vikas8520-coder/nerve/cli-eval

Name: cli-eval
Author: vikas8520-coder

Claude Code and agent skill by vikas8520-coder/nerve

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Create and run evaluation suites, watch live benchmark progress, view scorecards, compare model performance, and integrate eval runs with CI workflows from the CLI.

Install

npx vskill@latest install vikas8520-coder/nerve/cli-eval

SKILL.md

Overview

Create and run evaluation suites, watch live benchmark progress, view scorecards, compare model performance, and integrate eval runs with CI workflows from the CLI.

Quick install

bash

npm install -g nerve   # or: npx nerve
nerve --version

Subcommands

`eval`

Example:

bash

nerve eval

`eval suites`

Example:

bash

nerve eval suites

`eval list`

Example:

bash

nerve eval list

`eval get <suiteId>`

Example:

bash

nerve eval get <suiteId>

`eval create`

Flags:

--file <path>

Example:

bash

nerve eval create

`eval run <suiteId>`

Flags:

-m, --model <id>
--combo <name>
--concurrency <n>
--tag <tag>
--watch

Example:

bash

nerve eval run <suiteId>

`eval list`

Flags:

--suite <id>
--status <s>
--since <ts>
--limit <n>

Example:

bash

nerve eval list

`eval get <runId>`

Example:

bash

nerve eval get <runId>

`eval results <runId>`

Flags:

--failed

Example:

bash

nerve eval results <runId>

`eval cancel <runId>`

Flags:

--yes

Example:

bash

nerve eval cancel <runId>

`eval scorecard <runId>`

Example:

bash

nerve eval scorecard <runId>

`simulate [prompt]`

Flags:

--file <path>
-m, --model <id>
--combo <name>
--reasoning-effort <level>
--thinking-budget <n>
--explain

Example:

bash

nerve simulate [prompt]

Nerve — CLI Evals

Requires the nerve CLI. See CLI entry-point skill for install + global flags.

What are evals?

Evals are automated test suites that score LLM outputs against expected answers or rubrics. Nerve stores suites and run results in its local database.

Eval suites

bash

nerve eval suites list                       # List all eval suites
nerve eval suites list --json                # JSON output
 
nerve eval suites get <suiteId>              # Full suite definition

Create a suite

bash

nerve eval suites create \
  --name "code-quality" \
  --rubric "exact-match" \
  --samples-file ./samples.jsonl                 # JSONL: {input, expected_output}

Rubric options: exact-match, contains, llm-judge, regex.

--samples-file format (one JSON object per line):

jsonl

{"input": "What is 2+2?", "expected_output": "4"}
{"input": "Translate 'hello' to Spanish", "expected_output": "hola"}

Run an eval

bash

nerve eval suites run <suiteId> \
  --model claude-sonnet-4-6                      # Run suite against a specific model
 
nerve eval suites run <suiteId> \
  --model gpt-4o \
  --watch                                        # Live TUI progress (EvalWatch)

The run is asynchronous. Use --watch for a live terminal dashboard or poll manually:

bash

RUN_ID=$(nerve eval suites run <suiteId> --model claude-sonnet-4-6 --output json | jq -r '.id')
nerve eval get $RUN_ID

Manage runs

bash

nerve eval list                              # List all eval runs
nerve eval list --json
 
nerve eval get <runId>                       # Run details (status, model, score)
nerve eval results <runId>                   # Per-sample results
nerve eval scorecard <runId>                 # Full scorecard with pass/fail per sample
nerve eval cancel <runId>                    # Cancel a running eval

Scorecard output

bash

nerve eval scorecard <runId> --output json

Response fields per sample:

json

{
  "id": "sample-1",
  "score": 0.95,
  "passed": true,
  "input": "What is 2+2?",
  "output": "4",
  "expected": "4"
}

Comparing models

Run the same suite against multiple models and compare:

bash

for MODEL in claude-sonnet-4-6 gpt-4o gemini-2.0-flash; do
  nerve eval suites run $SUITE_ID --model $MODEL --output json | jq '{model: .model, score: .score}'
done

CI integration

bash

# Run and fail CI if score drops below threshold
SCORE=$(nerve eval suites run $SUITE_ID --model claude-sonnet-4-6 --output json | jq -r '.score')
python3 -c "import sys; score=float('$SCORE'); sys.exit(0 if score >= 0.90 else 1)"

Errors

suites create fails with invalid rubric → use one of: exact-match, contains, llm-judge, regex
suites run returns model not found → verify model ID with nerve models --search <name>
eval get shows status: failed → check nerve logs --search eval for error details
scorecard returns empty results → the run may still be running; poll nerve eval get <runId> until status is completed

Security

Security scanPassed, 90 of 100Pattern checks on the skill's files.
Model reviewPassed

See the full security report

Quality evaluation

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Source

SKILL.md in the repositoryCheckingskills/cli-eval/SKILL.md

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