skills-benchmarks is a test suite that measures how the design of skill documentation affects Claude Code's adherence to recommended coding patterns. It is used to compare documentation approaches across LangChain-related tasks and other agent workflows. Its catalogue entries represent skills, hooks, instructions, and a plugin used in the benchmark project.
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 langchain-ai/skills-benchmarks --skill framework-selectiongit clone --depth 1 https://github.com/langchain-ai/skills-benchmarksWrote 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/langchain-ai/skills-benchmarks/framework-selection)<a href="https://agentmods.dev/skills/langchain-ai/skills-benchmarks/framework-selection"><img src="https://agentmods.dev/badge/skills/langchain-ai/skills-benchmarks/framework-selection/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/langchain-ai/skills-benchmarks/framework-selection"><img src="https://agentmods.dev/badge/skills/langchain-ai/skills-benchmarks/framework-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00061 | $0.01634 |
| Opus 5.5 | $0.00024 | $0.00654 |
| Sonnet 5.5 | $0.00012 | $0.00327 |
| Haiku 4.5 | $0.00006 | $0.00163 |
Grade A, and why
framework-selection 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- framework-selection — 92% identical, 4 lines differ
- framework-selection — 92% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
┌─────────────────────────────────────────┐
│ Deep Agents │ ← highest level: batteries included
│ (planning, memory, skills, files) │
├─────────────────────────────────────────┤
│ LangGraph │ ← orchestration: graphs, loops, state
│ (nodes, edges, state, persistence) │
├─────────────────────────────────────────┤
│ LangChain │ ← foundation: models, tools, chains
│ (models, tools, prompts, RAG) │
└─────────────────────────────────────────┘
Picking a higher layer does not cut you off from lower layers — you can use LangGraph graphs inside Deep Agents, and LangChain primitives inside both.
This skill should be loaded at the top of any project before selecting other skills or writing agent code. The framework you choose dictates which other skills to invoke next.
Decision Guide
Answer these questions in order:
| Question | Yes → | No → |
|---|---|---|
| Does the task require breaking work into sub-tasks, managing files across a long session, persistent memory, or loading on-demand skills? | Deep Agents | ↓ |
| Does the task require complex control flow — loops, dynamic branching, parallel workers, human-in-the-loop, or custom state? | LangGraph | ↓ |
| Is this a single-purpose agent that takes input, runs tools, and returns a result? | LangChain (create_agent) |
↓ |
| Is this a pure model call, retrieval pipeline, or simple prompt chain with no agent loop? | LangChain (direct model / chain) | — |
Framework Profiles
LangChain — Use when the task is focused and self-contained
Best for:
- Single-purpose agents that use a fixed set of tools
- RAG pipelines and document Q&A
- Model calls, prompt templates, output parsing
- Quick prototypes where agent logic is simple
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 · 164 lines · 61 tokens per session scan A f7203463dd6d
framework-selection is a skill published in the GitHub repository langchain-ai/skills-benchmarks (118 stars, last pushed 1mo ago), licensed MIT. It adds 61 tokens to every session and 1,634 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 2026-08-30.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
component-fixtures
Use when creating or updating component fixtures for screenshot testing or their shared infrastructure, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, validation, and common pitfalls.