skillgrade

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
--trials=N	Override trial count
--parallel=N	Run trials concurrently
--agent=gemini|claude|codex	Override agent (default: auto-detect from API key)
--provider=docker|local	Override provider
--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
  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
  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?
        model: gemini-2.0-flash          # optional model override
        weight: 0.3

    # Per-task overrides (optional)
    agent: claude
    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

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
  model: gemini-2.0-flash    # optional, auto-detected from API key

Uses Gemini or Anthropic based on available API key. Override with the model field.

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, skillgrade init
ANTHROPIC_API_KEY	Agent execution, LLM grading, skillgrade init
OPENAI_API_KEY	Agent execution (Codex), skillgrade init

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

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

Inspired by SkillsBench and Demystifying Evals for AI Agents.