Domain Expert Configuration logo

Domain Expert Configuration

Organization
dembrandt
domain-expert-configuration

Settings for experts who are not developers — domain language, sane defaults, grouping by professional concept. Use when designing settings panels, solver configuration or constraint editors.

Overview

Publisherdembrandt
Repositorydembrandt-skills
Skill namedomain-expert-configuration
Stars
169
Forks
21
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 dembrandt on GitHub. Read the source before you install it.

Installation

Install the Domain Expert Configuration 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/dembrandt/dembrandt-skills/tree/main/skills/domain-expert-configuration
  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/dembrandt/dembrandt-skills.git /tmp/dembrandt-skills
mkdir -p .claude/skills
cp -r /tmp/dembrandt-skills/skills/domain-expert-configuration .claude/skills/domain-expert-configuration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Domain Expert Configuration 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 Domain Expert Configuration 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 Domain Expert Configuration 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.

Domain Expert Configuration

A domain expert configuration UI exposes the parameters of a complex system — an optimisation algorithm, a planning engine, a simulation — to users who understand the problem domain deeply but have no knowledge of the system's internals.

The challenge: the system's parameters are defined in technical terms (weights, thresholds, flags, tolerances). The user thinks in domain terms (how long to wait, when to retry, how to group results). The UI must translate between these two vocabularies — always favouring the user's language.


The Core Principle: Domain Language Over Technical Language

Every parameter label, tooltip, and error message should describe what the parameter means in the user's world, not what it does inside the system.

Technical labelDomain label
max_concurrent_jobsMaximum tasks running at once
enable_auto_retryRetry failed tasks automatically
batch_allocation_thresholdStart a new batch after (% filled)
request_timeout_msMaximum wait per request (seconds)
enable_fuzzy_matchingAllow approximate matches

If you cannot write a domain label for a parameter, question whether the user should be exposed to it at all. Parameters that cannot be explained in domain terms belong in a developer configuration file, not in the user-facing UI.


Grouping by Domain Concept

Parameters should be grouped by the aspect of the real-world problem they control — not by their technical category (booleans together, numbers together) and not alphabetically.

A processing tool might group as:

Processing limits
  └─ Maximum tasks at once
  └─ Maximum wait per request
  └─ Memory ceiling

Matching rules
  └─ Allow approximate matches
  └─ Case sensitivity
  └─ Required fields

Batching behaviour
  └─ Batch fill threshold
  └─ Maximum batches

Each group should have a short heading that describes what aspect of the task it controls, not what kind of parameter it is.


Sensible Defaults

Every parameter must have a default that works correctly for the majority of cases. The user should be able to start with all defaults and get a reasonable result.

Show the default value: When a field is at its default, indicate this. When a user has changed a value away from the default, make it easy to reset.

Maximum wait per request  [___30___ s]  ← custom value
                          [↺ Reset to default (15 s)]

Batch fill threshold  [_70_ %]  (default)  ← at default

Why this matters: Domain experts often do not know what value to enter for an unfamiliar parameter. If the field is blank with no hint, they will either skip it (leaving the system in an unknown state) or enter an arbitrary value. A visible default communicates "this is what the system assumes unless you tell it otherwise."


Input Types Matched to Domain Semantics

Choose the input type based on what the parameter means, not just its data type.

Parameter natureInput typeExample
Binary rule (on/off)Toggle switch"Retry automatically: [toggle]"
Constrained number with clear unitNumber input with unit label"Max wait: [___] s"
Choice between named optionsSelect or radio group"Output format: [JSON ▾]"
Percentage or ratioSlider with numeric input"Batch fill threshold: [━●━━] 70%"
Free text identifierText input"Job reference: [___]"

Units are mandatory for all numerical inputs. Never show a bare number without its unit. Place the unit label adjacent to the input (suffix preferred: [___] cm, not cm [___]).


Progressive Disclosure

Not all parameters are equally important. Expose them in layers:

Primary settings (always visible): The parameters that control the most commonly adjusted behaviour. A domain expert should be able to accomplish 80% of their tasks by adjusting these alone.

Advanced settings (collapsed by default): Parameters for edge cases, fine-tuning, or less common scenarios. Behind a disclosure control ("Advanced options ▾"). Opened by users who need them, invisible to those who don't.

Developer / system parameters: Not shown in the user-facing UI at all. In a config file or environment variable.

Do not put everything in the advanced section as a catch-all. If a parameter is needed frequently, it belongs in the primary settings.


Saved vs. Session Configuration

Many operational tools distinguish between:

  • Saved configuration: The user's persisted preferences (their standard processing setup, their standard rules). Loaded automatically.
  • Session overrides: One-off adjustments for a specific run that should not change the saved defaults.

Make this distinction explicit in the UI. If the user adjusts a parameter for one run, they should not have to worry about corrupting their saved defaults.

┌─ Configuration ────────────────────────────┐
│  Maximum wait    [30 s]   ← session only    │
│  Retry on fail   [✓]      ← saved           │
│                                             │
│  [Save as default]   [Reset to saved]       │
└─────────────────────────────────────────────┘

Validation and Constraint Feedback

When a value is invalid or conflicts with another setting, tell the user in domain terms.

Technical errorDomain error
value out of range [0, 9999]"Wait time must be between 0 and 999 seconds"
constraint conflict: retry=true, fail_fast=true"Retry automatically and Stop on first failure cannot both be enabled"
threshold must be < 1.0"Batch fill threshold must be less than 100%"

Show validation inline, adjacent to the affected field. Do not wait for the user to submit before reporting conflicts.

For settings that interact with each other, show the relationship: "When automatic retry is off, the maximum-attempts setting has no effect." This prevents the expert from wasting time tuning a parameter that isn't active.


Proposals From a Model

When a model turns free text into a configuration, show its reading as the same form controls, never as prose.

  • Mark every value the model inferred rather than read. The mark clears on the field the user corrects.
  • Editing any control re-runs the proposal, so a correction shows its consequence.
  • Code selects, orders and counts. The model only phrases. A list the model assembled drops, merges or miscounts entries on some runs.
  • Say this split in the help text.

Prose forces a re-prompt to fix one number. A marked field makes it one gesture.


Review Checklist

  • Does every parameter label use domain language, not technical language?
  • Are parameters grouped by the domain concept they control, not by type or alphabetically?
  • Does every parameter have a visible default value?
  • Is there a "reset to default" action for individual parameters?
  • Do all numerical inputs show their unit adjacent to the field?
  • Are input types matched to domain semantics (toggle for binary, select for named options)?
  • Are advanced parameters hidden by default behind a disclosure control?
  • Is the distinction between saved configuration and session overrides explicit?
  • Is validation shown inline in domain language?
  • Are parameter interactions (conflicts, dependencies) explained in the UI?
  • Are model-inferred values shown as marked, editable fields, with selection and counts done in code?

Frequently asked questions

What does the Domain Expert Configuration AI skill do?

Settings for experts who are not developers — domain language, sane defaults, grouping by professional concept. Use when designing settings panels, solver configuration or constraint editors.

Why use Domain Expert Configuration on TypingMind?

Because you install it once and use it with any model. Domain Expert Configuration 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 Domain Expert Configuration in TypingMind?

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

Which AI models can use Domain Expert Configuration?

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 Domain Expert Configuration?

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

Is the Domain Expert Configuration AI skill free?

Yes. It is published on GitHub by dembrandt 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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