Liam ERD is a tool that turns database schemas into interactive entity-relationship diagrams, visual maps showing tables and their relationships. It helps people understand and document database structures, including complex schemas.
Borrowing it
Nothing to install: this file belongs to liam-hq/liam. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/liam-hq/liam/main/.claude/commands/benchmark-execute.mdgit clone --depth 1 https://github.com/liam-hq/liamWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/liam-hq/liam/benchmark-execute)<a href="https://agentmods.dev/commands/liam-hq/liam/benchmark-execute"><img src="https://agentmods.dev/badge/commands/liam-hq/liam/benchmark-execute/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/liam-hq/liam/benchmark-execute"><img src="https://agentmods.dev/badge/commands/liam-hq/liam/benchmark-execute.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.01048 |
| Opus 5.5 | $0.00006 | $0.00419 |
| Sonnet 5.5 | $0.00003 | $0.00210 |
| Haiku 4.5 | $0.00001 | $0.00105 |
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 2mo 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 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}}
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.
- 2mo ago First seen · 104 lines · 14 tokens per session scan C 42ef011c3f19
benchmark-execute is a command published in the GitHub repository liam-hq/liam (5,156 stars, last pushed yesterday), licensed Apache-2.0. It adds 14 tokens to every session and 1,048 once invoked, about $0.0001 per session on Opus 5.5. 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.
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