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 agentsope/SkillAlchemy --skill agentsop-langgraphgit clone --depth 1 https://github.com/agentsope/SkillAlchemyWrote 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/agentsope/skillalchemy/agentsop-langgraph)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-langgraph"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-langgraph/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/agentsope/skillalchemy/agentsop-langgraph"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-langgraph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00124 | $0.07819 |
| Opus 5.5 | $0.00050 | $0.03128 |
| Sonnet 5.5 | $0.00025 | $0.01564 |
| Haiku 4.5 | $0.00012 | $0.00782 |
Grade A, and why
agentsop-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 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.
How it starts
The opening of the file, as written. The whole thing — 566 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangGraph · SOP
Source posture: every non-trivial claim is cited inline. Citations use short tags like
[lc-docs],[lc-blog/interrupt],[gh/6731],[zenml/uber]— resolve them againstreferences/*.mdfor the full URL.
何时激活 (Activation Rules)
Activate this skill when any of the following triggers fire:
- The task mentions LangGraph,
StateGraph,MessageGraph,create_react_agent,interrupt(,Command(resume=,add_messages,checkpointer,PostgresSaver,Send(, orentrypoint/taskdecorators. - The user wants to build a stateful agent (memory across turns, long-running,
must survive a process crash) — LangGraph's stated sweet spot
[lc-docs/why-langgraph]. - The user wants human-in-the-loop (approve a tool call, edit state, multi-turn
validation) — LangGraph offers a first-class
interrupt()primitive that competitors require "duct-taping" to achieve[bswen/hitl]. - The user is hitting
GRAPH_RECURSION_LIMITerrors, infinite loops, orInvalidUpdateErroron parallel branches — these are LangGraph-specific failure modes with known fixes[lc-docs/errors][cheatsheet/gotchas]. - The user is choosing between LangGraph and CrewAI / AutoGen / OpenAI Swarm / raw LangChain — section 生态对照 gives the decision matrix.
- The user is migrating an existing LangChain chain or a hand-rolled while-loop agent to something durable and observable.
Do not activate if the task is a single LLM call, a one-shot RAG query, or
a stateless tool pipeline — Sec. 反模式 explains why graphs are overkill there.
核心心智模型 (Core Mental Model)
LangGraph is a state machine, not a chain. The cleanest one-liner from the
2026 docs: "If chains were about passing outputs between steps, graphs are about
maintaining and evolving a shared state over time" [eastondev/2026]. Pre-LLM
analog: think BPMN / finite state machine / Pregel-style "supersteps", not a
Unix pipe. The official position is even more reductive: LangGraph is "a
deterministic execution engine for AI reasoning workflows" [eastondev/2026].
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.
- 1mo ago First seen · 566 lines · 124 tokens per session scan A 9785e4a9d630
agentsop-langgraph is a skill published in the GitHub repository agentsope/SkillAlchemy (445 stars, last pushed 14d ago), licensed MIT. It adds 124 tokens to every session and 7,819 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-08-30.
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