Berd is an open-source desktop application for working with AI agents through a graphical interface. It is intended for people who want to use different AI models and an integrated Goose backend from a desktop app. The catalogue entries add agent skills, agents, and instructions for working within Berd.
Borrowing it
Nothing to install: this file belongs to block/berd. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/block/berd/main/.agents/skills/experimental-features/SKILL.mdgit clone --depth 1 https://github.com/block/berdWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/block/berd/experimental-features)<a href="https://agentmods.dev/skills/block/berd/experimental-features"><img src="https://agentmods.dev/badge/skills/block/berd/experimental-features/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/block/berd/experimental-features"><img src="https://agentmods.dev/badge/skills/block/berd/experimental-features.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.01291 |
| Opus 5.5 | $0.00008 | $0.00516 |
| Sonnet 5.5 | $0.00004 | $0.00258 |
| Haiku 4.5 | $0.00002 | $0.00129 |
Grade A, and why
experimental-features scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 stringtitleKeyanddescriptionKey: settings i18n keysconfig: 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 defaultselect: fixed string options with a defaultnumber: default plus optional min/max/steptext: 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +17 lines 615fb486b3e0
- 1mo ago First seen · 140 lines · 19 tokens per session scan A 2b7bc4048840
experimental-features is a skill published in the GitHub repository block/berd (1,007 stars, last pushed 2d ago), licensed Apache-2.0. It adds 19 tokens to every session and 1,291 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
component-fixtures
Use when creating or updating component fixtures for screenshot testing or their shared infrastructure, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, validation, and common pitfalls.