evals-implement

What this skill does

Generates the complete executable evaluation implementation following EDD Principle VIII (Close Production Loop) from the published goldset, with automated unit testing to verify evaluator correctness.

Output:

Grader/Metric Implementation - Python evaluators for each goldset criterion with binary pass/fail
- PromptFoo: Python grader functions with JSON output in
```
evals/{system}/graders/
```
- DeepEval: Custom metric classes inheriting from
```
BaseMetric
```
Evaluator Unit Tests - Automated tests (
```
evals/{system}/tests/test_check_*.py
```
) that run the goldset pass/fail examples against the generated graders to ensure the evaluator itself is accurate
Evaluation Configuration - Complete config file (
```
config.js
```
or
```
config.py
```
) with Tier 1 + Tier 2 evaluation structure
Auto-handoff to
```
/evals-validate
```
to run validation

Key EDD Principles Applied:

Principle VIII: Close Production Loop - Failure type gates route to appropriate actions
Principle II: Binary Pass/Fail - Ensure graders return strictly 1.0 (pass) or 0.0 (fail)
Principle IX: Test Data as Code - Unit test generated code against dataset examples

When to use

After
/evals-clarify
: Convert accepted goldset criteria into executable code
Regenerating configs: Re-build evaluator suite after adding new goldset criteria
Adding unit tests: Hardening the evaluator itself against regression or bugs

When NOT to use

Goldset not published: Run
```
/evals-clarify
```
to generate
```
goldset.json
```
first
Running evaluations: Use
```
/evals-validate
```
to run the suite against application outputs

Process

User Input

text

$ARGUMENTS

```
--system SYSTEM
```
— Override active evaluation framework (
```
promptfoo
```
or
```
deepeval
```
)
```
--no-tests
```
— Skip automated unit test generation for graders (not recommended)

Execution Steps

Phase 1: Trace-to-Grader Synthesis (Automated Eval Engineering)

Reads
```
evals/{system}/goldset.json
```
.
Maps rich evidence fields from the goldset criteria into grader logic (Trace-to-Grader Synthesis):
- Uses
```
pass_condition
```
  and
```
fail_condition
```
  as the grader's core rubric.
- Extracts pass/fail examples to act as raw data anchors and few-shot classification anchors inside the grader logic.
- Injects
```
Root Cause Analysis
```
  and
```
axial_coding
```
  notes as contextual prompt guidelines or regex patterns to catch exact failure manifestations.
For PromptFoo: Generates Python grader functions (
```
evals/{system}/graders/check_*.py
```
) containing specialized, dynamic LLM-judge templates or regex checks compiled from these goldset inputs.
For DeepEval: Generates Custom Metric classes inheriting from
```
BaseMetric
```
compiled from these goldset inputs.
All graders conform strictly to the binary pass/fail standard (returning only
```
1.0
```
or
```
0.0
```
, with zero Likert scale leakage).

Phase 2: Unit Test Generation

Generates matching unit tests (
```
evals/{system}/tests/test_check_*.py
```
) for each grader.
Unit tests verify the grader correctly identifies the goldset's training pass and fail examples.

Phase 2b: Closed-Loop Grader Self-Tuning

Executes generated unit tests (
```
pytest evals/{system}/tests/
```
) to verify evaluator accuracy.
Grader Calibration Loop:
1. Inspects test results to detect any misclassifications (false positives/negatives) on the training cases.
2. If any test fails, triggers a feedback edit step that parses the failure reasons and automatically adjusts the grader's internal prompt rubric, regex stubs, or score thresholds.
3. Re-runs pytest to check accuracy.
4. Repeats for up to 3 iterations (the hard circuit-breaker limit).
Holdout Locking: Ensure the holdout validation set (
```
holdout.json
```
) remains completely isolated and is never loaded or exposed to the self-tuning loop (to prevent overfitting).
Failure Escalation: If the grader does not converge to 100% training accuracy within 3 iterations, the loop halts, surfaces the failing test case details, and raises an error rather than passing silently.

Phase 3: Config Generation

Generates the unified framework configuration file (
```
config.js
```
or
```
config.py
```
).
Configures separate Tier 1 (fast checks, <30s, deterministic) and Tier 2 (semantic checks, <5min, LLM-judge) pipelines.

Phase 4: Auto-Handoff

Trigger

/evals-validate

to run validation.

Verification

```
evals/{system}/graders/
```
contains Python grader scripts for each criterion compiled dynamically from goldset pass/fail examples and root-cause analyses
```
evals/{system}/tests/
```
contains matching unit test files
Framework config (
```
config.js
```
or
```
config.py
```
) successfully generated
Grader calibration self-tuning loop ran and converged to 100% training accuracy within the 3-iteration cap (or raised explicit non-convergence errors)
Holdout dataset protection confirmed (validation
```
holdout.json
```
remained completely isolated and untouched during tuning)
All grader unit tests pass locally (
```
pytest evals/{system}/tests/
```
)
Handover summary lists generated graders, self-tuning iterations, and test results

evals-implement

NPX Install

Tags

SKILL.md Content

evals-implement

What this skill does

When to use

When NOT to use

Process

User Input

Execution Steps

Phase 1: Trace-to-Grader Synthesis (Automated Eval Engineering)

Phase 2: Unit Test Generation

Phase 2b: Closed-Loop Grader Self-Tuning

Phase 3: Config Generation

Phase 4: Auto-Handoff

Verification