Skills langchain-architecture
📦

langchain-architecture

Content revision r1 Safe ⚙️ External commands

Design LangChain Application Architectures

LLM applications can become difficult to structure as agents, memory, retrieval, and tools grow. This skill guides LangChain design choices and production checks.

Supports: Claude Codex Code(CC)
🥉 79 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "langchain-architecture" from https://skillstore.io/skills/sickn33-langchain-architecture.md and its manifest at https://skillstore.io/api/skills/sickn33-langchain-architecture/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.

Markdown detail GET /skills/sickn33-langchain-architecture.md Signed manifest GET /api/skills/sickn33-langchain-architecture/manifest Signed lockfile GET /api/skills/sickn33-langchain-architecture/lockfile

Test it

Using "langchain-architecture". Design a customer support RAG assistant for product manuals.

Expected outcome:

  • Recommended pattern: retrieval augmented generation with source documents returned.
  • Core components: document loader, text splitter, embeddings, vector store, retriever, and answer chain.
  • Production checks: source visibility, stale document handling, token limits, and retrieval quality tests.

Using "langchain-architecture". Choose memory for a long-running chat assistant.

Expected outcome:

  • Use summary memory for older context and window memory for recent turns.
  • Track important entities separately when user preferences or account details matter.
  • Add tests for context drift, privacy boundaries, and maximum prompt length.

Using "langchain-architecture". Review an agent with search and email tools.

Expected outcome:

  • Separate read-only search tools from tools that cause external actions.
  • Require confirmation before sending messages or making irreversible changes.
  • Log tool calls, failures, token usage, and user approval decisions.

Security Audit

Safe
v4 • 7/7/2026 Open versioned report

The external command findings are false positives caused by Markdown inline code and fenced Python examples, not shell execution. The network reconnaissance blocker is also a false positive because the cited text describes AI agents with tool access, not scanning or probing networks.

1
Files scanned
351
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

⚙️ External commands (20)
SKILL.md:20 SKILL.md:86-111 SKILL.md:111-116 SKILL.md:116-144 SKILL.md:144-147 SKILL.md:147-172 SKILL.md:172-175 SKILL.md:175-207 SKILL.md:207-212 SKILL.md:212-232 SKILL.md:232-237 SKILL.md:237-258 SKILL.md:258-262 SKILL.md:262-285 SKILL.md:285-290 SKILL.md:290-295 SKILL.md:295-298 SKILL.md:298-311 SKILL.md:311-314 SKILL.md:314-318
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 →
Share & cite this report

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Open versioned report
Security Assessment

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https://skillstore.io/skills/sickn33-langchain-architecture/audits/4?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). langchain-architecture security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-langchain-architecture/audits/4

BibTeX citation

@techreport{sickn33-sickn33-langchain-architecture-2026, author = {sickn33}, title = {langchain-architecture security audit report (audit version 4)}, institution = {Skillstore}, year = {2026}, number = {4}, url = {https://skillstore.io/skills/sickn33-langchain-architecture/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: "langchain-architecture security audit report (audit version 4)" version: "unspecified" type: report authors: - name: "sickn33" date-released: "2026-07-07" url: "https://skillstore.io/skills/sickn33-langchain-architecture/audits/4" identifiers: - type: other value: "skillstore:sickn33-langchain-architecture:audit:4" description: "Skillstore immutable audit report identifier"

Compare variants

3 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score
sickn33 Recommended Current

sickn33-langchain-architecture

Skillstore Score 79
Evidence Confidence High
Skillstore usage 11
Updated

2026-08-21

wshobson

wshobson-langchain-architecture

Skillstore Score 78
Evidence Confidence High
Skillstore usage 18
Updated

2026-08-21

sickn33

langchain-architecture

Skillstore Score 78
Evidence Confidence High
Skillstore usage 10
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
85
Maintainability
87
Content
69
Community
91
Spec Compliance

What You Can Build

Plan a RAG Assistant

Design document loading, chunking, vector storage, retrieval, and source return behavior for a knowledge assistant.

Structure Agent Tools

Define agent tool boundaries, descriptions, memory behavior, and execution checks before implementation.

Review Production Readiness

Assess error handling, callbacks, token monitoring, rate limits, testing, and fallback behavior for a LangChain workflow.

Try These Prompts

Choose a Pattern 
I am building a LangChain app for [goal]. Compare agent, chain, and RAG patterns. Recommend one design and explain the tradeoffs.
Design Retrieval 
Design a LangChain RAG workflow for [document type]. Include loaders, chunking, embeddings, vector store choice, retrieval settings, and source handling.
Add Memory and Tools 
Create an architecture for an agent that uses [tools] and remembers [context]. Define memory type, tool descriptions, guardrails, and failure handling.
Audit Production Readiness 
Review this LangChain architecture for production. Check observability, testing, latency, cost, retries, rate limits, input validation, and security risks.

Best Practices

  • Start with the simplest chain or RAG pattern before adding autonomous agent behavior.
  • Write clear tool descriptions and enforce validation around action-capable tools.
  • Add callbacks, tests, rate limits, timeouts, and cost monitoring before production use.

Avoid

  • Giving an agent broad tool access without approvals or execution limits.
  • Using unbounded conversation memory until the context window becomes unreliable.
  • Shipping retrieval without checking chunk quality, source accuracy, and stale data handling.

Frequently Asked Questions

What does this skill help me design?
It helps design LangChain applications with agents, chains, memory, retrieval, callbacks, testing, and production checks.
Does it generate a full application?
No. It provides architecture guidance and examples that you adapt to your codebase and current LangChain version.
Can I use it with Claude, Codex, and Claude Code?
Yes. The report marks support for Claude, Codex, and Claude Code workflows.
Is it focused only on RAG?
No. It also covers agents, sequential chains, memory strategies, callbacks, testing, and performance patterns.
What should I verify before production?
Verify error handling, input validation, rate limits, token costs, observability, tool approvals, and retrieval accuracy.
Are the examples version specific?
The examples are conceptual and may require API updates for the LangChain version used in your project.

Developer Details

Author

sickn33

License

MIT

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/web-app/public/skills/langchain-architecture

Ref

816c62b2546ddb1c6a0453e7c781b5e095117819

Maintenance freshness

7/18/2026

Usage

8 downloads · 109 views

File structure

📄 SKILL.md

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