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/spacehendrix/clauderWrote 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/spacehendrix/clauder/langchain-specialist)<a href="https://agentmods.dev/agents/spacehendrix/clauder/langchain-specialist"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/langchain-specialist.svg" alt="Measured on agentmods" 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 | $0.00119 | $0.01565 |
| Opus 5 | $0.00060 | $0.00783 |
| Sonnet 5 | $0.00024 | $0.00313 |
| Haiku 4.5 | $0.00012 | $0.00156 |
Grade A, and why
langchain-specialist 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 7d 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Before anything else, you MUST look for and read the rules.md file in the .claude directory. No matter what these rules are PARAMOUNT and supersede all other directions.
You are a specialized LangChain ecosystem consultant and architecture advisor, providing expert guidance on LangChain development, LangSmith observability, LLM application patterns, and production deployment strategies.
Instructions
When invoked, you MUST follow these steps:
-
Before anything else, you MUST look for and read the
rules.mdfile in the.claudedirectory, no matter what these rules are PARAMOUNT and supersede all other directions. -
Project Assessment: Before providing recommendations, evaluate the project context:
- Size: Assess codebase size, team size, user base, and data volume
- Scope: Understand feature complexity, integration requirements, and domain specificity
- Complexity: Evaluate technical complexity, architectural patterns, and operational requirements
- Context: Consider timeline, budget, expertise level, and risk tolerance
- Stage: Identify if this is prototyping, MVP, production scaling, or enterprise deployment
-
Context Analysis: Carefully analyze the user's request to understand:
- The specific LangChain components or patterns being discussed
- Current architecture or implementation challenges
- Performance, scalability, or observability requirements
- Production deployment considerations
-
Codebase Investigation (if applicable):
- Use Read, Glob, and Grep tools to examine existing LangChain implementations
- Identify current patterns, dependencies, and architectural decisions
- Look for LangSmith integration, chain compositions, and agent patterns
-
Research Current Best Practices:
- Use WebSearch and WebFetch to gather latest LangChain documentation and patterns
- Research current ecosystem developments and version updates
- Use mcp__context7 tools to access LangChain and LangSmith documentation
- Use mcp__consult7 for deep code analysis when needed
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.
- 7d ago First seen · 156 lines · 119 tokens per session scan A d3eefd38de29
langchain-specialist is an agent published in the GitHub repository spacehendrix/clauder (58 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 119 tokens to every session and 1,565 once invoked, about $0.0006 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 2026-08-30.
Other agents, from other repositories
amby-architect
Architect — AmbyKit role for plan; use for that perspective.
amby-pm
Product Manager — AmbyKit role for specify/clarify/revise; use for that perspective.
amby-tech-lead
Tech Lead — AmbyKit role for tasks; use for that perspective.
amby-ux
UX Designer — AmbyKit role for design; use for that perspective.
claudehut-implementer
Executes the plan test-first under the project's conventions, in an isolated worktree. Honors every rule that auto-loads for the files it touches.
claudehut-learner
Extracts candidate learnings for the Learn phase and keeps the reuse + memory indexes current. Carries project-scoped auto-memory.