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 ecosystem-primergit 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/ecosystem-primer)<a href="https://agentmods.dev/skills/langchain-ai/skills-benchmarks/ecosystem-primer"><img src="https://agentmods.dev/badge/skills/langchain-ai/skills-benchmarks/ecosystem-primer/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/ecosystem-primer"><img src="https://agentmods.dev/badge/skills/langchain-ai/skills-benchmarks/ecosystem-primer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 159 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
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 | $0.00071 | $0.01748 |
| Opus 5 | $0.00036 | $0.00874 |
| Sonnet 5 | $0.00014 | $0.00350 |
| Haiku 4.5 | $0.00007 | $0.00175 |
Grade A, and why
ecosystem-primer 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 11d 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
- Deep Agents (top layer, harness) — batteries-included toolkit built on LangChain + LangGraph. Ships with planning, file management, subagent spawning, and memory out of the box.
- LangGraph (middle layer, runtime) — low-level orchestration for durable execution, custom control flow, and stateful workflows. LangChain agents run on top of LangGraph.
- LangChain (bottom layer, framework) — abstractions for models, tools, and the agent loop. Provider-agnostic, easiest to start with.
- LangSmith (cross-cutting) — observability and evaluation platform. Framework-agnostic; always recommended alongside any of the above.
Higher layers depend on lower ones, but you don't need to use lower layers directly. Deep Agents gives you LangGraph's durable execution without writing graph code. LangChain gives you models and tools without managing graph edges.
Step 1 — Choose Your Tool
Evaluate these conditions in order and stop at the first match:
- If the task needs planning, file management across a long session, persistent memory, subagent delegation, or on-demand skills → Deep Agents
- Else, if the task needs custom control flow (deterministic loops, branching logic) → LangGraph
- Else, if it's a single-purpose agent with a fixed set of tools → LangChain (
create_agentfunction) - Else, if it's a pure model call, retrieval pipeline, or simple prompt chain with no agent loop → LangChain (direct model / chain)
This is your layer. BUT you are not done: later in Step 4, you MUST load the layer-specific skill before writing any agent code.
Tool Profiles
LangChain — agent framework
Best for:
- Single-purpose agents with a fixed tool set
- RAG pipelines and document Q&A
- Model calls, prompt templates, structured output
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.
- 11d ago First seen · 192 lines · 71 tokens per session scan A 51d6ea5520c7
ecosystem-primer is a skill published in the GitHub repository langchain-ai/skills-benchmarks (116 stars, last pushed 22d ago), licensed MIT. It adds 71 tokens to every session and 1,748 once invoked, about $0.0004 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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