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Agent skill

Mental Model Mismatch And Onboarding

by hashgraph-online in hashgraph-online/awesome-codex-plugins

A skill your agent uses when designing onboarding, when diagnosing why users keep getting confused, when migrating users from one product convention to another, or when the system genuinely differs…

Apache-2.0Auto-check passedSales & Support

Install Mental Model Mismatch And Onboarding

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill mental-model-mismatch-and-onboarding -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins mental-model-mismatch-and-onboarding --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/mental-model-mismatch-and-onboarding .claude/skills/mental-model-mismatch-and-onboarding && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
mental-model-mismatch-and-onboarding
GitHub stars
1.3k
Token cost
~1.5k tokens
SKILL.md length
700 words
Files
2 (incl. references)
Skills in repo
716
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when designing onboarding, when diagnosing why users keep getting confused, when migrating users from one product convention to another, or when the system genuinely differs…

  • Works in 3 steps: Change the system → Teach the model → Surface the divergence
  • Designing onboarding
  • SKILL.md covers Diagnosing mismatches, Three fix strategies, Onboarding patterns that teach… and Anti-patterns, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Mental Model Mismatch And Onboarding is an agent skill from hashgraph-online/awesome-codex-plugins. Use this skill when designing onboarding, when diagnosing why users keep getting confused, when migrating users from one product convention to another, or when the system genuinely differs from familiar comparable products. Trigger when the user mentions "users don't get it," "we keep getting the same support tickets," or "this is a new pattern they need to learn." Sub-aspect of mental-model; read that first.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/onboarding-patterns.md`).

It sits in Sales & Support, covering Customer support. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • Designing onboarding
  • Diagnosing why users keep getting confused
  • Migrating users from one product convention to another
  • The system genuinely differs from familiar comparable products

Example prompts

  • “users don”
  • “we keep getting the same support tickets,”
  • “this is a new pattern they need to learn.”
  • “/mental-model-mismatch-and-onboarding”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Change the system
  2. Teach the model
  3. Surface the divergence

What it can do on your machine

Read from SKILL.md and the folder at commit 3e1456a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are html).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mental Model Mismatch And Onboarding loads about 1.5k tokens when it runs, and up to ~2k if it reads all its reference files. Until then it costs about 113 tokens; SKILL.md has 700 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~113
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 3e1456a, republished under its Apache-2.0 licence (© hashgraph-online). 700 words, ~1,528 tokens.

Download SKILL.mdSave it as .claude/skills/mental-model-mismatch-and-onboarding/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
mental-model-mismatch-and-onboarding
description
Use this skill when designing onboarding, when diagnosing why users keep getting confused, when migrating users from one product convention to another, or when the system genuinely differs from familiar comparable products. Trigger when the user mentions "users don't get it," "we keep getting the same support tickets," or "this is a new pattern they need to learn." Sub-aspect of `mental-model`; read that first.

Mental-model mismatches and onboarding

When the user's mental model and the system's actual behavior diverge, three things can be done: change the system to match the user's model; change the user's model through onboarding and teaching; or surface the divergence so the user knows when their model doesn't apply.

Diagnosing mismatches

Common signals of mental-model mismatch:

  • Repeated support tickets about the same misunderstanding ("Why doesn't X work?" when X works fine — the user's expectation didn't match).
  • Drop-off at the same flow step (the user reached this step expecting one thing; got another; left).
  • High undo / cancel rate at a particular action.
  • Forum / community questions asking how to do things the system doesn't actually do.
  • Negative reviews mentioning unmet expectations.

Each pattern points to a divergence between user model and system reality. The fix depends on which side to change.

Three fix strategies

1. Change the system

If the user's model is reasonable and many users share it, change the system to match. Often this is also the simpler design.

Example: users expect "save" to commit immediately. If your system actually queues the save for later, either make save immediate or rename the action to reflect the actual behavior.

2. Teach the model

If the system's behavior is genuinely better and users can learn it, invest in onboarding.

Examples:

  • Anti-lock brakes required teaching: "brake firmly, steer; don't pump." Manufacturer campaigns and driver-ed material taught the new model.
  • Spreadsheets taught a new computing model in the early 1980s; once learned, transferable.
  • Modal editors (vim) require learning that "modes" exist; users who learn become extremely productive.

Onboarding that teaches a model:

  • Walks the user through the first task with explicit guidance.
  • Provides explanation in context — not a separate tutorial page.
  • Shows the system's state as actions happen, so the user observes the model.
  • Uses progressive disclosure of complexity over time.
3. Surface the divergence

When the system genuinely differs and you can't (or shouldn't) change the system, surface the difference at the moment it matters.

Examples:

  • A "soft-deleted" item shows "Deleted (recoverable for 30 days)" — surfaces the system's actual model.
  • A subscription cancellation shows "Canceled — access continues until [date]" — surfaces what canceled actually means.
  • A search that ranks by relevance (not chronologically) shows "Sorted by relevance" — explains the unexpected order.

The point is to expose the divergence at the moment of the action, not buried in documentation.

Onboarding patterns that teach mental models

Guided first task

Walk the user through their first concrete task — not "here's a tour" but "let's create your first project."

html
<aside class="onboarding-guide">
  <h3>Let's create your first project</h3>
  <p>1. Click the + button at the top right →</p>
  <!-- The new-project button is highlighted; the user clicks; next step appears -->
</aside>

Active practice teaches the interaction model better than passive watching.

Show full SKILL.md (269 more words)Show less
Inline explanation at the moment of action

When the user encounters a feature whose model differs from common expectations, explain in line:

html
<div class="field">
  <label for="visibility">Project visibility</label>
  <select id="visibility">
    <option value="private">Private (default)</option>
    <option value="team">Team</option>
    <option value="public">Public</option>
  </select>
  <p class="hint">
    Private = only you. Team = everyone in your workspace. Public = anyone with the link.
  </p>
</div>

The explanation prevents knowledge mistakes by surfacing the actual model.

Progressive complexity

Don't expose every feature on day one. Reveal them as the user encounters surfaces where they apply. The user's interaction model grows with their need.

"What's new" surfaces

When you ship a feature whose model users wouldn't predict from prior versions, announce explicitly. A "what's new in v2" panel that explains how the new feature works, with brief examples, prevents widespread misunderstanding.

Anti-patterns

  • Tour-only onboarding. Walk the user through a passive demo. They forget within hours; their interaction model isn't reinforced.
  • Documentation-as-onboarding. "If you have questions, see the help center." Most users don't.
  • Hidden divergences. A system that differs from expectation but doesn't say so. Users assume; produce errors.
  • Over-explanation in normal flows. If you have to constantly explain how things work, the design isn't matching mental models well; redesign rather than over-tutorialize.

Heuristics

  1. The "first surprise" diagnostic. Watch a new user. Where do they say "huh, that's not what I expected"? Each surprise is a mental-model mismatch — fix the system or the surfaced model.
  2. The support-ticket pattern audit. Cluster recent support tickets by the misunderstanding behind them. Patterns reveal mismatches.
  3. The cancel-rate signal. Steps with high cancel rates often mean users reached them with the wrong expectation. Investigate.
  • mental-model (parent).
  • mental-model-system-vs-interaction — distinguishing the two model types.
  • expectation-effect — mental models are the user's expectations.
  • affordance — affordance teaches interaction models at the per-element level.
  • mimicry — borrowing familiar patterns leverages existing models.

© hashgraph-online, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/mental-model-mismatch-and-onboarding of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • references/onboarding-patterns.md

Open the folder on GitHubat commit 3e1456a

Compare with similar skills

Mental Model Mismatch And Onboarding next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Mental Model Mismatch And Onboarding compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Mental Model Mismatch And Onboarding this skillhashgraph-online/awesome-codex-plugins1.3k—~1.5kAutomated safety check: PassApache-2.0
Siftranknoperator/siftrank225—~2.9kAutomated safety check: PassMIT
Supporthaacked/dotfiles134—~2.9kAutomated safety check: PassNone
Customer Support Agentmastra-ai/mastra29k—~2.2kAutomated safety check: PassCustom licence
Ticket TriageBevel-Software/Hexis1001 repos~2.9kAutomated safety check: PassApache-2.0
Add Agent Adaptersafedep/gryph172—~679Automated safety check: PassApache-2.0

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Categories

Questions about Mental Model Mismatch And Onboarding

What does Mental Model Mismatch And Onboarding do?

A skill your agent uses when designing onboarding, when diagnosing why users keep getting confused, when migrating users from one product convention to another, or when the system genuinely differs…. Mental Model Mismatch And Onboarding is an agent skill from hashgraph-online/awesome-codex-plugins. Use this skill when designing onboarding, when diagnosing why users keep getting confused, when migrating users from one product convention to another, or when the system genuinely differs from familiar comparable products.

When should I use Mental Model Mismatch And Onboarding?

Mental Model Mismatch And Onboarding fits situations like: designing onboarding; diagnosing why users keep getting confused; migrating users from one product convention to another; the system genuinely differs from familiar comparable products.

How do I install Mental Model Mismatch And Onboarding in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill mental-model-mismatch-and-onboarding -a claude-code`. Or copy the skill folder (plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/mental-model-mismatch-and-onboarding in hashgraph-online/awesome-codex-plugins) into .claude/skills/mental-model-mismatch-and-onboarding in your project. Claude Code loads it when a task matches its description.

How do I install Mental Model Mismatch And Onboarding in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill mental-model-mismatch-and-onboarding -a codex`. Or copy the skill folder (plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/mental-model-mismatch-and-onboarding in hashgraph-online/awesome-codex-plugins) into .agents/skills/mental-model-mismatch-and-onboarding in your project. Codex loads it when a task matches its description.

Can I use Mental Model Mismatch And Onboarding in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill mental-model-mismatch-and-onboarding -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mental-model-mismatch-and-onboarding, .gemini/skills/mental-model-mismatch-and-onboarding, .github/skills/mental-model-mismatch-and-onboarding and .opencode/skills/mental-model-mismatch-and-onboarding in your project.

What does Mental Model Mismatch And Onboarding need to run?

SKILL.md names no scripts, command-line tools or credentials: Mental Model Mismatch And Onboarding is instructions for the agent only.

Does Mental Model Mismatch And Onboarding access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Mental Model Mismatch And Onboarding safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Mental Model Mismatch And Onboarding use?

Mental Model Mismatch And Onboarding is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mental Model Mismatch And Onboarding use?

About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 507 tokens, read only when the agent opens those files.

What are the alternatives to Mental Model Mismatch And Onboarding?

Skills that share tags, products or a category with Mental Model Mismatch And Onboarding: Siftrank (noperator/siftrank, 225 stars), Support (haacked/dotfiles, 134 stars), Customer Support Agent (mastra-ai/mastra, 29k stars) and Ticket Triage (Bevel-Software/Hexis, 100 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mental Model Mismatch And Onboarding?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,267 GitHub stars. The repository holds 716 skills in this directory. The repository was last updated on October 10, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.