Borrowing it
Nothing to install: this file belongs to vishnu2kmohan/mcp-server-langgraph. 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/vishnu2kmohan/mcp-server-langgraph/main/.claude/commands/benchmark.mdgit clone --depth 1 https://github.com/vishnu2kmohan/mcp-server-langgraphWrote 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/vishnu2kmohan/mcp-server-langgraph/benchmark)<a href="https://agentmods.dev/commands/vishnu2kmohan/mcp-server-langgraph/benchmark"><img src="https://agentmods.dev/badge/commands/vishnu2kmohan/mcp-server-langgraph/benchmark/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/vishnu2kmohan/mcp-server-langgraph/benchmark"><img src="https://agentmods.dev/badge/commands/vishnu2kmohan/mcp-server-langgraph/benchmark.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 | $0.00006 | $0.03856 |
| Opus 5.5 | $0.00002 | $0.01542 |
| Sonnet 5.5 | $0.00001 | $0.00771 |
| Haiku 4.5 | $0.00001 | $0.00386 |
Grade A, and why
benchmark scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 576 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Performance Benchmarks
Usage: /benchmark or /benchmark --quick
Purpose: Execute performance benchmarks and analyze results
🎯 What This Command Does
Runs the project's performance benchmark suite using the existing Makefile command:
make benchmark
This executes benchmarks for:
- Agent response time
- LLM call latency
- Authorization check performance
- Database query performance
- Context loading speed
📊 Execution Steps
Step 1: Run Benchmarks
Execute the benchmark suite:
# Full benchmark suite
make benchmark
# View benchmark configuration
cat tests/benchmark/config.yaml
Step 2: Parse Results
Analyze benchmark output:
# Benchmark results are typically in:
# - pytest output (console)
# - .benchmark/ directory (if configured)
# Look for metrics like:
# - ops/sec (operations per second)
# - mean, median, p95, p99 latencies
# - memory usage
# - CPU usage
Step 3: Compare with Baselines
# If baseline metrics exist
if [ -f "tests/regression/baseline_metrics.json" ]; then
echo "Comparing with baseline..."
# Check for regressions (>20% slower)
fi
Step 4: Generate Summary
Provide user with formatted summary:
=== Performance Benchmark Results ===
Benchmark Suite: Agent Performance
Executed: YYYY-MM-DD HH:MM:SS
Results:
┌─────────────────────────────┬──────────┬─────────┬─────────┐
│ Benchmark │ Mean │ P95 │ Status │
├─────────────────────────────┼──────────┼─────────┼─────────┤
│ agent_response │ 2.3s │ 4.5s │ ✅ PASS │
│ llm_call │ 1.8s │ 3.2s │ ✅ PASS │
│ authorization_check │ 25ms │ 45ms │ ✅ PASS │
│ database_query │ 15ms │ 30ms │ ✅ PASS │
│ context_loading │ 120ms │ 200ms │ ✅ PASS │
└─────────────────────────────┴──────────┴─────────┴─────────┘
Baseline Comparison:
- No regressions detected ✅
- 2/5 benchmarks improved (context_loading: -15ms)
Memory Usage:
- Peak: 256 MB
- Average: 180 MB
Overall Status: ✅ ALL BENCHMARKS PASSING
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.
- 1mo ago First seen · 576 lines · 6 tokens per session scan A de06674579dc
benchmark is a command published in the GitHub repository vishnu2kmohan/mcp-server-langgraph (4 stars, last pushed 2d ago), licensed MIT. It adds 6 tokens to every session and 3,856 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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