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mastering-langgraph

1 forks
95
A

Build stateful AI agents and agentic workflows with LangGraph in Python. Covers tool-using agents with LLM-tool loops, branching workflows, conversation memory, human-in-the-loop oversight, and production monitoring. Use when - (1) building agents that use tools and loop until task complete, (2) creating multi-step workflows with conditional branches, (3) adding persistence/memory across turns with checkpointers, (4) implementing human approval with interrupt(), (5) debugging via time-travel ...

#references#agents#langgraph#nodes#memory#agentic-skill#Development Workflow#Build
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Installation for Agentic Skill

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skilz install SpillwaveSolutions/mastering-langgraph-agent-skill/mastering-langgraph
skilz install SpillwaveSolutions/mastering-langgraph-agent-skill/mastering-langgraph --agent opencode
skilz install SpillwaveSolutions/mastering-langgraph-agent-skill/mastering-langgraph --agent codex
skilz install SpillwaveSolutions/mastering-langgraph-agent-skill/mastering-langgraph --agent gemini

First time? Install Skilz: pip install skilz

Works with 14 AI coding assistants

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Extract and copy to ~/.claude/skills/ then restart Claude Desktop

1. Clone the repository:
git clone https://github.com/SpillwaveSolutions/mastering-langgraph-agent-skill
2. Copy the agent skill directory:
cp -r mastering-langgraph-agent-skill ~/.claude/skills/
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Need detailed installation help? Check our platform-specific guides:

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Agentic Skill Details

Owner
SpillwaveSolutions (GitHub)
Repository
mastering-langgraph-agent-skill
Forks
1
Type
Technical
Meta-Domain
development
Primary Domain
python
Market Score
95
Agentic Skill Grades →

Agent Skill Grade

A
Score: 95/100 Click to see breakdown

Score Breakdown

Spec Compliance
14/15
PDA Architecture
27/30
Ease of Use
22/25
Writing Style
9/10
Utility
18/20
Modifiers: +5

Areas to Improve

  • Redundant operator.add explanations
  • Missing TOC in some reference files
  • Common Pitfalls duplicated

Recommendations

  • Add trigger phrases to description for discoverability
  • Add table of contents for files over 100 lines

Graded: 2026-01-19

Developer Feedback

I took a look at your mastering-langgraph skill and wanted to share some thoughts.

Links:

The TL;DR

You're at 95/100, solid A grade territory. This is based on Anthropic's skill best practices framework. Your strongest area is Spec Compliance (14/15) – the YAML frontmatter is clean and your naming conventions are spot-on. Weakest pillar is Utility (18/20), mainly around feedback loops and validation checkpoints, but honestly it's a small gap. The skill is production-ready.

What's Working Well

  • Progressive Disclosure Architecture is chef's kiss – SKILL.md sits at ~300 lines as a tight hub, cleanly routing to 9 specialized reference files. No nested rabbit holes. You're getting maximum bang for your tokens.
  • Explicit trigger phrases – "LangGraph", "StateGraph", "tool-using agents", "interrupt()" are all discoverable and specific. Developers looking for agent patterns will find this.
  • Working examples throughout – Your Quick Start code is copy-paste ready with expected output. The production checklist doubles as a deployment template. That's practical.
  • Consistent terminology – "nodes", "edges", "state", "checkpointer" stay consistent across all 11 files. No confusing terminology shifts.

The Big One: Redundant Explanations Eating Your Points

Here's the thing: you're explaining operator.add aggregation in both SKILL.md (lines 150-156) and core-api.md (lines 36-51) with nearly identical tables. Same with Common Pitfalls – they appear in SKILL.md (176-215) and again in debugging-monitoring.md (138-218).

Why it matters: You're burning tokens on repetition when those tokens could go toward new patterns or deeper examples. It costs you ~2-3 points in Writing S...

AI-Detected Topics

Extracted using NLP analysis

references agents langgraph nodes memory agentic-skill Development Workflow Build Quick Start Build Scenarios

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