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Skillgrade Setup

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mgechev
skillgrade-setup

Sets up and runs skillgrade evaluation pipelines for AI agents and Agent Skills. Use when initializing eval configurations, running trials, reviewing results, exporting reports, or integrating with CI. Don't use for writing grader scripts, general test authoring, or non-agentic documentation.

Overview

Publishermgechev
Repositoryskillgrade
Skill nameskillgrade-setup
Stars
722
Forks
49
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mgechev on GitHub. Read the source before you install it.

Installation

Install the Skillgrade Setup 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/mgechev/skillgrade/tree/main/skills/skillgrade-setup
  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/mgechev/skillgrade.git /tmp/skillgrade
mkdir -p .claude/skills
cp -r /tmp/skillgrade/skills/skillgrade-setup .claude/skills/skillgrade-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skillgrade Setup 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 Skillgrade Setup 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 Skillgrade Setup 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.

Skillgrade Evaluation Setup

Procedures

Step 1: Install Skillgrade

  1. Verify Node.js 20+ and Docker are available.
  2. Run npm i -g skillgrade to install the CLI globally.

Step 2: Initialize an Eval Configuration

  1. Navigate to any project directory.
  2. If the directory contains a SKILL.md and an API key is set, skillgrade init generates AI-powered tasks and graders tailored to the skill.
  3. Without a SKILL.md or API key, skillgrade init creates a ready-to-edit generic eval template.
  4. Pass --force to overwrite an existing eval.yaml.

Step 3: Configure eval.yaml

  1. Read references/eval-yaml-spec.md for the full configuration schema.
  2. Define tasks under the tasks: key. Each task requires:
    • name: unique task identifier
    • instruction: what the agent should accomplish (OR turns: for multi-turn evals)
    • graders: one or more scoring mechanisms (see the skillgrade-graders skill)
  3. Optionally add workspace: (files the agent edits) and context: (reference material not directly graded).
  4. Configure defaults: for agent, provider, trials, timeout, and threshold.
  5. For multi-turn evals, replace instruction with turns: — each turn has its own instruction and optional graders. The workspace persists across turns.
  6. For batch evals, add dataset: path/to/file.jsonl (or .csv) to a task. Each row expands into a separate task with {{key}} template variables interpolated in the instruction.

Step 4: Run Evaluations

  1. Select an appropriate preset based on the evaluation goal:
    • --smoke (5 trials): Quick capability check.
    • --reliable (15 trials): Reliable pass rate estimate.
    • --regression (30 trials): High-confidence regression detection.
  2. Run the evaluation: skillgrade --smoke.
  3. Run a specific eval by name: skillgrade --eval=fix-linting.
  4. Run multiple evals: skillgrade --eval=fix-linting,write-tests.
  5. Run only deterministic graders (skip LLM calls): skillgrade --grader=deterministic.
  6. Run only LLM rubric graders: skillgrade --grader=llm_rubric.
  7. Run only human review graders: skillgrade --grader=human.
  8. The agent is auto-detected from the API key. Override with --agent=gemini|claude|codex|acp|opencode|command.
  9. For ACP, pass --acp-command="gemini --acp" or set defaults.acp.command.
  10. For OpenCode, pass --opencode-agent=build|plan|explore or --opencode-model=provider/model.
  11. For a custom agent, pass --agent=command --command="node mycli.js" or set defaults.command. The instruction is piped to the command's stdin.
  12. Override the provider with --provider=docker|local (or any custom-registered provider).

Step 5: Review Results

  1. Run skillgrade preview for a CLI report.
  2. Run skillgrade preview browser to open the web UI at http://localhost:3847.
  3. Export results: --format=json|junit|csv|markdown. Combine with commas: --format=json,junit.
  4. Reports are saved to $TMPDIR/skillgrade/<eval-name>/results/. Override with --output=DIR.

Step 6: Integrate with CI

  1. Add a GitHub Actions step that installs skillgrade, navigates to the eval directory, and runs with --regression --ci --provider=local.
  2. Use --provider=local in CI — the runner is already an ephemeral sandbox, so Docker adds overhead without benefit.
  3. The --ci flag causes a non-zero exit code if the pass rate falls below --threshold (default: 0.8).
  4. Use --format=junit to generate JUnit XML for CI dashboard integration.
  5. Read references/ci-example.md for a complete workflow template.

Step 7: Extend with Custom Registries

  1. Register custom graders: registerGrader('my_grader', () => new MyGrader()).
  2. Register custom agents: registerAgent('my-agent', (config) => new MyAgent(config)).
  3. Register custom providers: registerProvider('ssh', (config) => new SSHProvider(config)).
  4. Register custom reporters: registerReporter('dashboard', () => new DashboardReporter()).

Error Handling

  • If skillgrade init produces a generic template, it means no SKILL.md was found — this is expected for non-skill evals.
  • If evaluation hangs, check Docker is running and the container has network access for API calls.
  • If all trials fail with "No API key," ensure the environment variable is exported, not just set inline for a different command.
  • If a task has both instruction and turns, skillgrade will error — use one or the other.
  • If a dataset file is not found, skillgrade errors at config load time with the resolved path.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Skillgrade Setup AI skill do?

Sets up and runs skillgrade evaluation pipelines for AI agents and Agent Skills. Use when initializing eval configurations, running trials, reviewing results, exporting reports, or integrating with CI. Don't use for writing grader scripts, general test authoring, or non-agentic documentation.

Why use Skillgrade Setup on TypingMind?

Because you install it once and use it with any model. Skillgrade Setup 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 Skillgrade Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mgechev/skillgrade/tree/main/skills/skillgrade-setup. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Skillgrade Setup?

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 Skillgrade Setup?

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

Is the Skillgrade Setup AI skill free?

Yes. It is published on GitHub by mgechev 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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