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.
npx skills add joao-aires/agent-skills-public --skill langgraph-fundamentalsgit clone --depth 1 https://github.com/joao-aires/agent-skills-publicWrote 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/skills/joao-aires/agent-skills-public/langgraph-fundamentals)<a href="https://agentmods.dev/skills/joao-aires/agent-skills-public/langgraph-fundamentals"><img src="https://agentmods.dev/badge/skills/joao-aires/agent-skills-public/langgraph-fundamentals/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/joao-aires/agent-skills-public/langgraph-fundamentals"><img src="https://agentmods.dev/badge/skills/joao-aires/agent-skills-public/langgraph-fundamentals.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00043 | $0.01340 |
| Opus 5.5 | $0.00017 | $0.00536 |
| Sonnet 5.5 | $0.00009 | $0.00268 |
| Haiku 4.5 | $0.00004 | $0.00134 |
Grade A, and why
langgraph-fundamentals 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 yesterday.
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.
This is a copy
100% identical to langgraph-fundamentals — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
- StateGraph: Main class for building stateful graphs
- Nodes: Functions that perform work and update state
- Edges: Define execution order (static or conditional)
- START/END: Special nodes marking entry and exit points
- State with Reducers: Control how state updates are merged
Graphs must be compile()d before execution.
Designing a LangGraph application
Follow these 5 steps when building a new graph:
- Map out discrete steps — sketch a flowchart of your workflow. Each step becomes a node.
- Identify what each step does — categorize nodes: LLM step, data step, action step, or user input step. For each, determine static context (prompt), dynamic context (from state), retry strategy, and desired outcome.
- Design your state — state is shared memory for all nodes. Store raw data, format prompts on-demand inside nodes.
- Build your nodes — implement each step as a function that takes state and returns partial updates.
- Wire it together — connect nodes with edges, add conditional routing, compile with a checkpointer if needed.
| Use LangGraph When | Use Alternatives When |
|---|---|
| Need fine-grained control over agent orchestration | Quick prototyping → LangChain agents |
| Building complex workflows with branching/loops | Simple stateless workflows → LangChain direct |
| Require human-in-the-loop, persistence | Batteries-included features → Deep Agents |
State Management
| Need | Solution | Example |
|---|---|---|
| Overwrite value | No reducer (default) | Simple fields like counters |
| Append to list | Reducer (operator.add / concat) | Message history, logs |
| Custom logic | Custom reducer function | Complex merging |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 165 lines · 43 tokens per session scan A 193bea142c9d
langgraph-fundamentals is a skill published in the GitHub repository joao-aires/agent-skills-public (2 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 1,340 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to langgraph-fundamentals, differing in 0 lines, and is treated as a copy.
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