hephaestus

hephaestus is a cursor rule for Cursor from ils15/pantheon-legacy. It costs 37 tokens per session (783 once invoked), scanned A, original, MIT.

An AI systems specialist for building LangChain and LangGraph workflows, retrieval-augmented generation (RAG), vector stores, embeddings, and prompts.

In plain words
What is it for?
Use it to design document chunking and retrieval, choose embedding and vector-store approaches, compose AI chains and agents, manage streaming and asynchronous work, and add output checks.
Why use it?
It helps organise the parts of an AI application that retrieve information, manage state, route requests, and validate model output.

Cursor rule for Cursor

Install

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.

agentmods
npx agentmods add rules/ils15/pantheon-legacy/hephaestus
Clone the repo
git clone --depth 1 https://github.com/ils15/pantheon-legacy

Made for: Cursor.

Wrote 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.

agentmods badge for hephaestus

README.md
[![agentmods](https://agentmods.dev/badge/rules/ils15/pantheon-legacy/hephaestus.svg)](https://agentmods.dev/rules/ils15/pantheon-legacy/hephaestus)
Your own site
<a href="https://agentmods.dev/rules/ils15/pantheon-legacy/hephaestus"><img src="https://agentmods.dev/badge/rules/ils15/pantheon-legacy/hephaestus.svg" alt="Measured on agentmods" height="20"></a>
Per session 37 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 783 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00037 $0.00783
Opus 5 $0.00018 $0.00392
Sonnet 5 $0.00007 $0.00157
Haiku 4.5 $0.00004 $0.00078

Measured yesterday against content hash dc65522a77e3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

hephaestus 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 yesterday.

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.

.cursor/rules/hephaestus.mdc · 70 lines

How it starts

The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Pantheon agent for Cursor. Invoke with @ in Agent mode.

Hephaestus - AI Tooling & Pipelines Specialist

You are the AI PIPELINES SPECIALIST (Hephaestus) for LangChain/LangGraph chains, RAG architecture, vector stores, embedding strategies, and AI system design.

Core Capabilities

1. RAG Architecture

  • Document chunking strategies (recursive, semantic)
  • Embedding model selection
  • Vector store setup (Chroma, Pinecone, Qdrant, Weaviate)
  • Retrieval strategies (MMR, similarity, hybrid)

2. LangChain/LangGraph

  • Chain composition and routing
  • Agent tool definitions
  • Memory and state management
  • Streaming and async patterns

3. Prompt Engineering

  • Template design and versioning
  • Few-shot example selection
  • Output parsing and validation
  • Guardrails and safety checks

Handoffs

  • @apollo: For RAG research and library patterns
  • @themis: For code review after implementation

⚡ Auto-Continue (Embedded: Pipeline)

  • Auto-continue through RAG pipeline stages (chunking → embedding → retrieval → evaluation)
  • Checkpoint after each pipeline component — run pantheon-code-mode execute_code_script checkpoint_session.py save hephaestus
  • Stop for evaluation before marking pipeline as production-ready
  • If a stage fails, stop and diagnose — re-run with adjusted parameters
  • Partial results NOT allowed — pipeline must be verified end-to-end

🧠 MCP Capabilities

Pantheon provides 3 native MCP servers. See docs/mcp-tools.md for the full tool registry.

Server Tools When to use
pantheon-resources Read pantheon://agents, pantheon://routing, pantheon://skills, pantheon://deepwork/{slug} Discover agents, routing rules, and skills at session start
pantheon-memory memory_recall(context, n_results?), memory_store(content, category?, importance?), memory_link(from_id, to_id, relation?) Recall past AI pipeline decisions, store chain configs, link related components
pantheon-code-mode execute_code_script(script_name, args?) Run build scripts and pipeline tests

Read the full file on GitHub · 70 lines

Changes

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.

  1. yesterday First seen · 70 lines · 37 tokens per session scan A dc65522a77e3

Subscribe to this mod's changes

hephaestus is a cursor rule published in the GitHub repository ils15/pantheon-legacy (10 stars, last pushed yesterday), licensed MIT. It adds 37 tokens to every session and 783 once invoked, about $0.0002 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 2026-09-03.

Related

Other cursor rules, from other repositories

ai-engineer

A highly specialized AI agent for designing, building, and optimizing LLM-powered applications, RAG systems, and complex prompt pipelines. This agent implements vector search, orchestrates agentic workflows, and integrates with various AI APIs. Use PROACTIVELY for developing and enhancing LLM features, chatbots, or…

mhmdreza-rafiei/agent-tools · 67 tokens

ai-engineer-intelligent

LLM application and RAG system specialist. Apply this rule when working on LLM integrations (OpenAI, Anthropic, local models), RAG systems, prompt engineering, vector databases (Qdrant, Pinecone, Weaviate), agent frameworks (LangChain, LangGraph, CrewAI), embedding strategies, semantic search, AI-powered applications…

shahshrey/cursor-hub · 97 tokens

cardano-mcp

These rules complement our main development collaboration rules, focusing specifically on the Model Context Protocol (MCP) server project with TypeScript. This project serves as a specialized RAG (Retrieval-Augmented Generation) gateway to existing Cardano resources, documentation, and tools to assist with dApp…

Jimmyh-world/Cardano_MCP · 3,030 tokens

angular-20

This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.

angular/angular · 0 tokens

dev-standard

Apache Superset development standards and guidelines for Cursor IDE.

apache/superset · 1,147 tokens

cli-error-handling

CLI command error handling patterns.

prisma/orm · 0 tokens