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/hyunjunjeon/deepagent-research-context-engineering/langgraph-docsnpx skills add HyunjunJeon/Deepagent-research-context-engineering --skill langgraph-docsgit clone --depth 1 https://github.com/HyunjunJeon/Deepagent-research-context-engineeringWrote 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/hyunjunjeon/deepagent-research-context-engineering/langgraph-docs)<a href="https://agentmods.dev/skills/hyunjunjeon/deepagent-research-context-engineering/langgraph-docs"><img src="https://agentmods.dev/badge/skills/hyunjunjeon/deepagent-research-context-engineering/langgraph-docs.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 | $0.00029 | $0.00209 |
| Opus 5 | $0.00015 | $0.00105 |
| Sonnet 5 | $0.00006 | $0.00042 |
| Haiku 4.5 | $0.00003 | $0.00021 |
Grade A, and why
langgraph-docs 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 4d 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-docs — 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.
What it actually says
langgraph-docs
Overview
This skill explains how to access LangGraph Python documentation to help answer questions and guide implementation.
Instructions
1. Fetch the Documentation Index
Use the fetch_url tool to read the following URL: https://docs.langchain.com/llms.txt
This provides a structured list of all available documentation with descriptions.
2. Select Relevant Documentation
Based on the question, identify 2-4 most relevant documentation URLs from the index. Prioritize:
- Specific how-to guides for implementation questions
- Core concept pages for understanding questions
- Tutorials for end-to-end examples
- Reference docs for API details
3. Fetch Selected Documentation
Use the fetch_url tool to read the selected documentation URLs.
4. Provide Accurate Guidance
After reading the documentation, complete the users request.
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.
- 4d ago First seen · 36 lines · 29 tokens per session scan A f700b5ce6a2d
langgraph-docs is a skill published in the GitHub repository HyunjunJeon/Deepagent-research-context-engineering (53 stars, last pushed 7mo ago), licensed MIT. It adds 29 tokens to every session and 209 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to langgraph-docs, differing in 0 lines, and is treated as a copy.
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