langchain-architecture

Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

Quality

66%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./.github/skills/SKILL.md

Quality

Content

65%Weight 40%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable with abundant executable LangChain code and clear topical organization, but it explains concepts Claude already knows and its progressive-disclosure references point to bundle files that are not present in the repository.

Suggestions

Remove or condense definitional prose for agents/chains/memory/callbacks that Claude already knows to improve token efficiency.

Create the referenced bundle files (references/*.md and assets/*.py) or remove the dangling Resources links so navigation actually works.

Add explicit validation/verification checkpoints to the production-oriented patterns (e.g., assert tool output, validate chain I/O keys) to raise workflow clarity.

Dimension	Reasoning	Score
Conciseness	The body is mostly code and structured lists, but it pads in concepts Claude already knows (e.g., 'Autonomous systems that use LLMs to decide which actions to take', 'Sequences of calls to LLMs or other utilities', 'Hooks for logging, monitoring, and debugging') and runs ~330 lines, so it could be tightened.	2 / 3
Actionability	It provides multiple copy-paste-ready, executable code blocks (Quick Start, RAG pattern, custom agent with @tool, SequentialChain, callbacks, tests), with specific imports and runnable snippets.	3 / 3
Workflow Clarity	Content is well-organized by topic and includes a Production Checklist, but the architecture patterns show sequences without explicit validation checkpoints or validate→fix→retry feedback loops for risky operations.	2 / 3
Progressive Disclosure	The Resources section signals one-level-deep references (references/agents.md, references/memory.md, assets/agent-template.py, etc.), but those bundle files do not actually exist in references/ or assets/, so the signaled navigation is broken and the dense inline body is not truly split out.	2 / 3
	Total	9 / 12 Passed

Description

67%Weight 40%Scale 1-3

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description cleanly answers both 'what' and 'when' with an explicit trigger clause and a clear LangChain niche, but it stays at a moderate specificity level and uses somewhat jargon-leaning trigger terms with mild overlap risk against generic agent-building skills.

Suggestions

Lead with concrete verbs beyond just 'Design' (e.g., 'Build, compose, and debug agent/chain pipelines') to push specificity toward a fuller action list.

Add plainer user-facing trigger phrasing (e.g., 'Use when the user mentions LangChain, agents, chains, or conversation memory') to broaden natural keyword coverage.

Sharpen distinctiveness by scoping triggers to LangChain-specific tasks rather than generic 'implementing AI agents'.

Dimension	Reasoning	Score
Specificity	Quotes 'Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns' — it names the domain and several concrete components (agents, memory, tool integration), but the only true action verb is 'Design'; the rest are capability areas rather than a comprehensive list of concrete actions.	2 / 3
Completeness	It states both what ('Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns') and when ('Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows') with an explicit 'Use when' clause.	3 / 3
Trigger Term Quality	Triggers like 'building LangChain applications, implementing AI agents, creating complex LLM workflows' are reasonably natural, but phrasing leans technical ('tool integration patterns', 'complex LLM workflows') and omits common variations a user might say.	2 / 3
Distinctiveness Conflict Risk	The LangChain framing is a fairly clear niche, but 'implementing AI agents' and 'complex LLM workflows' are broad enough that the description could overlap with other general agent- or LLM-building skills.	2 / 3
	Total	9 / 12 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
referenced_paths_exist	Referenced path issues: 8 missing	Warning

	Total	15 / 16 Passed

Repository: Kuingsmile/word-GPT-Plus
Path: .github/skills/SKILL.md
Commit: 8b0dcf5

Reviewed: 12 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.