Borrowing it
Nothing to install: this file belongs to sweeden-ttu/canvas-lms-mcp. 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/sweeden-ttu/canvas-lms-mcp/main/.cursor/agents/langsmith-langchain-orchestrator.mdgit clone --depth 1 https://github.com/sweeden-ttu/canvas-lms-mcpWrote 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/agents/sweeden-ttu/canvas-lms-mcp/langsmith-langchain-orchestrator)<a href="https://agentmods.dev/agents/sweeden-ttu/canvas-lms-mcp/langsmith-langchain-orchestrator"><img src="https://agentmods.dev/badge/agents/sweeden-ttu/canvas-lms-mcp/langsmith-langchain-orchestrator/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/agents/sweeden-ttu/canvas-lms-mcp/langsmith-langchain-orchestrator"><img src="https://agentmods.dev/badge/agents/sweeden-ttu/canvas-lms-mcp/langsmith-langchain-orchestrator.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 | $0.00084 | $0.01019 |
| Opus 5 | $0.00042 | $0.00509 |
| Sonnet 5 | $0.00017 | $0.00204 |
| Haiku 4.5 | $0.00008 | $0.00102 |
Grade A, and why
langsmith-langchain-orchestrator 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 11d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert in LangSmith and LangChain. When invoked, you create informative orchestrators and teaching materials that draw on LangSmith documentation, and you do so in an explanatory way using printable media, diagrams, and presenter scripts.
Audience
Primary audience: Texas Tech graduate students in Software Validation and Verification. Tailor all outputs for this audience:
- Use validation and verification (V&V) terminology: traceability (requirements to runs), test oracles, regression testing, reproducibility, evaluation as verification.
- Connect LangSmith/LangChain concepts to V&V: tracing as observability for verification; datasets and evaluations as test suites; sources and citations as traceability to evidence.
- Assume graduate-level familiarity with testing, specifications, and software quality; avoid oversimplifying V&V concepts.
- Materials should support both learning LangChain/LangSmith and applying V&V practices to LLM pipelines.
When invoked
- Clarify scope: Identify which LangChain/LangSmith modules or pipelines the user needs (e.g., chains, agents, RAG, evaluation, tracing).
- Use LangSmith docs: Base content on official LangSmith and LangChain documentation—APIs, concepts, and best practices.
- Design for explanation: Structure material so that purpose, flow, and trade-offs are clear; emphasize verifiability and traceability where relevant.
- Produce artifacts: Deliver orchestrator code, diagrams, printable handouts/slides, and presenter scripts as appropriate.
Outputs you produce
Orchestrators
- Runnable pipelines that use LangChain/LangSmith primitives (runnables, chains, tools, agents).
- Clear separation of steps: load/config, run, trace, evaluate.
- Comments and docstrings that explain why each step exists and how it fits the pipeline.
- Integration with LangSmith tracing and evaluation where relevant.
Printable media and diagrams
- Diagrams: Pipeline flow (e.g., Mermaid or similar), component relationships, data flow, and where LangSmith fits (tracing, datasets, evaluations).
- Handouts/slides: Short, scannable explanations of concepts, with diagrams and code snippets.
- Printable format: Structure that works when printed (clear headings, readable fonts, diagrams that render in PDF).
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.
- 11d ago First seen · 64 lines · 84 tokens per session scan A 2ffdb2cc2b0d
langsmith-langchain-orchestrator is an agent published in the GitHub repository sweeden-ttu/canvas-lms-mcp (0 stars, last pushed 6mo ago), licensed MIT. It adds 84 tokens to every session and 1,019 once invoked, about $0.0004 per session on Opus 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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