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Habit Words

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joetawil7
habit-words

Learns the words a user habitually writes to their coding agent that make its answers worse ("be 100% sure", "don't assume", "full review", "are you sure?", "all fine, right?"), from what they actually typed in their recent Claude Code sessions. Shows what each one does, what went wrong after it, and what to say instead, then writes the habit words block that maps each word to the checks it should trigger, so the checks run without the words. Use when asked to learn, read, refresh or review the user's habit words or prompts, "which of my words make you worse", from setup-first-pass's profile step, or when the start-of-session check says the habit words are due.

Overview

Publisherjoetawil7
Repositoryfirst-pass
Skill namehabit-words
Stars
113
Forks
0
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 joetawil7 on GitHub. Read the source before you install it.

Installation

Install the Habit Words 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/joetawil7/first-pass/tree/main/skills/habit-words
  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/joetawil7/first-pass.git /tmp/first-pass
mkdir -p .claude/skills
cp -r /tmp/first-pass/skills/habit-words .claude/skills/habit-words
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Habit Words 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 Habit Words 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 Habit Words 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.

habit-words

"Be 100% sure" or "make it bug free" names no place to look, so it changes how sure the answer sounds, not what gets checked. This skill finds the words this user writes, from their own prompts, and maps each to the checks it should mean. The user never has to write them again, and when they do, the words trigger the checks instead of a more certain tone.

The block this skill writes follows ${CLAUDE_SKILL_DIR}/../setup-first-pass/assets/words-block.md: read it first, and keep its markers and shape.

1. Read the sessions

Plugin: node "${CLAUDE_SKILL_DIR}/../../scripts/cli.mjs" words reads the user's last 20 Claude Code sessions that hold a prompt they typed (--sessions <n> for another number), from ~/.claude/projects (or $CLAUDE_CONFIG_DIR/projects). It writes only what they typed to a temp file and prints the file's path, what it left out, the newest prompt's time, and how many prompts hit each phrase family. Left out: tool output, pasted text, notifications, messages from other sessions, skill and system text, commands, compaction summaries and turns a script started (claude -p). Prompts queued while the agent was busy are kept: they are often the corrections. The one thing kept that the user did not type is the end of the agent's reply before each prompt tagged "possible pushback". Anything that looks like a key, token, password, email or phone number is replaced: best effort, so never quote a line that still looks like one. Each run writes one new file; note every path.

Without the plugin (another tool), ask the user to paste 30 to 50 recent prompts, and work from those.

Say the coverage before the findings: sessions, dates, prompts, what was left out. Read the whole file. Past about 800 prompts, read every prompt the families or the pushback marker tag, plus a sample of 150 of the rest, and say so.

2. Find the words

  • The families are candidates, not verdicts. Keep a phrase where it asks for certainty or completeness without naming a check ("be 100% sure", "cover all cases"); drop it where it names one ("make sure the tests pass", "check if this is still true").
  • Find the user's own phrases the families miss, by reading: stacked demands ("dont assume, be 100% confident, cover all gaps" in one breath), yes-shaped questions, absolutes ("no hacking is possible"), frustration, open grants, and the ones that work well.
  • Count every phrase you report: node "${CLAUDE_SKILL_DIR}/../../scripts/cli.mjs" words --count "<regex>" (repeat --count for several; one read), with the same --sessions as step 1, so the counts cover the prompts you read. Every number shown comes from a count, never from reading. Prompts where the user talks about the words (like a request to run this skill) are counted too: say how many.
  • The impact: a prompt tagged "possible pushback" shows the end of the reply before it. A habit word followed, in the same session, by the user correcting the result ("reread your numbers", "that's wrong", "is it really working?") is the evidence. Quote at most a few words of a prompt, never a whole one, and never names, contacts, amounts or anything secret.

3. Map each word to checks

For each word keep:

  • what the user writes, in their spelling;
  • how often (prompts, sessions), from a count;
  • what it does to an agent, in one line (why the answer gets worse, not better);
  • what happened after it, with the date, where the sessions show it;
  • the check it now triggers: the working rule that answers it, by its name (Evidence, the pre-mortem, Done means proven, Say the coverage, No yes by default, Pushback gets checked, Bugs: reproduce first, Grants stay narrow, One step at a time), or, when no rule covers it, one new instruction in the same style;
  • what to say instead: the words that name the check.

Also list the prompts that worked (they named the check, the source or the scope), so the user keeps them.

4. Show the user

A table: You wrote · How often · What it does · What happened · Say instead, the prompts that worked, then the block you will write. Ask before writing it.

5. Write the block

With a yes, write the block between its markers: the heading, one line saying the words mean the checks and never a more certain tone, and one bullet per word: "<the user's words>" (<n> prompts): <the check it triggers>. The start marker is

<!-- first-pass:words:start v<plugin version> through <newest prompt's time from step 1> from <n> sessions (written by the habit-words skill from the user's own prompts; re-run it to refresh) -->

The start-of-session check reads through to say when the words are due again: once 20 sessions the user typed in have changed since. If the user says "not now" to a refresh, set through in the existing marker to the current time and change nothing else.

Where: never in a file teammates share, because these are one person's words. So:

  • in a main folder, right after the profile block in the root AGENTS.md;
  • in a single repo, or wherever the profile block sits in a file the repo commits, in ~/.claude/CLAUDE.md instead;
  • an existing words block is replaced where it is, if that file is not shared; otherwise it is moved to ~/.claude/CLAUDE.md, and the report says so.

Never edit outside the markers.

6. Clean up

Delete every file step 1 wrote (each words run writes one): they hold the user's prompts. node "${CLAUDE_SKILL_DIR}/../../scripts/cli.mjs" words --delete "<file>" for each; it fails if the name matches no file, so a mistyped name never reads as deleted. A file an unfinished run left is removed by the next run once it is 6 hours old. The report ends with:

Read: <n> prompts from <n> sessions, <first date> to <last date>; left out <what>
Words: <n> mapped (<n> new instructions), block <written to file | not written, because>
Temp files: <n> written, <n> deleted

Frequently asked questions

What does the Habit Words AI skill do?

Learns the words a user habitually writes to their coding agent that make its answers worse ("be 100% sure", "don't assume", "full review", "are you sure?", "all fine, right?"), from what they actually typed in their recent Claude Code sessions. Shows what each one does, what went wrong after it, and what to say instead, then writes the habit words block that maps each word to the checks it should trigger, so the checks run without the words. Use when asked to learn, read, refresh or review the user's habit words or prompts, "which of my words make you worse", from setup-first-pass's profil...

Why use Habit Words on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/joetawil7/first-pass/tree/main/skills/habit-words. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Habit Words?

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 Habit Words?

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

Is the Habit Words AI skill free?

Yes. It is published on GitHub by joetawil7 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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