs kept in version control and regularly re-read by agents; skip throwaway docs like plans.

Why use Compact Docs Writer on TypingMind?

Because you install it once and use it with any model. Compact Docs Writer 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 Compact Docs Writer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/eai-org/agent-toolkit/tree/main/skills/compact-docs-writer. 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 Compact Docs Writer?

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 Compact Docs Writer?

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

Is the Compact Docs Writer AI skill free?

Yes. It is published on GitHub by eai-org 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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