Oryntra: Skill for Claude Code

.agents/skills/langgraph-human-in-the-loop/SKILL.md

langgraph-human-in-the-loop is a skill for Claude Code, Codex from andersonlemesc/Oryntra. It costs 55 tokens per session (3,896 once invoked), scanned A, a copy of langgraph-human-in-the-loop, Apache-2.0.

A guide to pausing a LangGraph workflow for a person’s approval or correction, then continuing it later.

In plain words
What is it for?
Adding approval gates, validation questions, human intervention, and error-handling paths to LangGraph applications.
Why use it?
It explains the saved state and identifiers needed to resume safely, as well as what happens when a paused step starts again. This prevents incomplete or incorrectly resumed workflows.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is andersonlemesc/Oryntra's own configuration. It tells Claude Code and Codex how to work on Oryntra itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Oryntra configures →

Reuse

Borrowing it

Nothing to install: this file belongs to andersonlemesc/Oryntra. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/andersonlemesc/Oryntra/main/.agents/skills/langgraph-human-in-the-loop/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/andersonlemesc/Oryntra

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for langgraph-human-in-the-loop

README.md
[![agentmods](https://agentmods.dev/badge/skills/andersonlemesc/oryntra/langgraph-human-in-the-loop/github.svg)](https://agentmods.dev/skills/andersonlemesc/oryntra/langgraph-human-in-the-loop)
Your own site
<a href="https://agentmods.dev/skills/andersonlemesc/oryntra/langgraph-human-in-the-loop"><img src="https://agentmods.dev/badge/skills/andersonlemesc/oryntra/langgraph-human-in-the-loop/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.

agentmods 80×15 button for langgraph-human-in-the-loop

Your own site · 80×15
<a href="https://agentmods.dev/skills/andersonlemesc/oryntra/langgraph-human-in-the-loop"><img src="https://agentmods.dev/badge/skills/andersonlemesc/oryntra/langgraph-human-in-the-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,896 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00055 $0.03896
Opus 5 $0.00028 $0.01948
Sonnet 5 $0.00011 $0.00779
Haiku 4.5 $0.00006 $0.00390

Measured 9d ago against content hash 3e43a99257a8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

langgraph-human-in-the-loop 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 9d 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.

Origin

This is a copy

100% identical to langgraph-human-in-the-loop — 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.

.agents/skills/langgraph-human-in-the-loop/SKILL.md · 533 lines

How it starts

The opening of the file, as written. The whole thing — 533 lines — stays where its author put it; the contents beside it link to each section on GitHub.

  • interrupt(value) — pauses execution, surfaces a value to the caller
  • Command(resume=value) — resumes execution, providing the value back to interrupt()
  • Checkpointer — required to save state while paused
  • Thread ID — required to identify which paused execution to resume

Requirements

Three things are required for interrupts to work:

  1. Checkpointer — compile with checkpointer=InMemorySaver() (dev) or PostgresSaver (prod)
  2. Thread ID — pass {"configurable": {"thread_id": "..."}} to every invoke/stream call
  3. JSON-serializable payload — the value passed to interrupt() must be JSON-serializable

Basic Interrupt + Resume

interrupt(value) pauses the graph. The value surfaces in the result under __interrupt__. Command(resume=value) resumes — the resume value becomes the return value of interrupt().

Critical: when the graph resumes, the node restarts from the beginning — all code before interrupt() re-runs.

class State(TypedDict): approved: bool

def approval_node(state: State): # Pause and ask for approval approved = interrupt("Do you approve this action?") # When resumed, Command(resume=...) returns that value here return {"approved": approved}

checkpointer = InMemorySaver() graph = ( StateGraph(State) .add_node("approval", approval_node) .add_edge(START, "approval") .add_edge("approval", END) .compile(checkpointer=checkpointer) )

config = {"configurable": {"thread_id": "thread-1"}}

Initial run — hits interrupt and pauses

result = graph.invoke({"approved": False}, config) print(result["interrupt"])

[Interrupt(value='Do you approve this action?')]

Resume with the human's response

result = graph.invoke(Command(resume=True), config) print(result["approved"]) # True

</python>
<typescript>
Pause execution for human review and resume with Command.
```typescript
import { interrupt, Command, MemorySaver, StateGraph, StateSchema, START, END } from "@langchain/langgraph";
import { z } from "zod";

const State = new StateSchema({
  approved: z.boolean().default(false),
});

const approvalNode = async (state: typeof State.State) => {
  // Pause and ask for approval
  const approved = interrupt("Do you approve this action?");
  // When resumed, Command({ resume }) returns that value here
  return { approved };
};

const checkpointer = new MemorySaver();
const graph = new StateGraph(State)
  .addNode("approval", approvalNode)
  .addEdge(START, "approval")
  .addEdge("approval", END)
  .compile({ checkpointer });

const config = { configurable: { thread_id: "thread-1" } };

// Initial run — hits interrupt and pauses
let result = await graph.invoke({ approved: false }, config);
console.log(result.__interrupt__);
// [{ value: 'Do you approve this action?', ... }]

// Resume with the human's response
result = await graph.invoke(new Command({ resume: true }), config);
console.log(result.approved);  // true

Read the full file on GitHub · 533 lines

Changes

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.

  1. 9d ago First seen · 533 lines · 55 tokens per session scan A 3e43a99257a8

Subscribe to this mod's changes

langgraph-human-in-the-loop is a skill published in the GitHub repository andersonlemesc/Oryntra (6 stars, last pushed 6d ago), licensed Apache-2.0. It adds 55 tokens to every session and 3,896 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to langgraph-human-in-the-loop, differing in 0 lines, and is treated as a copy.

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