Alchemy

io.github.fernforge由 GitHub 账号 fernforge 发布v0.1.1更新于 Nov 10, 2026

Serves the Alchemy writing ruleset to any agent, so its prose loses the usual LLM tells.

发布方自己的服务:Alchemy MCP Server

已验证STDIO仅桌面开发者工具AI 与机器学习

概览

AI 生成的概览

提供一套写作规则,让 AI 助手在撰写文档、提交信息等文本时避免常见的 LLM 腔调。

可以做什么
Alchemy 提供 get_writing_rules 工具和 alchemy://rules 资源,返回一份简短规则集(约 1000 token),列出应避免的 LLM 写作痕迹:空洞对比、堆砌形容词、破折号滥用、填充词和开场套话。助手可在写散文前先读取规则,使生成的 README、提交信息和文档更平实。同一套规则也以文件形式分发,可从助手指令文件中引用。
适用场景
当助手经常在你的项目中撰写文字,而你希望其输出避免可识别的 LLM 措辞时,值得添加。若你希望跨项目按需提供规则,而不是在每个仓库中提交规则文件,也很适用。
运行要求
以 npm 包 @fernforge/alchemy-mcp 通过 stdio 在本地运行,需要 Node.js 和 npx。未声明账户、API 密钥或环境变量。
安装前请注意
该服务器只返回静态规则集,不读取或修改你的文件。安装意味着在本机运行第三方 npm 包,使用前请先审查该包。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Alchemy,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

[Alchemy — write like a human, not an LLM]

Alchemy

Alchemy is a drop-in ruleset that stops your AI coding agent from writing like an LLM: no em-dash pile-ups, no "not just X, it's Y", no stray delve. You add it once, your agent reads it before it writes, and the READMEs, commits, and docs it produces start sounding like a person wrote them.

Turn lead into gold. It's one file, about 1000 tokens, that lives next to your agent's other instructions: ALCHEMY.md. Everything else here just gets it into your project.

How do I stop my AI from writing like an LLM?

Install it in your project and point your agent at it. Pick whichever fits your stack:

bash
# Node / npmnpx @fernforge/alchemy init
# Python / pippip install alchemy-writingalchemy init

init writes ALCHEMY.md into your project and links it from whatever agent config you already have: CLAUDE.md, AGENTS.md, .cursorrules, or Copilot instructions. From then on the agent reads the rules as part of its normal context. To print the ruleset to stdout without installing anything:

bash
npx @fernforge/alchemy print   # or: alchemy print

Prefer to do it by hand? Copy ALCHEMY.md into your repo and reference it from your agent's instruction file. That's the whole mechanism. No service, no API, no account.

Before and after

Here's the kind of sentence Alchemy catches. Same fact, two ways of saying it:

Before

Redis isn't just a database — it's a powerful, robust caching layer that unlocks the full potential of your entire stack.

After

Redis is an in-memory store. Put your cache and sessions in it and your database stops being the bottleneck.

The "before" stacks four tells in one sentence: the "isn't just X, it's Y" non-contrast, the adjective pile (powerful, robust), the lone dramatic em-dash, and "unlocks the full potential". None of it says what Redis does. The "after" drops all of it and states one concrete thing you can act on. That's the whole move, applied everywhere the agent writes.

Which LLM tells does it remove?

The concrete ones, by name:

  • Empty contrasts: "not just X, it's Y", "it's not about A, it's about B".
  • Staccato trios and rule-of-three padding: "fast, reliable, scalable", "No X. No Y. Just Z."
  • Stacked adjectives standing in for facts: robust, seamless, powerful, comprehensive.
  • Em-dash pile-ups. The real tell is density, so the rule is: use them rarely.
  • Filler vocabulary: delve, leverage, harness, unlock, elevate, tapestry, realm, showcase.
  • Throat-clearing openers ("In today's fast-paced world", "At its core") and recap closers ("In conclusion", "Ultimately").
  • Restating the prompt before answering it.

The fix is never a synonym swap. It's replacing the vague word with a specific fact: not "robust" but "handles 10k req/sec". The rules push the agent toward the fact.

The principle underneath: no single word proves a machine wrote something. The signal is many of these clustered together over flat, evenly-weighted text. Alchemy targets the clustering, so it thins the tells without stripping a word that's genuinely the right one.

Use it with Claude, Cursor, or Copilot

init detects the config files already in your project and links the ruleset from them, so the same ALCHEMY.md works across agents:

  • Claude Code / Claude reads it via CLAUDE.md.
  • Cursor reads it via .cursorrules.
  • Copilot reads it via its instructions file.
  • Any agent that supports AGENTS.md picks it up there.

One ruleset, whichever agent you're driving.

Use it as an MCP server

To serve the rules on demand across every project instead of committing a file to each repo, run Alchemy as an MCP server. Add it to your client config (Claude Desktop, Cursor, Cline):

json
{  "mcpServers": {    "alchemy": { "command": "npx", "args": ["-y", "@fernforge/alchemy-mcp"] }  }}

It exposes a get_writing_rules tool and an alchemy://rules resource, so any MCP-aware agent can pull the ruleset when it's about to write prose. See mcp/ for details.

Three ways to install, one ruleset

All three ship the same rules from the same source, so pick by your toolchain and mix freely:

  • npm: @fernforge/alchemy — npx @fernforge/alchemy init
  • PyPI: alchemy-writing — pip install alchemy-writing
  • MCP: @fernforge/alchemy-mcp — npx @fernforge/alchemy-mcp

Where the rules come from

They're grounded in what people actually flag as AI writing, not guesses: Wikipedia's "Signs of AI writing", the Kobak et al. study measuring which words spiked in research papers after ChatGPT, Pangram's phrase-frequency data, and the long-running arguments on Reddit and in r/Professors about spotting it. It's kept short on purpose: concrete guidance an agent can hold in context, not an essay.

This README follows its own rules. If it reads fine, that's the pitch.

Contributing

Found a tell it misses? Open an issue or a PR with a real before-and-after pair. Concrete examples are worth more than new abstract rules.

Edit the root ALCHEMY.md only. The copies under python/ and mcp/ are generated from it by node scripts/sync-rules.mjs, and CI fails if they drift. The npm, PyPI, and MCP packages publish from GitHub Releases (see .github/workflows).

License

MIT

来源:README.md,提交 83e5eec

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.1.1最新Sep 16, 2026

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其他 Alchemy 集成