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 agentmods add skills/ariegoldkin/claude-forge/langgraphnpx skills add ArieGoldkin/claude-forge --skill langgraphgit clone --depth 1 https://github.com/ArieGoldkin/claude-forgeWrote 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/ariegoldkin/claude-forge/langgraph)<a href="https://agentmods.dev/skills/ariegoldkin/claude-forge/langgraph"><img src="https://agentmods.dev/badge/skills/ariegoldkin/claude-forge/langgraph.svg" alt="Measured on agentmods" 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 | $0.00096 | $0.01194 |
| Opus 5 | $0.00048 | $0.00597 |
| Sonnet 5 | $0.00019 | $0.00239 |
| Haiku 4.5 | $0.00010 | $0.00119 |
Grade A, and why
langgraph 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangGraph Patterns
LangGraph is a framework for building stateful, multi-step workflows with LLMs. It models workflows as directed graphs where nodes are functions and edges define control flow. Use it when you need conditional routing, parallel execution, human approval gates, or persistent state across workflow steps.
Quick Reference
| Pattern | Description | Reference |
|---|---|---|
| State Management | TypedDict/Pydantic schemas, reducers, MessagesState | references/state-management.md |
| Conditional Routing | Branching, retry loops, quality gates, END fallbacks | references/routing.md |
| Parallel Execution | Fan-out/fan-in, Send API, map-reduce, error isolation | references/parallel-execution.md |
| Human-in-the-Loop | Interrupt points, approval gates, feedback loops, API integration | references/human-in-loop.md |
| Checkpoints | MemorySaver, PostgresSaver, Store, crash recovery, debugging | references/checkpoints.md |
| Supervisor | Coordinator agents, round-robin/priority dispatch, LLM-based routing | references/supervisor.md |
Pattern Summaries
State Management
Design workflow state with TypedDict (lightweight, internal) or Pydantic (validated, boundaries).
Use Annotated[list, add] for accumulating results across nodes and MessagesState for chat workflows.
See references/state-management.md.
Conditional Routing
Route execution dynamically with add_conditional_edges. Patterns include quality gates
(pass/fail/retry), retry loops with max attempts, and state-based routers. Always include an
END fallback. See references/routing.md.
What ships with it
7 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.
- 3d ago First seen · 98 lines · 96 tokens per session scan A c6082fbc3c4f
langgraph is a skill published in the GitHub repository ArieGoldkin/claude-forge (5 stars, last pushed 25d ago), licensed MIT. It adds 96 tokens to every session and 1,194 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
dataset-transformation
Generates code that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline…
dataset-evaluation
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model…
use-case-specification
Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has…
model-selection
Selects a base model for the user's use case by querying SageMaker Hub. Use when the user asks which model to use, wants to select or change their base model, mentions a model name or family (e.g., "Llama", "Mistral", "Nova"), or wants to evaluate a base model — always activate even for known model names because the…
finetuning-technique
Selects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes. Use when the user has decided to finetune and needs to choose a technique, or when the technique needs to be validated against a model. Requires a base model to already be…
finetuning
Generates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function and RLAIF custom…