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.
git clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-skillsWrote 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/rules/bilal140202/the-lord-of-the-skills/canonical__langchain)<a href="https://agentmods.dev/rules/bilal140202/the-lord-of-the-skills/canonical__langchain"><img src="https://agentmods.dev/badge/rules/bilal140202/the-lord-of-the-skills/canonical__langchain/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/rules/bilal140202/the-lord-of-the-skills/canonical__langchain"><img src="https://agentmods.dev/badge/rules/bilal140202/the-lord-of-the-skills/canonical__langchain.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 | $0.00000 | $0.00338 |
| Opus 5 | $0.00000 | $0.00169 |
| Sonnet 5 | $0.00000 | $0.00068 |
| Haiku 4.5 | $0.00000 | $0.00034 |
Grade A, and why
canonical__langchain 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 12d 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.
What it actually says
LangChain Cursor Rules
Architecture
- Use LangChain Expression Language (LCEL) for chain composition
- Use
|pipe operator for chaining:prompt | llm | output_parser - Use
RunnablePassthroughfor passing data through chains - Use
RunnableBranchfor conditional logic
RAG (Retrieval-Augmented Generation)
- Use appropriate text splitters:
RecursiveCharacterTextSplitter(default) - Choose embedding model based on use case (OpenAI, Cohere, HuggingFace)
- Use vector stores: Pinecone (managed), Chroma (local), pgvector (Postgres)
- Implement retrieval chain: query → retrieve → augment prompt → generate
Agents
- Use tool-calling agents for tasks requiring external actions
- Define tools with clear descriptions (LLM reads these to decide)
- Set max iterations to prevent infinite loops
- Use structured output with Pydantic models
Memory
- Use
ConversationBufferMemoryfor short conversations - Use
ConversationSummaryMemoryfor long conversations - Use
ConversationBufferWindowMemoryfor fixed-window history
Best Practices
- DO: Use streaming for better UX:
chain.stream(input) - DO: Implement fallbacks:
chain.with_fallbacks([fallback_chain]) - DO: Add callbacks for logging and monitoring
- DON'T: Chain too many LLM calls — each adds latency and cost
- DON'T: Skip input validation — sanitize user inputs
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.
- 12d ago First seen · 38 lines · 0 tokens per session scan A c40a6b423896
canonical__langchain is a cursor rule published in the GitHub repository Bilal140202/the-lord-of-the-skills (4 stars, last pushed 6d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 338 tokens. 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.
Other cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
cli-error-handling
CLI command error handling patterns.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.
control-plane-descriptors
Control plane descriptor and instance implementation patterns.