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Embodiment Description

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wanshuiyin
embodiment-description

Write detailed embodiment descriptions for patent specifications. Use when user says "撰写实施例", "write embodiment", "实施例描述", "detailed description", or wants to describe how to practice an invention.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill nameembodiment-description
Stars
16.3K
Forks
1.4K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by wanshuiyin on GitHub. Read the source before you install it.

Installation

Install the Embodiment Description AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep
mkdir -p .claude/skills
cp -r /tmp/Auto-claude-code-research-in-sleep/skills/embodiment-description .claude/skills/embodiment-description
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Embodiment Description in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Embodiment Description on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Embodiment Description is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Embodiment Description

Write detailed embodiments for: $ARGUMENTS

Embodiments describe HOW to make and use the invention -- they are the patent equivalent of experiment sections, but describe the invention rather than evaluating it empirically.

Constants

  • MIN_EMBODIMENTS = 1 — At least one complete embodiment required
  • MAX_EMBODIMENTS = 3 — Practical limit; more embodiments strengthen enablement
  • EMBODIMENT_STYLE = detaileddetailed (full working example) or outline (sketch)
  • REFERENCE_NUMERAL_PREFIX = 100 — Starting reference numeral for first figure's components

Inputs

  1. patent/INVENTION_DISCLOSURE.md — invention decomposition (core/supporting/optional features)
  2. patent/CLAIMS.md — drafted claims that the embodiments must support
  3. User-provided figures (if any) in any directory
  4. patent/figures/numeral_index.md if it exists (from /figure-description)

Workflow

Step 1: Plan Embodiments

For each claim category (method, system, etc.), plan at least one embodiment:

EmbodimentCovers ClaimsTypeKey Variations
1Claims 1, XBest mode / preferred[primary implementation]
2Claims 2, 3Alternative[different parameters/materials]
3Claims 4, 5Additional alternative[different configuration]

Step 2: Write Each Embodiment

For each embodiment, write a detailed description following this structure:

Opening paragraph: "In one embodiment, [invention summary with reference to what is being described]."

Component/step-by-step description:

For method embodiments:

  • Describe each step in order
  • Reference figure numerals: "As shown in FIG. 1, at step 202, the processor 102 receives the input data 104..."
  • Include specific parameters, ranges, and conditions
  • Describe what happens at each decision point

For system/apparatus embodiments:

  • Describe each component
  • Reference figure numerals: "Referring to FIG. 1, the system 100 comprises a processor 102, a memory 104, and a communication interface 106..."
  • Describe interconnections between components
  • Describe operation of the system step-by-step

Variations and alternatives:

  • "In some embodiments, the processor 102 may be a GPU, an FPGA, or an ASIC."
  • "In another embodiment, the memory 104 may be replaced with a distributed storage system."
  • "The parameters described above are exemplary; other values within the range [X, Y] are also contemplated."

These variations are critical -- they support broader claim interpretation.

Step 3: Reference Numeral Integration

Ensure consistent reference numeral usage:

  1. Every component mentioned must have a numeral
  2. Numeral must appear first in parentheses after the component name: "the processor (102)"
  3. Subsequent references: "the processor 102" (no parentheses)
  4. Numbering follows figure series: 100-series for FIG. 1, 200-series for FIG. 2

Format:

  • First mention: "the processor (102)"
  • Later in same embodiment: "the processor 102"
  • Cross-figure: "the processor 102 (shown in both FIG. 1 and FIG. 2)"

Step 4: Claim Support Verification

For each claim element, verify it appears in at least one embodiment:

Claim ElementEmbodimentReference NumeralDescription Paragraph
[element][which][numeral][paragraph reference]

If any claim element lacks embodiment support, add the necessary description.

Step 5: Software/Algorithm Embodiments (if applicable)

For method/software inventions, include:

  • Pseudocode or algorithmic description (NOT actual code)
  • Flowchart description tied to figures
  • Data structure descriptions
  • Interface specifications

Example:

In one embodiment, the method comprises the following steps:
At step 202, the processor 102 receives input data from the input device 108.
At step 204, the processor 102 extracts feature vectors from the input data using a convolutional neural network.
At step 206, the processor 102 applies the attention mechanism 110 to the feature vectors...

Step 6: Output

Embodiment sections are written to patent/specification/detailed_description.md (or appended to the specification structure).

Each embodiment section should be self-contained but cross-reference other embodiments when describing alternatives.

Key Rules

  • Embodiments must teach a POSITA to make and use the invention without undue experimentation.
  • Include at least one "best mode" embodiment (US requirement).
  • Multiple embodiments strengthen the specification against enablement challenges.
  • Describe the invention, do NOT evaluate it empirically ("The embodiment achieves 95% accuracy" is wrong; "The processor classifies the input data" is correct).
  • CRITICAL — NO experimental data, test results, accuracy percentages, detection rates, precision values, or comparative performance data. These belong in papers, not patents. The embodiment teaches HOW to make and use, not HOW WELL it performs.
  • WRONG: "传感器对直径超过150μm的金属颗粒实现了100%的检测精度,即使在检测限处仍保持94%的高精度。"
  • RIGHT: "当不锈钢颗粒通过间隙传感区域时,谐振频率下降。颗粒直径越大,频率偏移幅度越大。"
  • Do NOT include tables of experimental results, graphs of measurement data, or comparisons with prior art performance.
  • CRITICAL — An embodiment is NOT an experiment. Do NOT describe "repeated experiments", "accuracy evaluation", "precision testing", "calibration experiments", or "comparison with reference methods". An embodiment describes ONE way to make and use the invention — it is a recipe, not a test report.
  • Do NOT copy experimental sections from source papers verbatim. Transform the experimental setup into a manufacturing/operation description.
  • If the source material is a paper, extract ONLY: (1) what was built, (2) what materials/parameters were used, (3) how it operates. Ignore all test methodology, results, and performance metrics.
  • Include specific parameters where possible, but frame them as exemplary, not limiting.
  • Reference numerals must be consistent with the figures.
  • Do NOT use subjective language ("excellent", "surprising", "superior").

Frequently asked questions

What does the Embodiment Description AI skill do?

Write detailed embodiment descriptions for patent specifications. Use when user says "撰写实施例", "write embodiment", "实施例描述", "detailed description", or wants to describe how to practice an invention.

Why use Embodiment Description on TypingMind?

Because you install it once and use it with any model. Embodiment Description is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Embodiment Description in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/embodiment-description. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Embodiment Description?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Embodiment Description?

As many as you like. As long as a model supports skills, you can use Embodiment Description with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Embodiment Description AI skill free?

Yes. It is published on GitHub by wanshuiyin under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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