skills-benchmarks is a test suite that measures how the design of skill documentation affects Claude Code's adherence to recommended coding patterns. It is used to compare documentation approaches across LangChain-related tasks and other agent workflows. Its catalogue entries represent skills, hooks, instructions, and a plugin used in the benchmark project.
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 langchain-ai/skills-benchmarks --skill langgraph-persistencegit clone --depth 1 https://github.com/langchain-ai/skills-benchmarksWrote 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/langchain-ai/skills-benchmarks/langgraph-persistence)<a href="https://agentmods.dev/skills/langchain-ai/skills-benchmarks/langgraph-persistence"><img src="https://agentmods.dev/badge/skills/langchain-ai/skills-benchmarks/langgraph-persistence/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/langchain-ai/skills-benchmarks/langgraph-persistence"><img src="https://agentmods.dev/badge/skills/langchain-ai/skills-benchmarks/langgraph-persistence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 490 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- high Memory Poisoning · line 505 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00055 | $0.04334 |
| Opus 5.5 | $0.00022 | $0.01734 |
| Sonnet 5.5 | $0.00011 | $0.00867 |
| Haiku 4.5 | $0.00006 | $0.00433 |
Grade A, and why
langgraph-persistence 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 1mo ago.
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-persistence — 41 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 — 561 lines — stays where its author put it; the contents beside it link to each section on GitHub.
- Checkpointer: Saves/loads graph state at every super-step
- Thread ID: Identifies separate checkpoint sequences (conversations)
- Store: Cross-thread memory for user preferences, facts
Two memory types:
- Short-term (checkpointer): Thread-scoped conversation history
- Long-term (store): Cross-thread user preferences, facts
| Checkpointer | Use Case | Production Ready |
|---|---|---|
InMemorySaver |
Testing, development | No |
SqliteSaver |
Local development | Partial |
PostgresSaver |
Production | Yes |
Checkpointer Setup
class State(TypedDict): messages: Annotated[list, operator.add]
def add_message(state: State) -> dict: return {"messages": ["Bot response"]}
checkpointer = InMemorySaver()
graph = ( StateGraph(State) .add_node("respond", add_message) .add_edge(START, "respond") .add_edge("respond", END) .compile(checkpointer=checkpointer) # Pass at compile time )
ALWAYS provide thread_id
config = {"configurable": {"thread_id": "conversation-1"}}
result1 = graph.invoke({"messages": ["Hello"]}, config) print(len(result1["messages"])) # 2
result2 = graph.invoke({"messages": ["How are you?"]}, config) print(len(result2["messages"])) # 4 (previous + new)
</python>
<typescript>
Set up a basic graph with in-memory checkpointing and thread-based state persistence.
```typescript
import { MemorySaver, StateGraph, StateSchema, MessagesValue, START, END } from "@langchain/langgraph";
import { HumanMessage } from "@langchain/core/messages";
const State = new StateSchema({ messages: MessagesValue });
const addMessage = async (state: typeof State.State) => {
return { messages: [{ role: "assistant", content: "Bot response" }] };
};
const checkpointer = new MemorySaver();
const graph = new StateGraph(State)
.addNode("respond", addMessage)
.addEdge(START, "respond")
.addEdge("respond", END)
.compile({ checkpointer });
// ALWAYS provide thread_id
const config = { configurable: { thread_id: "conversation-1" } };
const result1 = await graph.invoke({ messages: [new HumanMessage("Hello")] }, config);
console.log(result1.messages.length); // 2
const result2 = await graph.invoke({ messages: [new HumanMessage("How are you?")] }, config);
console.log(result2.messages.length); // 4 (previous + new)
with PostgresSaver.from_conn_string( "postgresql://user:pass@localhost/db" ) as checkpointer: checkpointer.setup() # only needed on first use to create tables graph = builder.compile(checkpointer=checkpointer)
</python>
<typescript>
Configure PostgreSQL-backed checkpointing for production deployments.
```typescript
import { PostgresSaver } from "@langchain/langgraph-checkpoint-postgres";
const checkpointer = PostgresSaver.fromConnString(
"postgresql://user:pass@localhost/db"
);
await checkpointer.setup(); // only needed on first use to create tables
const graph = builder.compile({ checkpointer });
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.
- 1mo ago First seen · 561 lines · 55 tokens per session scan A a3c3fe4339f2
langgraph-persistence is a skill published in the GitHub repository langchain-ai/skills-benchmarks (118 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 4,334 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-persistence, differing in 41 lines, and is treated as a copy.
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