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.
npx skills add joonlab/joonlab-claudecode-setting-for-share --skill langsmith-evaluatorgit clone --depth 1 https://github.com/joonlab/joonlab-claudecode-setting-for-shareWrote 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/skills/joonlab/joonlab-claudecode-setting-for-share/langsmith-evaluator)<a href="https://agentmods.dev/skills/joonlab/joonlab-claudecode-setting-for-share/langsmith-evaluator"><img src="https://agentmods.dev/badge/skills/joonlab/joonlab-claudecode-setting-for-share/langsmith-evaluator/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/skills/joonlab/joonlab-claudecode-setting-for-share/langsmith-evaluator"><img src="https://agentmods.dev/badge/skills/joonlab/joonlab-claudecode-setting-for-share/langsmith-evaluator.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 | $0.00083 | $0.03644 |
| Opus 5 | $0.00042 | $0.01822 |
| Sonnet 5 | $0.00017 | $0.00729 |
| Haiku 4.5 | $0.00008 | $0.00364 |
Grade C, and why
langsmith-evaluator scanned grade C with 2 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 9d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -sSL https://raw.githubusercontent.com/langchain-ai/langsmith-cli/main/scripts/install.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sSL https://raw.githubusercontent.com/langchain-ai/langsmith-cli/main/scripts/install.sh | sh This is a copy
89% identical to langsmith-evaluator — 76 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 363 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LANGSMITH_API_KEY=lsv2_pt_your_api_key_here # Required
LANGSMITH_PROJECT=your-project-name # Check this to know which project has traces
LANGSMITH_WORKSPACE_ID=your-workspace-id # Optional: for org-scoped keys
OPENAI_API_KEY=your_openai_key # For LLM as Judge
IMPORTANT: Always check the environment variables or .env file for LANGSMITH_PROJECT before querying or interacting with LangSmith. This tells you which project contains the relevant traces and data. If the LangSmith project is not available, use your best judgement to identify the right one.
Python Dependencies
pip install langsmith langchain-openai python-dotenv
CLI Tool (for uploading evaluators)
curl -sSL https://raw.githubusercontent.com/langchain-ai/langsmith-cli/main/scripts/install.sh | sh
JavaScript Dependencies
npm install langsmith openai
<crucial_requirement>
Golden Rule: Inspect Before You Implement
CRITICAL: Before writing ANY evaluator or extraction logic, you MUST:
- Run your agent on sample inputs and capture the actual output
- Inspect the output - print it, query LangSmith traces, understand the exact structure
- Only then write code that processes that output
Output structures vary significantly by framework, agent type, and configuration. Never assume the shape - always verify first. Query LangSmith traces to when outputs don't contain needed data to understand how to extract from execution. </crucial_requirement>
<evaluator_format>
Offline vs Online Evaluators
Offline Evaluators (attached to datasets):
- Function signature:
(run, example)- receives both run outputs and dataset example - Use case: Comparing agent outputs to expected values in a dataset
- Upload with:
--dataset "Dataset Name"
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.
- 9d ago First seen · 363 lines · 83 tokens per session scan C b28078e85c10
langsmith-evaluator is a skill published in the GitHub repository joonlab/joonlab-claudecode-setting-for-share (10 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 3,644 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). It is 89% identical to langsmith-evaluator, differing in 76 lines, and is treated as a copy.
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