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 bugrabilge/bilge-development-kit --skill langchain-architecturegit clone --depth 1 https://github.com/bugrabilge/bilge-development-kitWrote 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/bugrabilge/bilge-development-kit/langchain-architecture)<a href="https://agentmods.dev/skills/bugrabilge/bilge-development-kit/langchain-architecture"><img src="https://agentmods.dev/badge/skills/bugrabilge/bilge-development-kit/langchain-architecture/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/bugrabilge/bilge-development-kit/langchain-architecture"><img src="https://agentmods.dev/badge/skills/bugrabilge/bilge-development-kit/langchain-architecture.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 | $0.00045 | $0.03240 |
| Opus 5.5 | $0.00018 | $0.01296 |
| Sonnet 5.5 | $0.00009 | $0.00648 |
| Haiku 4.5 | $0.00005 | $0.00324 |
Grade A, and why
langchain-architecture 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 — 466 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangChain Architecture
Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.
Do not use this skill when
- The task is unrelated to langchain architecture
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
Use this skill when
- Building autonomous AI agents with tool access
- Implementing complex multi-step LLM workflows
- Managing conversation memory and state
- Integrating LLMs with external data sources and APIs
- Creating modular, reusable LLM application components
- Implementing document processing pipelines
- Building production-grade LLM applications
Core Concepts
1. Agents
Autonomous systems that use LLMs to decide which actions to take.
Agent Types:
- ReAct: Reasoning + Acting in interleaved manner
- OpenAI Functions: Leverages function calling API
- Structured Chat: Handles multi-input tools
- Conversational: Optimized for chat interfaces
- Self-Ask with Search: Decomposes complex queries
2. Chains
Sequences of calls to LLMs or other utilities.
Chain Types:
- LLMChain: Basic prompt + LLM combination
- SequentialChain: Multiple chains in sequence
- RouterChain: Routes inputs to specialized chains
- TransformChain: Data transformations between steps
- MapReduceChain: Parallel processing with aggregation
3. Memory
Systems for maintaining context across interactions.
Memory Types:
- ConversationBufferMemory: Stores all messages
- ConversationSummaryMemory: Summarizes older messages
- ConversationBufferWindowMemory: Keeps last N messages
- EntityMemory: Tracks information about entities
- VectorStoreMemory: Semantic similarity retrieval
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 · 466 lines · 45 tokens per session scan A 3bd5fa500681
langchain-architecture is a skill published in the GitHub repository bugrabilge/bilge-development-kit (10 stars, last pushed 6mo ago), licensed MIT. It adds 45 tokens to every session and 3,240 once invoked, about $0.0002 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 2026-09-03.
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