Skillgrade

The easiest way to evaluate your Agent Skills. Tests that AI agents correctly discover and use your skills.

See examples/ — superlint (simple) and angular-modern (TypeScript grader).

Quick Start

Prerequisites: Node.js 20+, Docker

npm i -g skillgrade

1. Initialize — go to your skill directory (must have SKILL.md) and scaffold:

cd my-skill/
GEMINI_API_KEY=your-key skillgrade init    # or ANTHROPIC_API_KEY / OPENAI_API_KEY
# Use --force to overwrite an existing eval.yaml

Generates eval.yaml with AI-powered tasks and graders. Without an API key, creates a well-commented template.

2. Edit — customize eval.yaml for your skill (see eval.yaml Reference).

3. Run:

GEMINI_API_KEY=your-key skillgrade --smoke

The agent is auto-detected from your API key: GEMINI_API_KEY → Gemini, ANTHROPIC_API_KEY → Claude, OPENAI_API_KEY → Codex. Override with --agent=claude.

4. Review:

skillgrade preview          # CLI report
skillgrade preview browser  # web UI → http://localhost:3847

Reports are saved to $TMPDIR/skillgrade/<skill-name>/results/. Override with --output=DIR.

Presets

Flag	Trials	Use Case
--smoke	5	Quick capability check
--reliable	15	Reliable pass rate estimate
--regression	30	High-confidence regression detection

Options

Flag	Description
--eval=NAME[,NAME]	Run specific evals by name (comma-separated)
--grader=TYPE	Run only graders of a type (deterministic or llm_rubric)
--trials=N	Override trial count
--parallel=N	Run trials concurrently
--agent=gemini|claude|codex|acp|opencode|command	Override agent (default: auto-detect from API key)
--model=NAME	Model the agent answers with (gemini, claude, codex, opencode). Default: whatever the agent CLI is configured to use
--provider=docker|local	Override provider
--acp-command=CMD	ACP agent command (e.g., gemini --acp)
--command=CMD	Command to run for the command agent (e.g., node mycli.js)
--opencode-agent=NAME	OpenCode agent (build|plan|explore)
--opencode-model=MODEL	OpenCode model (provider/model format)
--output=DIR	Output directory (default: $TMPDIR/skillgrade)
--validate	Verify graders using reference solutions
--ci	CI mode: exit non-zero if below threshold
--threshold=0.8	Pass rate threshold for CI mode
--preview	Show CLI results after running

eval.yaml Reference

version: "1"

# Optional: explicit path to skill directory (defaults to auto-detecting SKILL.md)
# skill: path/to/my-skill

defaults:
  agent: gemini          # gemini | claude | codex | acp | opencode | command
  model: claude-opus-5   # model the agent answers with (gemini, claude, codex, opencode)
  provider: docker       # docker | local
  trials: 5
  timeout: 300           # seconds
  threshold: 0.8         # for --ci mode
  grader_model: gemini-3-flash-preview  # default LLM grader model
  grader_provider: gemini               # default LLM grader provider: gemini | anthropic | openai
  command: node mycli.js # command to run when agent is 'command' (see Custom Command Agent)
  acp:                   # ACP agent configuration (optional)
    command: gemini --acp  # command to start ACP-compatible agent
    env:                  # optional environment variables
      DEBUG: "1"
  docker:
    base: node:20-slim
    setup: |             # extra commands run during image build
      apt-get update && apt-get install -y jq
  environment:           # container resource limits
    cpus: 2
    memory_mb: 2048

tasks:
  - name: fix-linting-errors
    instruction: |
      Use the superlint tool to fix coding standard violations in app.js.

    workspace:                           # files copied into the container
      - src: fixtures/broken-app.js
        dest: app.js
      - src: bin/superlint
        dest: /usr/local/bin/superlint
        chmod: "+x"

    graders:
      - type: deterministic
        setup: npm install typescript    # grader-specific deps (optional)
        run: npx ts-node graders/check.ts
        weight: 0.7
      - type: llm_rubric
        rubric: |
          Did the agent follow the check → fix → verify workflow?
        provider: gemini                 # optional: gemini (default) | anthropic | openai
        model: gemini-3.5-flash          # optional model override
        weight: 0.3

    # Per-task overrides (optional)
    agent: claude
    model: claude-sonnet-5       # override the model for this task only
    grader_provider: anthropic   # override default LLM grader provider
    trials: 10
    timeout: 600

String values (instruction, rubric, run) support file references — if the value is a valid file path, its contents are read automatically:

instruction: instructions/fix-linting.md
rubric: rubrics/workflow-quality.md

Reference output and metadata

A task can carry the row's answer key and a set of labels:

- name: easy--tooltip-token
  instruction: |
    Report the background colour token of the Tooltip's default variant.
    Write {"token": "..."} to answer.json.
  expected:                 # the answer key — graders only, never the agent
    token: Brand/100
    variants: [default, hover]
  metadata:                 # labels for --filter, recorded with the results
    tier: easy
    form: open
    tags: [smoke]
  graders:
    - type: deterministic
      run: node graders/check-token.mjs

Both are optional: a task without expected is scored purely on what its graders measure, so golden-truth and metric-style tasks live in the same suite.

skillgrade delivers expected, it never interprets it — comparison is the grader's job. A deterministic grader receives the task context as one JSON document in SKILLGRADE_INPUT, so expected keeps its structure:

// graders/check-token.mjs — one script for every token task
const { task, trial, expected, metadata } = JSON.parse(process.env.SKILLGRADE_INPUT);
const answer = JSON.parse(fs.readFileSync('answer.json', 'utf8'));   // cwd is the workspace

console.log(JSON.stringify({
  score: answer.token === expected.token ? 1 : 0,
  details: `${task}: want ${expected.token}, got ${answer.token ?? '(none)'}`,
}));

# or from a shell grader
want=$(jq -r .expected.token <<< "$SKILLGRADE_INPUT")

An llm_rubric grader gets expected as an ## Expected Output section in its prompt. Both channels are built after the agent process has exited, and neither writes to the workspace — unlike run: and rubric:, which are staged into the agent's working directory before it starts, so keep answer keys out of those.

Selecting which tasks to run

metadata is what --filter selects on. Values are OR within a key and AND across keys; --filter and --not-filter are repeatable:

skillgrade --filter=tier=easy,medium              # easy OR medium
skillgrade --filter=tier=hard --filter=form=refuse    # hard AND refuse
skillgrade --filter=tags=smoke --not-filter=tags=flaky
skillgrade --filter-pattern='^easy--'             # regex over task names
skillgrade --filter=tier=easy --list              # print the selection, run nothing

A filter on a key that no task declares is an error rather than a silent match-everything — --filter=teir=easy should not quietly run the whole suite. Filters work the same whether the tasks are inline or imported.

Splitting eval.yaml across files

Any section can live in another file. $import takes a file, a directory (every .yaml/.yml inside it, sorted), a glob, or a list of those:

version: "1"

defaults:
  $import: shared/defaults.yaml   # merged in place — keys below win
  trials: 3

tasks:
  - $import: evals/easy/*.yaml    # one task per file, or a file holding a list
  - $import: evals/hard           # a whole directory
    trials: 10                    # applied to every task it imports
  - name: still-inline            # inline tasks keep working
    instruction: ...
    graders: [...]

Each imported file is a normal YAML document — a single task, a list of tasks, or an object for a section like defaults. Imported files may import further files; paths are relative to the file that contains the $import, and cycles are an error.

A task keeps working when you move it into its own file: its relative paths resolve against its own directory first, then the eval root. So evals/easy/one.yaml can say instruction: instruction.md for the file next to it while still pointing run: node graders/check.mjs at the shared graders directory at the root.

Graders

Deterministic

Runs a command and parses JSON from stdout:

- type: deterministic
  run: bash graders/check.sh
  weight: 0.7

Output format:

{
  "score": 0.67,
  "details": "2/3 checks passed",
  "checks": [
    {"name": "file-created", "passed": true, "message": "Output file exists"},
    {"name": "content-correct", "passed": false, "message": "Missing expected output"}
  ]
}

score (0.0–1.0) and details are required. checks is optional.

Bash example:

#!/bin/bash
passed=0; total=2
c1_pass=false c1_msg="File missing"
c2_pass=false c2_msg="Content wrong"

if test -f output.txt; then
  passed=$((passed + 1)); c1_pass=true; c1_msg="File exists"
fi
if grep -q "expected" output.txt 2>/dev/null; then
  passed=$((passed + 1)); c2_pass=true; c2_msg="Content correct"
fi

score=$(awk "BEGIN {printf \"%.2f\", $passed/$total}")
echo "{\"score\":$score,\"details\":\"$passed/$total passed\",\"checks\":[{\"name\":\"file\",\"passed\":$c1_pass,\"message\":\"$c1_msg\"},{\"name\":\"content\",\"passed\":$c2_pass,\"message\":\"$c2_msg\"}]}"

Use awk for arithmetic — bc is not available in node:20-slim.

LLM Rubric

Evaluates the agent's session transcript against qualitative criteria:

- type: llm_rubric
  rubric: |
    Workflow Compliance (0-0.5):
    - Did the agent follow the mandatory 3-step workflow?

    Efficiency (0-0.5):
    - Completed in ≤5 commands?
  weight: 0.3
  provider: gemini           # gemini (default) | anthropic | openai
  model: gemini-2.0-flash    # optional, auto-detected from API key

The provider field selects which LLM API to call:

Provider	API Key Env Var	Base URL Env Var (optional)	Default Model
gemini	GEMINI_API_KEY	-	Dynamically resolved latest Flash model (via API)
anthropic	ANTHROPIC_API_KEY	ANTHROPIC_BASE_URL	Dynamically resolved latest Haiku model (via API)
openai	OPENAI_API_KEY	OPENAI_BASE_URL	Dynamically resolved latest Mini/Flash model (via API)

ANTHROPIC_BASE_URL and OPENAI_BASE_URL enable custom/self-hosted endpoints (Ollama, vLLM, etc.). They apply to both LLM grading and skillgrade init.

Combining Graders

graders:
  - type: deterministic
    run: bash graders/check.sh
    weight: 0.7      # 70% — did it work?
  - type: llm_rubric
    rubric: rubrics/quality.md
    weight: 0.3      # 30% — was the approach good?

Final reward = Σ (grader_score × weight) / Σ weight

CI Integration

Use --provider=local in CI — the runner is already an ephemeral sandbox, so Docker adds overhead without benefit.

# .github/workflows/skillgrade.yml
- run: |
    npm i -g skillgrade
    cd skills/superlint
    GEMINI_API_KEY=${{ secrets.GEMINI_API_KEY }} skillgrade --regression --ci --provider=local

Exits with code 1 if pass rate falls below --threshold (default: 0.8).

Tip: Use docker (the default) for local development to protect your machine. In CI, local is faster and simpler.

Environment Variables

Variable	Used by
GEMINI_API_KEY	Agent execution, LLM grading (provider: gemini), skillgrade init
ANTHROPIC_API_KEY	Agent execution, LLM grading (provider: anthropic), skillgrade init
OPENAI_API_KEY	Agent execution (Codex), LLM grading (provider: openai), skillgrade init
ANTHROPIC_BASE_URL	LLM grading (provider: anthropic), skillgrade init — custom Anthropic-compatible endpoint
OPENAI_BASE_URL	LLM grading (provider: openai), skillgrade init — custom OpenAI-compatible endpoint (Ollama, vLLM, etc.)
GEMINI_MODEL	Override the default model used for Gemini LLM grading (defaults to dynamic API lookup; throws if resolution fails)
INIT_GEMINI_MODEL	Override the model used for Gemini in skillgrade init (defaults to GEMINI_MODEL or dynamic API lookup; throws if resolution fails)
ANTHROPIC_MODEL	Override the default model used for Anthropic LLM grading (defaults to dynamic API lookup; throws if resolution fails)
INIT_ANTHROPIC_MODEL	Override the model used for Anthropic in skillgrade init (defaults to ANTHROPIC_MODEL or dynamic API lookup; throws if resolution fails)
OPENAI_MODEL	Override the default model used for OpenAI LLM grading (defaults to dynamic API lookup; throws if resolution fails)
INIT_OPENAI_MODEL	Override the model used for OpenAI in skillgrade init (defaults to OPENAI_MODEL or dynamic API lookup; throws if resolution fails)

Variables are also loaded from .env in the skill directory. Shell values override .env. All values are redacted from persisted session logs.

Custom Command Agent

Bring your own agent. The built-in adapters (gemini, claude, codex, ...) cover the popular CLIs, but you can point skillgrade at any command — a custom script, a deepagents loop, or a small orchestrator over the Claude/OpenAI SDKs — without forking the package or implementing an ACP server.

Quick Start

skillgrade --agent=command --command="node mycli.js"

Or in eval.yaml:

defaults:
  agent: command
  command: "node mycli.js"
  provider: local        # run on the host; or use docker + docker.setup to install your CLI

command can also be set per task to override the default.

How the instruction reaches your command

The task instruction is piped to your command's stdin (skillgrade writes it to /tmp/.prompt.md, then runs cat /tmp/.prompt.md | <command> inside the workspace directory). If your CLI takes the prompt as an argument instead, wrap it in a one-line script that reads stdin.

Your command runs in the workspace and is free to read/edit files there — graders score the resulting workspace state (and any live checks), not your command's stdout, so any agent slots in cleanly.

Docker vs local

• provider: local is the simplest fit for a custom agent: your command runs on the host with your tools already installed.
• provider: docker still works — skillgrade does not auto-install anything for the command agent, so install your CLI and dependencies via docker.setup:

defaults:
  agent: command
  command: "mycli run"
  docker:
    base: node:20-slim
    setup: "npm install -g my-cli-package"

OpenCode Agent

OpenCode is an AI coding agent that supports multiple AI models and specialized subagents.

Quick Start

# Use OpenCode with default agent and model
skillgrade --agent=opencode

# Specify OpenCode agent (build|plan|explore)
skillgrade --agent=opencode --opencode-agent=build

# Specify both agent and model (provider/model format)
skillgrade --agent=opencode --opencode-agent=build --opencode-model=anthropic/claude-sonnet-4-20250514

OpenCode Agents

Agent	Description
build	Default primary agent with full tool access
plan	Read-only planning/analysis agent
explore	Fast codebase exploration agent

OpenCode Models

Models are specified in provider/model format:

Model	Format
Claude Sonnet 4	anthropic/claude-sonnet-4-20250514
GPT 5.1 Codex	opencode/gpt-5.1-codex

CLI Options

Flag	Description
--agent=opencode	Use OpenCode agent
--opencode-agent=NAME	OpenCode agent (build|plan|explore)
--opencode-model=MODEL	OpenCode model (provider/model format)

How It Works

1. skillgrade invokes OpenCode CLI with opencode run
2. Passes instruction via temp file to avoid shell escaping issues
3. Supports both agent and model specification
4. Works with --provider=docker or --provider=local

ACP Agent

Agent Client Protocol (ACP) is an open protocol that standardizes communication between AI coding agents and clients. Using an ACP-compatible agent allows you to evaluate skills without managing API keys directly.

Quick Start

# Use Gemini CLI in ACP mode (requires gemini CLI installed)
skillgrade --agent=acp --acp-command="gemini --acp"

# Or configure in eval.yaml

defaults:
  agent: acp
  acp:
    command: gemini --acp

ACP-Compatible Agents

Any agent that supports the ACP protocol can be used:

Agent	Command
Gemini CLI	gemini --acp
Other ACP agents	Check agent documentation

How It Works

1. skillgrade starts the ACP agent as a subprocess
2. Communication happens via JSON-RPC 2.0 over stdio
3. No API key required — authentication is handled by the ACP agent
4. Works best with --provider=local since the ACP agent needs to be available in your environment

CLI Options

Flag	Description
--agent=acp	Use ACP-compatible agent
--acp-command=CMD	Command to start the ACP agent

The --acp-command can also be set in eval.yaml under defaults.acp.command.

Best Practices

• Grade outcomes, not steps. Check that the file was fixed, not that the agent ran a specific command.
• Instructions must name output files. If the grader checks for output.html, the instruction must tell the agent to save as output.html.
• Validate graders first. Use --validate with a reference solution before running real evals.
• Start small. 3–5 well-designed tasks beat 50 noisy ones.

For a comprehensive guide on writing high-quality skills, check out skills-best-practices. You can also install the skill creator skill to help author skills:

npx skills add mgechev/skills-best-practices

License

MIT

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Inspired by SkillsBench and Demystifying Evals for AI Agents.