benchmark-execute

A command for running a schema benchmark that compares LiamDB and OpenAI models on prepared datasets. A schema benchmark tests how well models produce database structures from prompts.

In plain words
What is it for?
Use it to run the benchmark for either supported model, including tests for generated table and column names, ambiguous prompts, and recall of important tables.
Why use it?
It sets up a fresh benchmark workspace and handles the longer processing and progress reporting needed for model comparisons.

Command for Claude Code

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/liam-hq/liam/benchmark-execute
Clone the repo
git clone --depth 1 https://github.com/liam-hq/liam

Made for: Claude Code.

Per session 14 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,048 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00014 $0.01048
Opus 5 $0.00007 $0.00524
Sonnet 5 $0.00003 $0.00210
Haiku 4.5 $0.00001 $0.00105

Measured 2d ago against content hash 42ef011c3f19, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

benchmark-execute scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

rm -rf benchmark-workspace && pnpm --filter @liam-hq/schema-bench setupWorkspace
.claude/commands/benchmark-execute.md · 104 lines

How it starts

The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Benchmark Command

Execute schema benchmark comparison between LiamDB and OpenAI models.

Arguments

  • model: Target model to benchmark (LiamDB or OpenAI, case-insensitive)

Usage

/benchmark-execute LiamDB
/benchmark-execute openai

Execution

Important: Benchmark operations are time-intensive. The system now supports:

  • Parallel dataset processing for faster execution
  • Automatic input format standardization
  • Improved error handling and progress reporting

Critical: When executing LiamDB benchmarks, use a 30-minute (1800-second) timeout to prevent premature termination. The deep modeling workflow can take 10+ minutes per test case.

First, I'll clean up any existing workspace and set up a fresh benchmark environment with multiple datasets:

rm -rf benchmark-workspace && pnpm --filter @liam-hq/schema-bench setupWorkspace

This will set up four benchmark datasets:

  • default: Standard schema generation benchmark (3 complex cases)
  • entity-extraction: Tests if specified table/column names appear in output (5 cases)
  • ambiguous-recall: Measures recall of core tables from an ambiguous prompt. Uses the same input across 3 cases with different expected schemas (3/5/10 tables) to evaluate robustness.
  • logical-deletion: Evaluates account deletion design without naive is_deleted. Focuses on PII separation, referential integrity for orders, legal retention/holds, closure reasons, and auditability.

The system features:

  • Parallel Processing: Datasets are processed simultaneously for faster execution
  • Smart Concurrency: Each dataset uses MAX_CONCURRENT=5 for stability
  • Input Standardization: Entity-extraction inputs are automatically wrapped in {"input": "..."} format

Next, I'll execute the specified model with dataset selection:

{{#if (eq (lower model) "liamdb")}}

# Run LiamDB on all datasets in the workspace
pnpm --filter @liam-hq/schema-bench executeLiamDB -all

# Run LiamDB on a specific dataset
pnpm --filter @liam-hq/schema-bench executeLiamDB -entity-extraction

# Run LiamDB on the ambiguous-recall dataset only
pnpm --filter @liam-hq/schema-bench executeLiamDB -ambiguous-recall

# Run LiamDB on the logical-deletion dataset only
pnpm --filter @liam-hq/schema-bench executeLiamDB -logical-deletion

# Run LiamDB on multiple datasets
pnpm --filter @liam-hq/schema-bench executeLiamDB -default -entity-extraction -ambiguous-recall -logical-deletion

{{else if (eq (lower model) "openai")}}

# OpenAI currently targets the default dataset
pnpm --filter @liam-hq/schema-bench executeOpenai

{{else}} Error: Invalid model specified. Please use 'LiamDB' or 'OpenAI'. {{/if}}

Read the full file on GitHub · 104 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 104 lines · 14 tokens per session scan C 42ef011c3f19

Subscribe to this mod's changes

benchmark-execute is a command published in the GitHub repository liam-hq/liam (5,100 stars, last pushed 4d ago), licensed Apache-2.0. It adds 14 tokens to every session and 1,048 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.