Skills llm-application-dev-langchain-agent
📦
llm-application-dev-langchain-agent
Content revision r1 Safe ⚙️ External commands🌐 Network access
Build Production LangChain Agents
Production LangChain agents need clear architecture, memory, evaluation, and observability. This skill guides Claude, Codex, and Claude Code through LangGraph patterns, async tools, RAG, testing, and deployment.
Supports: Claude Codex Code(CC)
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "llm-application-dev-langchain-agent" from https://skillstore.io/skills/sickn33-llm-application-dev-langchain-agent.md and its manifest at https://skillstore.io/api/skills/sickn33-llm-application-dev-langchain-agent/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "llm-application-dev-langchain-agent". Design a customer support agent with RAG and streaming responses.
Expected outcome:
- Architecture: intake, retrieval, tool execution, response, and escalation nodes.
- Memory: short-term thread memory plus vector retrieval for support documents.
- Production checks: tracing, retries, timeouts, streaming tests, and health checks.
Using "llm-application-dev-langchain-agent". Review my agent plan before launch.
Expected outcome:
- Main risk: missing timeout and retry boundaries around model and retrieval calls.
- Observability gap: add trace IDs, LangSmith runs, latency metrics, and error counters.
- Next step: write evaluation cases for tool selection, answer quality, and fallback behavior.
Using "llm-application-dev-langchain-agent". Plan a multi-agent research workflow.
Expected outcome:
- Use a supervisor node to route between research, synthesis, and review agents.
- Keep shared state small and persist long-term context through a retriever.
- Evaluate routing accuracy, source relevance, response quality, and cost per task.
Security Audit
Safev4 • 7/7/2026 Open versioned report
No evidence of executable shell commands, malicious network access, system reconnaissance, or prompt injection was found. The static alerts are Markdown backticks, fenced illustrative Python snippets, or benign terminology in SKILL.md.
1
Files scanned
247
Lines analyzed
0
Review items
0
False positives ignored
Risk Factors
⚙️ External commands (28)
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🌐 Network access (1)
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
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<iframe src="https://skillstore.io/embed/skills/sickn33-llm-application-dev-langchain-agent.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
sickn33. (2026). llm-application-dev-langchain-agent security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-llm-application-dev-langchain-agent/audits/4BibTeX citation
@techreport{sickn33-sickn33-llm-application-dev-langchain-agent-2026,
author = {sickn33},
title = {llm-application-dev-langchain-agent security audit report (audit version 4)},
institution = {Skillstore},
year = {2026},
number = {4},
url = {https://skillstore.io/skills/sickn33-llm-application-dev-langchain-agent/audits/4},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "llm-application-dev-langchain-agent security audit report (audit version 4)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-07"
url: "https://skillstore.io/skills/sickn33-llm-application-dev-langchain-agent/audits/4"
identifiers:
- type: other
value: "skillstore:sickn33-llm-application-dev-langchain-agent:audit:4"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: High 55
Architecture
85
Maintainability
87
Content
69
Community
83
Spec Compliance
What You Can Build
Plan an Agent Architecture
Map a LangGraph agent into state, nodes, routing, tools, memory, and evaluation steps before implementation.
Prepare for Production Deployment
Review async execution, streaming, retries, monitoring, caching, health checks, and documentation for a production agent service.
Design a RAG Workflow
Choose embeddings, vector retrieval, hybrid search, reranking, and memory patterns for a retrieval-augmented agent.
Try These Prompts
Beginner Architecture Outline
Design a basic LangGraph agent for my use case: [describe task]. Include state, nodes, tools, memory, and testing steps.
RAG Agent Design
Design a RAG agent for [domain]. Recommend retrieval, memory, reranking, observability, and failure handling choices.
Production Readiness Review
Review this LangChain agent design for production readiness: [paste summary]. Identify gaps in async handling, monitoring, security, cost, and testing.
Multi-Agent Implementation Plan
Create an implementation plan for a multi-agent LangGraph system with supervisor routing, shared state, tool boundaries, evaluation, and deployment checks.
Best Practices
- Use async model, tool, and retriever calls with bounded retries and timeouts.
- Trace agent runs and collect metrics for latency, errors, cost, and answer quality.
- Keep secrets in environment variables and validate all external tool inputs.
Avoid
- Hardcoding provider keys, model credentials, or vector database secrets in prompts or examples.
- Shipping an agent without evaluation datasets, integration tests, or rollback criteria.
- Allowing tools to call external services without timeouts, validation, or audit logging.
Frequently Asked Questions
What does this skill help build?
It helps design LangChain and LangGraph agents with memory, tools, retrieval, observability, testing, and deployment practices.
Does it generate a full project?
No. It provides guidance and examples, but it does not include a runnable project scaffold.
Which tools can use this skill?
The report marks it as compatible with Claude, Codex, and Claude Code.
Can it help with RAG systems?
Yes. It covers embeddings, vector stores, hybrid retrieval, reranking, and memory patterns for RAG agents.
Does it cover production operations?
Yes. It includes monitoring, streaming, retries, health checks, caching, testing, and documentation guidance.
What should users verify before implementation?
Users should verify current library APIs, provider credentials, infrastructure settings, and security requirements.