A workflow that queries a local AnythingLLM knowledge base and also searches the internet when answering questions. AnythingLLM is a tool for asking questions about documents stored in a searchable collection.

Use it for questions that may require both internal documents and online sources, or combine it with a deeper multi-step research workflow.

It combines private documents with current web information and can fall back to web search when the local collection has no useful result.

anythingllm — Skill for Claude Code and Codex by id5463

Install

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.

Any agent

npx skills add id5463/mcp-server-research --skill anythingllm

Clone the repo

git clone --depth 1 https://github.com/id5463/mcp-server-research

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/id5463/mcp-server-research/anythingllm/github.svg)](https://agentmods.dev/skills/id5463/mcp-server-research/anythingllm)

Your own site

<a href="https://agentmods.dev/skills/id5463/mcp-server-research/anythingllm"><img src="https://agentmods.dev/badge/skills/id5463/mcp-server-research/anythingllm/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/id5463/mcp-server-research/anythingllm"><img src="https://agentmods.dev/badge/skills/id5463/mcp-server-research/anythingllm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 780 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00026	$0.00780
Opus 5	$0.00013	$0.00390
Sonnet 5	$0.00005	$0.00156
Haiku 4.5	$0.00003	$0.00078

Measured 8d ago against content hash ae022a404df6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

anythingllm scanned grade A with 1 finding 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 8d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (ddg_search_mcp_server.py, search.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -X POST http://localhost:8899/api/v1/workspace/aba71416-334d-45de-b789-19c1035c3060/chat \

skill/research/anythingllm/SKILL.md · 84 lines

What it actually says

AnythingLLM — 自动知识库 + 联网搜索技能

任何时候用户提问，自动执行：

查询本地 AnythingLLM 知识库（如有相关文档）
同时联网搜索（使用 search.py 或 ddg-search 技能）
合并结果回答

前置条件

AnythingLLM 服务运行在 http://localhost:8899
API Key 已配置（通过环境变量或 AnythingLLM 管理界面获取）
Python ddgs 包已安装（用于联网搜索）

API 端点

方法	路径	说明
GET	`/api/v1/workspaces`	列出工作区
POST	`/api/v1/workspace/:slug/chat`	问答（slug: `aba71416-334d-45de-b789-19c1035c3060`）
GET	`/api/v1/workspace/:slug/documents`	文档列表

查询知识库

curl -X POST http://localhost:8899/api/v1/workspace/aba71416-334d-45de-b789-19c1035c3060/chat \
  -H "Authorization: Bearer C6DN6MH-WRKMYGT-N8C5H00-NYC7KVE" \
  -H "Content-Type: application/json" \
  -d '{"message":"你的问题","mode":"query"}'

联网搜索

python "C:\Users\a\.agents\skills\research\anythingllm\search.py" "搜索关键词" [结果数量]

行为规则

用户提问时自动激活，无需手动加载技能
本地 KB 无结果时自动回退到纯联网搜索
两个数据源的结果合并输出
如需深度多步研究，推荐使用 deepsearch 技能

整合使用

与 deepsearch 配合

当需要深度研究时，AnythingLLM 作为本地知识库数据源为 deepsearch 提供内部文档支持：

deepsearch 进行多轮网络搜索
同时通过 AnythingLLM API 查询本地知识库
合并网络结果与本地知识，生成更完善的报告

与 ddg-search 配合

联网搜索也可通过 ddg-search 技能执行：

# 方式 1：使用 search.py
python "C:\Users\a\.agents\skills\research\anythingllm\search.py" "关键词"

# 方式 2：使用 ddgs Python 包
python -c "from ddgs import DDGS; ..."

注意事项

API Key 安全：不要在对话或脚本中硬编码 API Key
确保 AnythingLLM 服务在后台运行
不同工作区（workspace）包含不同文档集，选择正确的工作区 slug
查询模式 mode=query 为单次问答（无历史上下文），mode=chat 保留对话历史

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

8d ago First seen · 84 lines · 26 tokens per session scan A ae022a404df6

Subscribe to this mod's changes

anythingllm is a skill published in the GitHub repository id5463/mcp-server-research (0 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 780 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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