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/langchain-ai/deepagents/langgraph-docsnpx skills add langchain-ai/deepagents --skill langgraph-docsgit clone --depth 1 https://github.com/langchain-ai/deepagentsWhat 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 | $0.00062 | $0.00246 |
| Opus 5 | $0.00031 | $0.00123 |
| Sonnet 5 | $0.00012 | $0.00049 |
| Haiku 4.5 | $0.00006 | $0.00025 |
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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- langgraph-docs — 100% identical, 0 lines differ
What it actually says
langgraph-docs
Workflow
1. Fetch the Documentation Index
Use fetch_url to read: https://docs.langchain.com/llms.txt
This returns a structured list of all available documentation with descriptions.
2. Select Relevant Documentation
Identify 2-4 most relevant URLs from the index. Prioritize:
- Implementation questions — specific how-to guides
- Conceptual questions — core concept pages
- End-to-end examples — tutorials
- API details — reference docs
3. Fetch and Apply
Use fetch_url on the selected URLs, then complete the user's request using the documentation content.
If fetch_url fails or returns empty content, retry once. If it fails again, inform the user and suggest checking https://langchain-ai.github.io/langgraph/ directly.
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 · 29 lines · 62 tokens per session scan A 7c120c1b4031
langgraph-docs is a skill published in the GitHub repository langchain-ai/deepagents (28,825 stars, last pushed yesterday), licensed MIT. It adds 62 tokens to every session and 246 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
code-reviewer
当用户要求进行代码审查、Code Review、查找 Bug、安全风险、性能问题或代码质量问题时使用。.
emoji-translator
当用户明确要求把自然语言翻译成 Emoji、把 Emoji 解释成文字，或要求“表情翻译/emoji 翻译”时使用。.
defense-evasion
Endpoint defense bypass — AMSI/ETW patching, ScareCrow framework, custom loaders, direct/indirect syscalls, LOLBAS execution, process injection.
opsec
Operational security management — traffic shaping, scan rate limiting, source IP management, tool signature avoidance, evidence handling, anti-detection patterns.
benchmark
Benchmark mode marker — engagement objective is flag capture. Generic engagement rules apply unchanged.
seven-question-gate
7-question gate run before promoting a finding to FINDING + opening a report. Kills weak/non-impactful findings before they reach the report stage and damage validity ratio.