Bkit Evals logo

Bkit Evals

Organization
ww-w-ai
bkit-evals

Run skill evals via evals/runner.js — wrapper validates skill names, captures stdout/stderr, persists JSON results. Triggers: bkit evals, evals run, skill quality, eval runner

Overview

Publisherww-w-ai
Repositorybkit-claude-code
Skill namebkit-evals
Stars
601
Forks
154
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 ww-w-ai on GitHub. Read the source before you install it.

Installation

Install the Bkit Evals 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.
  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/ww-w-ai/bkit-claude-code.git /tmp/bkit-claude-code
mkdir -p .claude/skills
cp -r /tmp/bkit-claude-code/skills/bkit-evals .claude/skills/bkit-evals
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bkit Evals 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 Bkit Evals 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 Bkit Evals 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.

bkit Evals — Skill Quality Evaluation Runner

v2.1.11 Sprint β FR-β2. Wraps evals/runner.js with input validation, result persistence, and structured reporting. Replaces the bare node evals/runner.js <skill> invocation that previously required users to remember argv structure and ignored timeout / sandbox concerns.

Arguments

ArgumentDescriptionExample
run <skill>Execute the eval suite for one skill/bkit-evals run gap-detector
listList all skills that have an eval.yaml definition/bkit-evals list

If no argument is provided, render the same output as list.

Behavior

run <skill>

  1. Validate skill against /^[a-z][a-z0-9-]{0,63}$/. Reject anything else (no shell metacharacters, no slashes, no spaces) — see Security below.
  2. Spawn node evals/runner.js --skill <skill> via child_process.spawnSync (argv form, no shell). Default timeout 30 s, max 120 s. The --skill flag form is mandated by the runner CLI and locked by L3 contract test.
  3. Capture stdout / stderr. Parse the trailing JSON block via balanced-brace fallback (string-aware).
  4. Apply fail-closed defense: if parsed === null and stdout includes Usage:, return reason: 'argv_format_mismatch'; if parsed === null otherwise, return reason: 'parsed_null'. Exit code 0 alone NEVER implies success — the parsed JSON must be present.
  5. Persist the structured result to .bkit/runtime/evals-{skill}-{ISO timestamp}.json with stdout/stderr tails (2000 chars each), parsed payload, and reason field.
  6. Render a one-line summary in the chat:
    • exit code
    • parsed pass/fail counts (if available)
    • path of the persisted result file

list

  1. Read evals/config.json to enumerate skill classifications.
  2. For each classification (workflow, capability, hybrid), list skills that have evals/{classification}/{skill}/eval.yaml.
  3. Render a category-grouped table with skill name + a one-line note from the eval YAML (description field if present).

Security

  • Skill name regex prevents argument injection. Anything outside [a-z][a-z0-9-]{0,63} is rejected with reason: invalid_skill_name.
  • argv-array spawn (no shell). No template-string concatenation into command lines.
  • Result file path is composed from a hardcoded base + sanitized skill name + timestamp; no traversal possible.
  • Subprocess timeout enforced (default 30 s, hard cap 120 s) so a buggy eval cannot block the session indefinitely.

Module Dependencies

ModuleFunctionUsage
lib/evals/runner-wrapper.jsinvokeEvals(skill, opts)Validate + spawn + persist
lib/evals/runner-wrapper.jsisValidSkillName(name)Regex pre-check shared with list
evals/runner.js(subprocess)Existing eval execution engine

Result Schema

.bkit/runtime/evals-{skill}-{timestamp}.json:

json
{
  "skill": "gap-detector",
  "invokedAt": "<ISO 8601>",
  "exitCode": 0,
  "timedOut": false,
  "stdoutTail": "...",
  "stderrTail": "...",
  "parsed": { /* whatever runner.js prints as JSON, or null */ }
}

Examples

bash
# Single eval
/bkit-evals run gap-detector

# Discovery
/bkit-evals list

Related

  • /control trust — eval results contribute to trust score
  • /code-review — uses eval data when assessing skills
  • /bkit explore (FR-β1) — explore evals as a category

ARGUMENTS:

Frequently asked questions

What does the Bkit Evals AI skill do?

Run skill evals via evals/runner.js — wrapper validates skill names, captures stdout/stderr, persists JSON results. Triggers: bkit evals, evals run, skill quality, eval runner

Why use Bkit Evals on TypingMind?

Because you install it once and use it with any model. Bkit Evals 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 Bkit Evals in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ww-w-ai/bkit-claude-code/tree/main/skills/bkit-evals. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bkit Evals?

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 Bkit Evals?

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

Is the Bkit Evals AI skill free?

Yes. It is published on GitHub by ww-w-ai 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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