evals-init

SkillSecurity

Initialize evals/{system}/ directory structure for evaluation system following EDD principles (Standalone). Choose PromptFoo or DeepEval based on tech stack, generate security baseline.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the evals-init skill

What this skill tells your AI

The instructions your AI receives, as published by tikalk/adlc-team-skills in skills/evals/evals-init/SKILL.md and read by ahel’s review.

What this skill does

Initialize the project-level evaluation directory structure following EDD (Eval-Driven Development) principles to prepare for systematic evaluation development. This is completely standalone with zero spec-kit dependencies.

Output:

  1. Directory Structure - evals/{system}/ with proper organization (promptfoo | deepeval)
  2. Security Baseline - Auto-created graders for PII leakage, prompt injection, hallucination detection, misinformation detection
  3. Configuration Files - Standalone config.yml and goldset templates under .adlc/evals/
  4. Auto-handoff to /evals-specify to begin error analysis

Key EDD Principles Applied:

  • Principle I: Spec-Driven Contracts - Evals validate spec compliance
  • Principle II: Binary Pass/Fail - No Likert scales in grader templates
  • Principle IV: Evaluation Pyramid - Tier 1 (fast) + Tier 2 (goldset) structure
  • Principle IX: Test Data as Code - Version control setup for datasets

When to use

  • Starting systematic evaluation: Set up the initial evaluation harness for your application
  • EDD Adoption: Converting from traditional testing to evaluation-driven development
  • Security-first evaluation: Auto-generate baseline security checks from the start

When NOT to use

  • Evals directory already exists: Use /evals-validate to run tests, or /evals-specify to add criteria
  • Evaluating team directives: This is for project-level application behavior testing, not directives compliance

Process

User Input

$ARGUMENTS

Parse flags from the arguments first, then treat remaining text as focus areas:

  • --system SYSTEM — Choose promptfoo or deepeval. If omitted, choose interactively based on tech stack.
  • Remaining text — System description (focus setup)

Execution Steps

Phase 1: Tech Stack Detection
  • Scan project manifests (package.json, requirements.txt, Cargo.toml, go.mod, etc.)
  • Recommends PromptFoo for mixed/JS stacks; DeepEval for Python-native stacks
Phase 2: Create Directory Structure

Creates:

evals/
├── {system}/                    # promptfoo | deepeval
│   ├── goldset.md              # Published goldset
│   ├── goldset.json            # Auto-generated for system consumption
│   ├── config.yml              # System-specific configuration
│   ├── config.{js,py}          # Generated system config (.js for promptfoo, .py for deepeval)
│   └── graders/                # Binary pass/fail graders
│       ├── check_pii_leakage.py           # Security baseline
│       ├── check_prompt_injection.py     # Security baseline
│       ├── check_hallucination.py        # Security baseline
│       └── check_misinformation.py       # Security baseline
├── results/                    # Git-ignored run outputs
└── .adlc/
    └── drafts/evals/           # Draft eval records (Markdown + YAML)
Phase 3: Configuration Copy
  • Create .adlc/evals/ if missing.
  • Copy skills/evals/evals-templates/evals-config-template.yml to .adlc/evals/evals-config.yml.
Phase 4: Auto-Handoff

Trigger /evals-specify to begin error analysis.

Verification

  • evals/{system}/goldset.md exists (initially empty)
  • .adlc/evals/evals-config.yml exists
  • Graders directory populated with 4 security baseline python scripts
  • Results directory contains .gitignore to prevent versioning traces
  • Handover report generated with recommended framework and next steps

Signals

GitHub stars
133
Forks
1
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
evals-init
Source
github.com/tikalk/adlc-team-skills