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Experimental Features

Organization
block
experimental-features

Use when adding, reviewing, configuring, graduating, or removing Berd experiments.

Overview

Publisherblock
Repositoryberd
Skill nameexperimental-features
Stars
978
Forks
128
Bundled files
Instructions only
LicenseApache-2.0
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 block on GitHub. Read the source before you install it.

Installation

Install the Experimental Features 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/block/berd/tree/main/.agents/skills/experimental-features
  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/block/berd.git /tmp/berd
mkdir -p .claude/skills
cp -r /tmp/berd/.agents/skills/experimental-features .claude/skills/experimental-features
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Experimental Features 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 Experimental Features 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 Experimental Features 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.

Experimental Features

Use experiments for opt-in, user-local in-progress Berd UI or workflow behavior. Do not use experiments for secrets, credentials, backend authority, packaged policy, or app state that should survive graduation as a normal preference.

When To Use Experiments

  • An individual user opts into unstable UI or workflow behavior.
  • Stable behavior can remain the default path.
  • Config is small, non-sensitive, typed, and user-editable.
  • The feature can be graduated or removed later.

When To Use distro.json

Use distro.json for packaged build policy and startup defaults, especially when the Tauri shell or sidecar needs bundled resources/config.

Good distro fits include providerAllowlist, kgoose, featureToggles.costTracking, bundled config.yaml, bin/, skills/, and agents/.

Do not use distro.json for normal app state, user preferences, dynamic runtime switches, ACP-backed data, or per-user experiments.

Registry Shape

Add experiments only in src/features/experiments/experimentDefinitions.ts.

Each definition needs:

  • id: stable kebab-case string
  • titleKey and descriptionKey: settings i18n keys
  • config: optional typed controls

Experiments without a manual per-experiment override follow the global autoEnable preference. That preference defaults on in dev builds and off in production builds. Users can force an experiment on/off or reset it back to auto from settings.

Config entries under an experiment are settings for that experiment, not nested experiments or independent feature flags. Keep them stored with the parent experiment and gate their runtime effect on the parent experiment being enabled.

Supported config controls:

  • boolean: switch with a boolean default
  • select: fixed string options with a default
  • number: default plus optional min/max/step
  • text: default plus optional placeholder; never for secrets

Use getExperiment(id) or useExperiment(id) for callers. When an experiment is disabled, keep config stored but gate behavior as disabled.

Internal-Only Experiments

Internal-only experiments need build gates at every layer they touch:

  • Add a BuildFeature resolved from a positive-opt-in VITE_* variable in src/shared/profile/buildProfile.ts, and declare the variable in src/env.d.ts.
  • Map the experiment id to that feature in BUILD_FEATURE_GATED_EXPERIMENTS.
  • If the experiment has backing Tauri commands, gate them behind a matching block-* Cargo feature and map the VITE_* variable in scripts/block-feature-gates.sh; drift-guard tests in scripts/release/tests/release-scripts.test.mjs enforce this.
  • Set the gate to 0 in the public env in .github/workflows/release.yml.

Registry-level hiding stops stale per-user localStorage overrides from re-enabling internal surfaces in consumer builds (issue 168).

Storage Contract

Experiment preferences live in localStorage under goose:experimental-features:

json
{
  "version": 2,
  "autoEnable": false,
  "experiments": {
    "experiment-id": {
      "enabled": false,
      "config": {}
    }
  }
}
  • Treat version as real schema state. On newer stored versions, abort writes instead of overwriting; on older versions, migrate explicitly or discard.
  • Store autoEnable as the global default provider. Store enabled only for explicit per-experiment overrides; clearing enabled returns that experiment to auto behavior and must preserve config.
  • Keep config under the parent experiment. Do not migrate config keys into separate experiment ids or apply auto-enable behavior to individual settings.
  • Preserve unknown experiment ids when writing so branch switches do not erase local choices.
  • Write only the touched experiment/key and re-read latest storage immediately before saving to reduce cross-window clobbering.
  • Setters return boolean; callers must surface failed writes to users.
  • Use useSyncExternalStore for React subscriptions. Memoize only the current raw storage value per registry/id; do not retain historical snapshot keys.

Config UX

  • Boolean controls use switches.
  • Select controls use fixed options.
  • Number controls keep a string draft while editing, commit on blur, treat empty input as no write, commit on Enter, and clamp to min/max on commit.
  • Text controls are never for secrets.
  • Config controls may stay editable in storage while disabled, but UI should make disabled/inert behavior clear when the experiment is off.

Tauri Guardrails

Do not add Rust commands, capabilities, or permissions unless the experiment needs backend authority. If backend access is required, add the smallest typed command possible, validate all IPC input, return Result, and use async for heavy work so the UI does not freeze.

When adding commands, update capabilities with least privilege. If backend state is needed, use Tauri managed state deliberately and protect shared mutable state correctly.

Testing

Cover:

  • dev default-on and production default-off behavior
  • global auto-enable overrides and per-experiment explicit override precedence
  • resetting an explicit override back to auto while preserving config
  • enabled and disabled behavior for any gated caller
  • invalid localStorage fallback
  • unsupported storage version fallback or migration
  • typed config validation
  • number-control draft and clamp behavior
  • same-window preference updates
  • cross-window storage events
  • read and write storage failures
  • preserving unknown experiment ids when writing
  • injected test registry UI behavior without shipping fake experiments

Run focused Vitest tests and just check for frontend changes.

Graduation Cleanup

When graduating or removing an experiment, remove the registry entry, i18n keys, settings UI tests, storage assumptions, and all gated code paths. Keep migrations small and explicit if the final feature needs a real user preference.

Frequently asked questions

What does the Experimental Features AI skill do?

Use when adding, reviewing, configuring, graduating, or removing Berd experiments.

Why use Experimental Features on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/block/berd/tree/main/.agents/skills/experimental-features. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Experimental Features?

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 Experimental Features?

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

Is the Experimental Features AI skill free?

Yes. It is published on GitHub by block under the Apache-2.0 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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