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Datasheet

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1Lethe
datasheet

Parse, analyze, and answer questions about IC datasheets (analog ICs, microcontrollers, sensors, etc.) and circuit schematics in PDF format. Use this skill whenever the user provides a datasheet PDF or schematic and asks about electrical characteristics, timing parameters, pinout, register maps, firmware interfaces, component connections, or any technical detail from the document. Also triggers when the user wants to understand a circuit schematic — identifying components, their values, connections, and signal flow. Use even for simple questions like "what's the supply voltage" because the skill handles intelligent page targeting to find the answer efficiently.

Overview

Publisher1Lethe
Repositorydatasheet_reader_skill
Skill namedatasheet
Stars
64
Forks
6
Bundled files
7
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.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by 1Lethe on GitHub. Read the source before you install it.

Installation

Install the Datasheet 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.
    https://github.com/1Lethe/datasheet_reader_skill/tree/master/skills/datasheet_reader
  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/1Lethe/datasheet_reader_skill.git /tmp/datasheet_reader_skill
mkdir -p .claude/skills
cp -r /tmp/datasheet_reader_skill/skills/datasheet_reader .claude/skills/datasheet
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Datasheet 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 Datasheet 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 Datasheet 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.

IC Datasheet & Schematic Reader

Parse and understand IC datasheets and circuit schematics to answer technical questions accurately.

Overview

This skill processes PDF documents containing IC datasheets or circuit schematics using a two-path approach:

  • Short documents (≤15 pages): Export all pages as images and use multimodal vision to analyze them directly. Ideal for schematics and brief spec sheets.
  • Long documents (>15 pages): Parse the table of contents first, locate relevant sections based on the user's question, then extract text and use multimodal vision only for pages with diagrams/figures.

Interface Specification (工具接口)

When this skill is called by another agent, workflow, or tool chain, use the following contract.

Input

@datasheet_reader(
  action:   "query" | "summarize" | "init" | "init_all" | "list",
  pdf:      "<path>",              // PDF 文件路径(query/summarize/init 必填)
  question: "<string>",            // 问题或总结范围(query 必填,summarize 可选)
  pages:    "<range>",             // 可选,限定页码范围如 "5-10"
  format:   "markdown" | "json"    // 输出格式,默认 "markdown"
)

Actions

action必填参数说明
querypdf, question针对具体问题从 PDF 中提取信息并回答
summarizepdf总结文档或指定章节(question 可选,用于限定范围)
initpdf为指定 PDF 生成/覆写 _context.md 缓存,并更新 datasheet_index.md 导航文件
init_all—批量初始化工作目录下所有 PDF,并重写 datasheet_index.md 导航文件
list—列出所有已初始化的文档及其主要内容摘要(读取 datasheet_index.md)

Output

format = "markdown"(默认):

直接返回带引用标注的文本:

STM32H750 的 VDD 范围为 1.62~3.6V
[来源: ST-STM32H750VB_2.pdf, 6.3.1 General operating conditions, p.96, Table 22]

format = "json":

json
{
  "status": "ok",
  "action": "query",
  "source_pdf": "ST-STM32H750VB_2.pdf",
  "answer": "STM32H750 的 VDD 范围为 1.62~3.6V",
  "citations": [
    {
      "text": "VDD 供电范围 1.62~3.6V",
      "pdf": "ST-STM32H750VB_2.pdf",
      "section": "6.3.1 General operating conditions",
      "page": 96,
      "detail": "Table 22 第1行"
    }
  ],
  "metadata": {
    "total_pages": 337,
    "sections_consulted": ["6.3.1 General operating conditions"],
    "pages_extracted": [96],
    "multimodal_pages": []
  }
}

错误时:

json
{"status": "error", "message": "File not found: ..."}

Calling Examples

// 精确问答
@datasheet_reader(action:"query", pdf:"datasheet/ST-STM32H750VB_2.pdf", question:"ADC最大采样率")

// 章节总结
@datasheet_reader(action:"summarize", pdf:"datasheet/UART_spec.pdf", question:"寄存器")

// 指定页码范围查询
@datasheet_reader(action:"query", pdf:"datasheet/rm0433.pdf", question:"USART波特率寄存器", pages:"1850-1860")

// JSON 输出(供下游解析)
@datasheet_reader(action:"query", pdf:"datasheet/ST-STM32H750VB_2.pdf", question:"封装尺寸", format:"json")

// 初始化
@datasheet_reader(action:"init", pdf:"datasheet/new_chip.pdf")

// 列出可用文档
@datasheet_reader(action:"list")

Prerequisites

Before first use, set up the Python environment by running:

python skills/datasheet_reader/scripts/setup_env.py

This creates a .venv/ with PyMuPDF installed. All subsequent script calls use this venv's Python.

The venv Python path on Windows: .venv\Scripts\python.exe

Workflow

Step 0: Context Cache — Check or Initialize

Before any PDF processing, check if a cached context file exists:

  1. Determine the context file path: <pdf_directory>/<pdf_stem>_context.md

    • Example: datasheet/ST-STM32H750VB_2.pdf → datasheet/ST-STM32H750VB_2_context.md
  2. If _context.md exists → Read it with the Read tool. The file starts with the PDF name and "主要内容" summary, followed by the full TOC. Use this directly — skip PDF TOC parsing entirely.

  3. If _context.md does NOT exist → Generate it:

    .venv\Scripts\python.exe skills/datasheet_reader/scripts/generate_context.py "<pdf_path>" --force

    This outputs JSON with the TOC structure and first 2-3 pages of text. Then:

    • Read the JSON output (TOC + first pages text)
    • Using your understanding of the content, identify:
      • IC model number(s) and variants
      • Document type (datasheet / reference manual / programming manual / app note / schematic)
      • Key content areas covered (electrical specs, peripherals, firmware interfaces, etc.)
      • Key peripherals or features listed
    • Write the _context.md file using this template:
    markdown
    # <PDF filename>
    
    ## 主要内容
    
    - **IC型号**: <model numbers and variants>
    - **类型**: <document type>
    - **文档覆盖内容**: <comma-separated list of major topics>
    - **关键外设/特性**: <key peripherals or features>
    - **总页数**: <N>
    
    ## 目录
    
    ### <level 1 title> (p.<page>)
    #### <level 2 title> (p.<page>)
    ...

    TOC depth rule: Always include Level 1 and Level 2 entries in the TOC section, regardless of document size. This provides sufficient granularity for precise section targeting.

  4. "初始化" command: When the user says "初始化 " or "初始化这个数据手册":

    • Run generate_context.py with --force on the specified PDF
    • Regenerate the _context.md (overwrite existing)
    • Update datasheet_index.md (see Step 0.6)
  5. "全部初始化" command: When the user says "全部初始化":

    • Run generate_context.py --batch <datasheet_directory> --force
    • Process each PDF in the batch output, generate/overwrite all _context.md files
    • Rewrite datasheet_index.md (see Step 0.6)

Step 0.5: Cross-Document Pre-Analysis(跨文档预分析)

When the user asks about a specific IC or topic (whether or not a specific PDF is given):

MANDATORY RULE — Index-First Routing

You MUST read datasheet_index.md before reading any _context.md file. Never skip the index and jump directly to context files. The index provides fast document-level routing; the context file provides the detailed TOC for page-level targeting. Violating this order (e.g., globbing _context.md files directly when an index exists) is not permitted.

MANDATORY RULE — Exhaustive Document Routing

When producing the Document Routing Plan, you MUST evaluate every PDF entry in the index for relevance. No entry may be skipped. For the following question types, you MUST check whether multiple documents are needed:

  • Questions about "how to configure/use" → require both Reference Manual (registers) + HAL/LL Driver Manual (API)
  • Questions about "electrical characteristics/pins" → require both Datasheet + Reference Manual
  • Questions about "programming model" → require both Programming Manual + Reference Manual
  • Questions about schematic connections → require both Schematic + Datasheet (pin mux)

If the question clearly involves only a single document (e.g., "What is the UART register address?"), selecting one PDF is acceptable, but the routing plan MUST state the exclusion rationale for every omitted document.

Phase A: Read Index
  1. Read datasheet_index.md (look in project root first, then in datasheet subdirectories)
Phase B: Analyze Relevance for Every PDF Entry
  1. Evaluate each PDF entry in the index against the user's question:
    • For each PDF, check the IC/Subject, Document Type, Coverage, and Key Features/Peripherals fields
    • Decompose the user's question into multiple dimensions/sub-questions (e.g., "how to configure DMA" involves: hardware functional description, register-level details, HAL API usage, pin assignment, etc.)
    • For each dimension, determine which PDF(s) can provide the relevant information
Phase C: Output Document Routing Plan(文档路由计划)
  1. Produce a Document Routing Plan (internal working document to guide subsequent extraction; not displayed directly to the user):
## Document Routing Plan

Question: <user's original question>
Dimension decomposition:
  1. <sub-question/dimension 1> (e.g., hardware functional description)
  2. <sub-question/dimension 2> (e.g., HAL driver API configuration)
  3. ...

| PDF filename | Relevance | Matched dimension | Content to consult | Priority |
|--------------|-----------|-------------------|-------------------|----------|
| rm0433...    | High      | Dimension 1       | DMA register-level description, functional overview | 1 |
| um2217...    | High      | Dimension 2       | HAL DMA/MDMA API interface | 2 |
| ST-STM32H750VB_2... | Low | — | Overview only, not essential | — |
| UART_spec... | None      | —                 | Unrelated to question | — |

Conclusion: N documents required (list filenames)
Exclusion rationale: <brief explanation for each PDF marked "Low" or "None">
Phase D: Read Context Files and Locate Sections
  1. For all "High relevance" PDFs, read their _context.md (via the Context File path in the index) to obtain the full TOC
  2. Locate specific chapters and page ranges from each PDF's TOC, then consolidate into a unified extraction plan:
## Extraction Plan

### PDF 1: rm0433...
- 14.1-14.3 MDMA controller (p.518-524)
- 15.1-15.3 DMA1/DMA2 (p.543-561)
- 16.1-16.3 BDMA (p.575-580)

### PDF 2: um2217...
- 23.1-23.2 HAL DMA Generic Driver (p.338-345)
- 52.1-52.2 HAL MDMA Generic Driver (p.1038-1049)
  1. Proceed to Step 1+ normal extraction workflow, processing each PDF according to the extraction plan
Fallback

Fallback (ONLY if datasheet_index.md does not exist): Glob all *_context.md files and read the header section of each. If this fallback is triggered, recommend the user run init_all to generate the index.

Step 0.6: Navigation Index — datasheet_index.md

A centralized index that aggregates paths and main content summaries of all initialized PDFs for fast document targeting.

Location Rules
  • Scan all directories in the project that contain PDF files
  • If all PDFs reside in a single directory → place the index inside that directory (e.g., datasheet/datasheet_index.md)
  • If PDFs are spread across multiple directories (hierarchical layout) → place the index at the project root (datasheet_index.md)
File Format
markdown
# Datasheet Index

> Auto-generated navigation file. Updated on initialization.

## <directory_path>/

### <PDF_filename>
- **IC/Subject**: <model>
- **Document Type**: <type>
- **Coverage**: <topics>
- **Key Features/Peripherals**: <features>
- **Total Pages**: <N>
- **Context File**: <relative path to _context.md>

### <next_PDF_filename>
- ...

## <another_directory>/

### ...
Updating the Index on Single Init
  1. Read the existing datasheet_index.md (if present)
  2. Search for the PDF's entry by matching ### <filename>:
    • Found → overwrite that entry (from ### <filename> up to the next ### or ##)
    • Not found → append the entry under the matching directory group (## <dir>/); create the group if it doesn't exist
  3. If datasheet_index.md itself does not exist → create the file with the header and the new entry
Rewriting the Index on Batch Init
  1. Iterate over all generated _context.md files
  2. Extract the "Main Content" section from each (everything before ## 目录 / ## Table of Contents)
  3. Group entries by directory and write the entire datasheet_index.md from scratch

Step 1: Identify Input and Assess Document

When the user provides a PDF path or asks about a datasheet/schematic:

  1. If a _context.md cache exists (from Step 0), use its TOC and page count directly. Otherwise, run the TOC parser:

    .venv\Scripts\python.exe skills/datasheet_reader/scripts/parse_toc.py "<pdf_path>" --fallback-scan
  2. Based on total_pages, choose the processing path:

    • ≤15 pages → Go to Short Document Path (Step 2A)
    • >15 pages → Go to Long Document Path (Step 2B)

Step 2A: Short Document Path (Schematics & Brief Specs)

For short documents like circuit schematics, application notes, or brief datasheets:

  1. Export all pages as images:

    .venv\Scripts\python.exe skills/datasheet_reader/scripts/export_page_images.py "<pdf_path>" "all" --output-dir "<temp_dir>" --dpi 200
  2. Use the Read tool to view each exported PNG image. The model's vision capability will interpret:

    • Component symbols and their reference designators (U1, R1, C3, etc.)
    • Component values (resistance, capacitance, IC part numbers)
    • Net connections and signal routing
    • Pin assignments and labels
  3. Synthesize the visual information to answer the user's question directly.

What you can answer in this mode:

  • Which IC corresponds to which reference designator
  • Component types and values
  • How components are connected (signal flow, power rails, etc.)
  • Pin-to-pin connections between ICs
  • Power supply topology

Step 2B: Long Document Path (Datasheets)

For long datasheets (microcontrollers, complex ICs, etc.):

Phase 1: Locate Relevant Sections

Using the TOC from Step 1, determine which sections are relevant to the user's question.

Question-to-section mapping heuristics:

  • Electrical characteristics / absolute max ratings → "Electrical", "DC Characteristics", "Absolute Maximum"
  • Timing / clock → "Timing", "Clock", "AC Characteristics"
  • Pinout / package → "Pin", "Package", "Ball Map"
  • Communication (UART/SPI/I2C) → "UART", "SPI", "I2C", "Serial", "Communication"
  • Memory / flash / registers → "Memory", "Flash", "Register Map"
  • Power / supply voltage → "Power", "Supply", "Electrical"
  • ADC/DAC → "ADC", "DAC", "Analog"
  • GPIO → "GPIO", "I/O", "Port"
  • Firmware / boot → "Boot", "Firmware", "System"

If the question is ambiguous or broad, present the TOC to the user and ask which section(s) they want to explore.

Phase 2: Extract Content

Once target pages are identified, extract text:

.venv\Scripts\python.exe skills/datasheet_reader/scripts/extract_pages.py "<pdf_path>" "<page_range>" --detect-images

This returns text content plus a list of pages that need multimodal analysis (pages with diagrams, timing charts, block diagrams, etc.).

Phase 3: Extract Tables (if relevant)

If the question involves specifications, parameters, or register definitions:

.venv\Scripts\python.exe skills/datasheet_reader/scripts/extract_tables.py "<pdf_path>" "<page_range>"
Phase 4: Multimodal Analysis for Figures

For pages flagged as needs_multimodal (timing diagrams, block diagrams, pin diagrams, etc.):

.venv\Scripts\python.exe skills/datasheet_reader/scripts/export_page_images.py "<pdf_path>" "<page_list>" --output-dir "<temp_dir>"

Then use the Read tool to view these images and extract information that text alone cannot convey.

Phase 5: Synthesize Answer

Combine text, tables, and visual information to provide a complete answer.

Step 3: Interaction Mode (Hybrid)

Auto-answer when:

  • The question is specific and maps clearly to a section (e.g., "What's the max UART baud rate?")
  • The TOC has an obvious matching section
  • The extracted content directly answers the question

Ask the user when:

  • The question is broad (e.g., "Tell me about this chip")
  • Multiple sections could be relevant
  • The TOC doesn't clearly indicate where to look
  • The document has no bookmarks and no detectable TOC structure

When asking, present the document structure concisely:

This is a [page_count]-page datasheet for [component]. Here's the structure:
1. Overview (p.1-5)
2. Pin Configuration (p.6-12)
3. Electrical Characteristics (p.13-25)
...
Which section would you like me to focus on?

Script Reference

All scripts are in skills/datasheet_reader/scripts/ and output JSON to stdout.

ScriptPurposeKey Args
setup_env.pyCreate venv, install deps--check-only
generate_context.pyExtract TOC + first pages for context cache<pdf> [--force] [--batch <dir>]
parse_toc.pyExtract PDF outline/TOC<pdf> [--fallback-scan]
extract_pages.pyGet text from page range<pdf> <range> [--detect-images]
export_page_images.pyRender pages as PNG<pdf> <pages> [--output-dir] [--dpi]
extract_tables.pyExtract tables as markdown<pdf> <range>

Page range format: "5-10", "1,3,5-7", or "all"

Output Format & Citation Rules

Every piece of information you extract or present must carry a source citation so the user can verify it against the original PDF. This is non-negotiable — traceability is critical for hardware engineering work where a wrong value can damage circuits.

Citation Format

Use inline citation markers after each information point:

[来源: <PDF文件名>, <章节号> <章节名>, p.<页码>]

Extended citation for specific content types:

Content TypeCitation SuffixExample
List item第N点[来源: UART_spec.pdf, 5.1 Initialization, p.17, 第3点]
Table cellTable N 第M行[来源: ST-STM32H750VB_2.pdf, 6.2 Absolute maximum ratings, p.94, Table 19 第2行]
Figure/diagramFigure N "<描述部分>"[来源: ST-STM32H750VB_2.pdf, 6.1.6 Power supply scheme, p.93, Figure 11 "VDD domain"部分]
Formula公式N or description[来源: UART_spec.pdf, 5.1 Initialization, p.18, 波特率公式]
Schematic (no chapters)page only[来源: board_v2.pdf, p.3]
Register bit fieldRegister <name> bit <N>[来源: UART_spec.pdf, 4.5 Line Control Register, p.12, LCR bit 7]

Output Mode 1: Q&A (Default)

When answering a specific question, attach citations to each key fact:

STM32H750VB 的 VDD 供电范围为 1.7V ~ 3.6V
[来源: ST-STM32H750VB_2.pdf, 6.3.1 General operating conditions, p.96, Table 22 第1行]

复位后 UART 默认配置为 8 位数据、无校验、1 停止位
[来源: UART_spec.pdf, 5.1 Initialization, p.17, 第4点]

Group related facts together but ensure each distinct claim has its own citation. If multiple facts come from the same source location, they can share one citation at the end of the group.

Output Mode 2: Summary (总结模式)

When the user asks to "summarize" a document, chapter, or section, output a structured markdown document:

markdown
# <PDF文件名> — <总结范围> 总结

## 概述
<该部分的功能定位和核心用途>
[来源: <PDF>, <章节>, p.<页码>]

## 详细内容

### <子章节标题1>
<详细解释,每个关键点附带引用>
[来源: ...]

### <子章节标题2>
...

## 关键参数表
| 参数 | 值 | 单位 | 条件 | 来源 |
|------|-----|------|------|------|
| VDD | 1.7~3.6 | V | - | [来源: ..., p.96, Table 22] |

## 相关图表
- Figure 11: Power supply scheme — 展示了各电源域的层级关系和推荐去耦电容值 [来源: ..., p.93, Figure 11]
- Figure 12: Current consumption measurement [来源: ..., p.94, Figure 12]

Output Mode 3: Pipeline (工具链模式)

When this skill is invoked as part of a larger tool pipeline (another agent or workflow calls it):

Detection: If the calling context specifies an output schema, JSON format requirement, or structured field names, switch to pipeline mode.

Output structure:

json
{
  "query": "<the question asked>",
  "source_pdf": "<filename>",
  "answer": "<the synthesized answer text>",
  "citations": [
    {
      "text": "<the specific claim>",
      "pdf": "<filename>",
      "section": "<chapter number and name>",
      "page": <page_number>,
      "detail": "<table/figure/list item specifics>"
    }
  ],
  "metadata": {
    "total_pages": 337,
    "sections_consulted": ["6.3.1 General operating conditions"],
    "pages_extracted": [96, 97],
    "multimodal_pages": []
  }
}

If no specific format is requested by the caller, use the default markdown + citation format (Mode 1 or Mode 2 depending on the task).

How to Track Citations During Extraction

While processing content from scripts, maintain a mental mapping:

  1. The parse_toc.py output gives you chapter numbers and names for each page range
  2. The extract_pages.py output includes the page number for each text block
  3. The extract_tables.py output includes page number and table index
  4. For multimodal pages, note the page number and any figure/table labels visible in the image

When synthesizing your answer, cross-reference each fact with the page it came from, then look up which TOC entry covers that page to get the chapter number and name.

Important Notes

  • Keep extracted page ranges focused. For a 1000-page datasheet, extract 10-20 pages at a time, not hundreds.
  • When text extraction gives garbled results (common with scanned PDFs), fall back to image export + multimodal for those pages.
  • Tables in datasheets often contain critical specs. Prefer table extraction over raw text for parameter values.
  • For timing diagrams and waveforms, image mode is essential — text extraction cannot capture these.
  • Clean up temp_images directory after answering to avoid accumulating files.
  • If the venv doesn't exist yet, run setup_env.py first before any other script.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Datasheet AI skill do?

Parse, analyze, and answer questions about IC datasheets (analog ICs, microcontrollers, sensors, etc.) and circuit schematics in PDF format. Use this skill whenever the user provides a datasheet PDF or schematic and asks about electrical characteristics, timing parameters, pinout, register maps, firmware interfaces, component connections, or any technical detail from the document. Also triggers when the user wants to understand a circuit schematic — identifying components, their values, connections, and signal flow. Use even for simple questions like "what's the supply voltage" because the...

Why use Datasheet on TypingMind?

Because you install it once and use it with any model. Datasheet 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 Datasheet in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/1Lethe/datasheet_reader_skill/tree/master/skills/datasheet_reader. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Datasheet?

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 Datasheet?

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

Is the Datasheet AI skill free?

It is published on GitHub by 1Lethe. Check the repository for licensing terms. 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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