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 G1Joshi/Agent-Skills --skill langchaingit clone --depth 1 https://github.com/G1Joshi/Agent-SkillsWrote 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/g1joshi/agent-skills/langchain)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/langchain"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/langchain/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/g1joshi/agent-skills/langchain"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/langchain.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00021 | $0.00309 |
| Opus 5 | $0.00010 | $0.00154 |
| Sonnet 5 | $0.00004 | $0.00062 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
langchain 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.
What it actually says
LangChain
LangChain is the standard framework for chaining LLM components. In 2025, the focus shifted to LangGraph for building stateful, cyclic agents.
When to Use
- Orchestration: Chaining "Prompt -> LLM -> Parser".
- Agents: Using LangGraph to build agents that can loop, retry, and keep state.
- Integrations: 1000+ connectors for vector DBs, APIs, and tools.
Core Concepts
LangGraph
The successor to AgentExecutor. A graph-based way to define agent flows with cycles (loops).
LCEL (LangChain Expression Language)
The declarative pipe syntax: prompt | llm | output_parser.
LangSmith
Observability platform to trace and debug complex chains.
Best Practices (2025)
Do:
- Use LangGraph: For any non-trivial agent.
AgentExecutoris legacy. - Use LCEL: It enables streaming and async out of the box.
- Trace everything: Connect to LangSmith to see why your agent failed.
Don't:
- Don't over-abstract: If a simple Python function works, don't wrap it in a Chain.
References
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.
- 9d ago First seen · 45 lines · 21 tokens per session scan A 3b59bb71aac0
langchain is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 21 tokens to every session and 309 once invoked, about $0.0001 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.
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