A CLI toolkit for bulk-processing and generating Anki flashcards with LLMs.

• Bulk-verify translations – End-to-end pipeline for cleaning large decks. Read more
• Add a Key Vocabulary field – Create a per-note field highlighting 1–3 key words with readings, meanings, and HTML context. Read more
• Generate new cards – Interactively create multiple contextual flashcards for a vocabulary word or concept from a single command. Read more
• Scriptable collection access – Query AnkiConnect directly from the CLI or AI agents. Command reference

Manually editing hundreds or thousands of Anki cards is tedious, error-prone, and time-consuming. Whether it's verifying translations, adding grammar notes, or generating contextual examples, doing it by hand doesn't scale.

anki-llm provides a bridge between your Anki collection and modern AI models.

Batch processing

• File-based: Export deck to file, process with LLM, import results back to Anki.
• Direct: Process and update notes in-place.

Card generation

Generate multiple contextual flashcard examples for a term, review interactively, and add selected cards to your deck.

• Batch processing workflows: File-based (with resume) or direct-to-Anki (one command).
• Export Anki decks to clean CSV or YAML files.
• Batch process note fields using OpenAI or Google Gemini models.
• Custom prompts: Use flexible template files to define exactly how the LLM should process your cards.
• Concurrent processing: Make multiple parallel API requests to speed up large jobs.
• Resilient: Automatically retries failed requests and saves progress incrementally (file mode).
• Automatic resume: Pick up where you left off if processing is interrupted (file mode).
• Copy mode: Alternatively, generate cards without API keys by pasting LLM responses from browser interfaces (ChatGPT, Claude, etc.).

Install globally via npm:

• Node.js (v18 or higher)
• Anki Desktop must be running.
• The AnkiConnect add-on must be installed in Anki.

anki-llm uses LLM APIs to process your notes. You need to configure an API key for the model provider you want to use.

The tool supports two API providers:

Pricing is per million tokens (M). Check the latest prices on the provider's website to be sure.

Set the appropriate environment variable for your chosen model provider:

For OpenAI models:

export OPENAI_API_KEY="your-api-key-here"

Get your API key from: https://platform.openai.com/api-keys

For Gemini models:

export GEMINI_API_KEY="your-api-key-here"

Get your API key from: https://aistudio.google.com/api-keys

Use anki-llm config to store defaults (for example, the model) so you don't have to repeat flags on every command.

# Set or override defaults
anki-llm config set model gpt-4o-mini

Config file lives at ~/.config/anki-llm/config.json.

Exports notes from an Anki deck.

• <deck>: The name of the Anki deck to export (must be in quotes if it contains spaces).
• [output]: Optional output file path. If omitted, automatically generates a filename from the deck name (e.g., "My Deck" → my-deck.yaml). You can also provide just a file extension (e.g., .csv) to auto-generate the filename with your preferred format.

Examples:

# Auto-generate filename with default .yaml format
anki-llm export "Japanese Core 1k"
# → japanese-core-1k.yaml

# Auto-generate filename with .csv format
anki-llm export "Japanese Core 1k" .csv
# → japanese-core-1k.csv

# Specify custom filename
anki-llm export "Japanese Core 1k" my-custom-name.yaml

Imports data from a file into an Anki deck. Existing notes (matched by key field) are updated, while new entries create new notes.

• <input>: Path to the data file to import (CSV or YAML).

Required options:

• -d, --deck: The name of the target Anki deck.

Common options:

• -n, --note-type: The Anki note type to use when creating new notes. If not specified, it will be inferred from existing notes in the deck.
• -k, --key-field: Field to use for identifying existing notes. If not specified, auto-detects using this priority: (1) noteId column if present, (2) first field of the note type, (3) error if neither found.

Batch-process notes from a CSV/YAML file using an LLM and user-defined prompts. This command saves the transformed results to an output file and features automatic resume, allowing it to safely skip completed notes if interrupted or re-run.

• <input>: Input file path (CSV or YAML).

Required options:

• -o, --output: Output file path (CSV or YAML).
• -p, --prompt: Path to the prompt template text file.
• Either --field or --json (mutually exclusive):

Common options:

• -m, --model: AI model to use (required unless set via config set model).
• -b, --batch-size: Number of concurrent API requests (default: 5).
• -r, --retries: Number of retries for failed requests (default: 3).
• -d, --dry-run: Preview the operation without making API calls (recommended for testing).
• -f, --force: Re-process all rows, ignoring existing output.
• --limit: Limit the number of new rows to process (useful for testing prompts on a small sample before processing large datasets).
• --require-result-tag: Only extracts content from within <result></result> tags in the AI response.
• --log: Generate a log file with detailed debug information.
• --very-verbose: Log full LLM responses to the log file (automatically enables --log). Useful for debugging prompts and understanding model outputs.

Workflow:

1. Export deck to file: anki-llm export "My Deck" notes.yaml
2. Process file: anki-llm process-file notes.yaml -o output.yaml --field Translation -p prompt.txt -m gpt-4o-mini
3. Import results: anki-llm import output.yaml -d "My Deck"

Examples:

# Process a file and update a single field
anki-llm process-file notes.yaml -o output.yaml --field Translation -p prompt.txt -m gpt-4o-mini

# Process with JSON mode (update multiple fields)
anki-llm process-file notes.yaml -o output.yaml --json -p prompt.txt -m gpt-4o-mini

# Test on 10 notes first (dry run)
anki-llm process-file notes.yaml -o output.yaml --field Translation -p prompt.txt --limit 10 --dry-run -m gpt-4o-mini

# Resume processing after interruption (automatic - just re-run the same command)
anki-llm process-file notes.yaml -o output.yaml --field Translation -p prompt.txt -m gpt-4o-mini

# Force re-process all notes (ignore existing output)
anki-llm process-file notes.yaml -o output.yaml --field Translation -p prompt.txt --force -m gpt-4o-mini

Key features:

• ✅ Automatic resume: Skips already-processed notes
• ✅ Incremental saves: Progress saved continuously
• ✅ Review before import: You can inspect/edit the output file before importing

When to use this command:

This command provides a file-based workflow for batch processing notes. It is the primary alternative to the process-deck command, which modifies notes directly in your Anki collection.

Use process-file instead of process-deck when you:

• Require a manual review step. The command outputs to a file, creating a safe staging area to inspect results before you commit them to your Anki deck.
• Need to process a large number of notes where interruptions are possible. Its resume capability ensures you don't lose progress if the process fails midway.
• Are operating in an environment without a running Anki instance. This command is fully self-contained and does not need to connect to the Anki application.

Batch-process notes directly from an Anki deck using an LLM and user-defined prompts, updating them in-place. No intermediate files needed. This is faster and more convenient when you've tested your prompt and know the end result is safe to run.

• <deck>: Name of the Anki deck to process (must be in quotes if it contains spaces).

Required options:

• -p, --prompt: Path to the prompt template text file.
• Either --field or --json (mutually exclusive):

Common options:

• -m, --model: AI model to use (required unless set via config set model).
• -b, --batch-size: Number of concurrent API requests (default: 5).
• -r, --retries: Number of retries for failed requests (default: 3).
• -d, --dry-run: Preview the operation without making API calls (recommended for testing).
• --limit: Limit the number of notes to process (useful for testing prompts on a small sample before processing entire deck).
• --require-result-tag: Only extracts content from within <result></result> tags in the AI response.
• --log: Generate a log file with detailed debug information.
• --very-verbose: Log full LLM responses to the log file (automatically enables --log). Useful for debugging prompts and understanding model outputs.

Prerequisites:

• Anki Desktop must be running
• AnkiConnect add-on must be installed

Workflow:

• Single command: anki-llm process-deck "My Deck" --field Translation -p prompt.txt -m gpt-4o-mini

Examples:

# Process a deck directly and update a single field
anki-llm process-deck "Japanese Core 1k" --field Translation -p prompt.txt

# Direct mode with JSON (update multiple fields)
anki-llm process-deck "Vocabulary" --json -p prompt.txt

# Test on 10 notes first (recommended before processing entire deck)
anki-llm process-deck "My Deck" --field Notes -p prompt.txt --limit 10 --dry-run

# Use a different model for a specific run
anki-llm process-deck "Spanish" --field Translation -p prompt.txt

Key features:

• ✅ No intermediate files: Process and update in one step
• ✅ Batch updates: Efficient bulk updates to Anki
• ✅ Error logging: Failed notes logged to [deck-name]-errors.jsonl
• ❌ No resume support: Must complete in one run (use process-file for large datasets)

Both process-file and process-deck support two response formats for the LLM:

• --field mode (single field update): The LLM response is saved to the specified field.

  anki-llm process-file notes.yaml -o out.yaml -p prompt.txt --field Translation

• --json mode (multi-field merge): The LLM must return valid JSON. All fields in the JSON are merged into your note.

  anki-llm process-file notes.yaml -o out.yaml -p prompt.txt --json

  Example: If your note has Japanese and Grammar fields, and the LLM returns:

  {
    "Japanese": "こんにちは",
    "Grammar": "greeting"
  }

  Both fields will be updated. Only fields present in the JSON are updated (partial updates are allowed). If the response is not valid JSON, the operation will fail and retry.

Interactively creates a prompt template file for the generate command. The wizard guides you through selecting a deck and note type, then uses an LLM to analyze your existing cards and generate a tailored prompt that matches your deck's style and formatting. This is the recommended way to get started with card generation.

• [output]: Optional output file path. If omitted, automatically generates a filename from the deck name.

Common options:

• -m, --model: The LLM model to use for the smart prompt generation step (e.g., gemini-2.5-pro).
• -t, --temperature: Temperature for LLM generation (0.0-2.0, default varies by model). Lower values produce more consistent output.
• --copy: Copy the LLM prompt to clipboard and wait for manual response pasting. Useful when you don't have API access and want to use a browser LLM interface like ChatGPT.

Workflow:

1. Run the wizard: anki-llm generate-init
2. Follow the interactive steps to select a deck and note type.
3. A prompt file (e.g., my-deck-prompt.md) is created for you.
4. Review and customize the generated prompt file.
5. Use the file with the generate command: anki-llm generate "term" -p my-deck-prompt.md

Generates multiple new Anki card examples for a given term, lets you review and select which ones to keep, and adds them directly to your deck.

• <term>: The word or phrase to generate cards for (must be in quotes if it contains spaces).

Required options:

• -p, --prompt: Path to the prompt template file (created with generate-init).

Common options:

• -c, --count: Number of card examples to generate (default: 3).
• -m, --model: AI model to use (defaults to gpt-5-mini or gemini-2.5-flash depending on your API key; can also be set via config set model).
• -d, --dry-run: Display generated cards without starting the interactive selection or import process.
• -r, --retries: Number of retries for failed requests (default: 3).
• -t, --temperature: LLM temperature, a value between 0 and 2 that controls creativity (default: 1.0).
• --max-tokens: Set a maximum number of tokens for the LLM response.
• -o, --output: Export cards to a file instead of importing to Anki (e.g., cards.yaml, cards.csv).
• --log: Enable logging of LLM responses to a file (useful for debugging).
• --copy: Copy the LLM prompt to clipboard and wait for manual response pasting. Useful when you don't have API access and want to use a browser LLM interface like ChatGPT.

The --prompt file is a text or markdown file that contains two parts: YAML frontmatter for configuration and a prompt body with instructions for the LLM.

Frontmatter (Required)

The frontmatter is a YAML block at the top of the file enclosed by ---.

• deck: The target Anki deck name.
• noteType: The name of the Anki note type (model) to use.
• fieldMap: Maps the keys from the LLM's JSON output to your actual Anki field names. The LLM will be instructed to generate JSON with the keys on the left, and anki-llm will use them to populate the Anki fields on the right.

Prompt Body

The body contains your instructions for the LLM. It must:

1. Include the {term} placeholder, which will be replaced by the <term> you provide on the command line.
2. Include the {count} placeholder, which will be replaced by the number of cards requested.
3. Instruct the LLM to return a JSON array of objects, where each object represents one card and uses the keys defined in fieldMap.
4. Include a "one-shot" example showing the exact JSON array structure and desired formatting (e.g., HTML for bolding or lists).
5. Encourage the LLM to generate diverse cards that highlight different nuances, contexts, or usage examples of the term.

Example Prompt File (japanese-vocab-prompt.md)

---
deck: Japanese::Vocabulary
noteType: Japanese (recognition)
fieldMap:
  en: English
  jp: Japanese
  context: Context
---

You are an expert assistant who creates {count} distinct Anki flashcards for a
Japanese vocabulary word. The term to create cards for is: **{term}**

IMPORTANT: Your output must be a single, valid JSON array of objects and nothing
else. Each object in the array should represent a unique flashcard. All field
values must be strings.

Follow the structure shown in this example precisely:

```json
[
  {
    "en": "How was your day?",
    "jp": "今日はどうでしたか？",
    "context": "A natural and common way to ask about someone's day politely. You can say 「今日どうだった？」 in casual speech."
  }
]
```

Return only a valid JSON array matching this structure. Ensure you generate
{count} varied and high-quality cards that highlight different nuances, contexts,
or usage examples of the term.

The --copy flag allows you to generate cards without API keys by manually copying prompts to a browser-based LLM interface (like ChatGPT, Claude, Gemini, etc.) and pasting responses back.

Workflow:

1. Run the command with --copy:

   anki-llm generate "今日" -p prompt.md --copy

2. The program automatically copies the LLM prompt to your clipboard.
3. Paste the prompt into your preferred LLM interface (ChatGPT, Claude, etc.).
4. Copy the complete JSON response from the LLM.
5. Paste it into the terminal.
6. Type END on a new line and press Enter to submit.
7. The program validates and processes your cards normally.

Benefits:

• No API key required
• Use any LLM interface you prefer
• Works with free-tier LLM services
• Full control over the LLM interaction

Examples:

# Generate 3 cards for a term using a prompt file
anki-llm generate "新しい" -p japanese-vocab-prompt.md

# Generate 5 cards and preview them without importing
anki-llm generate "ambiguous" -p english-vocab-prompt.md --count 5 --dry-run

# Use a different model for a specific run
anki-llm generate "maison" -p french-prompt.md -m gemini-2.5-pro

# Generate cards and export to YAML for later review/import
anki-llm generate "今日" -p japanese-vocab-prompt.md -o cards.yaml

# Import the exported cards when ready
anki-llm import cards.yaml --deck "Japanese::Vocabulary"

# Enable logging for debugging
anki-llm generate "新しい" -p prompt.md --log

# Use manual copy-paste mode (no API key required)
anki-llm generate "今日" -p japanese-vocab-prompt.md --copy

Key features:

• ✅ Interactive selection: Review and choose which generated cards to keep.
• ✅ Duplicate detection: Automatically flags cards that may already exist in your deck.
• ✅ Export option: Save generated cards to YAML/CSV for review before importing.
• ✅ Highly customizable: Full control over card generation via the prompt file.

Query the AnkiConnect API directly with any supported action. This command is especially useful for AI agents (like Claude Code) to explore and interact with your Anki collection programmatically.

• <action>: The AnkiConnect API action to perform (e.g., deckNames, findNotes, cardsInfo).
• [params]: Optional JSON string of parameters for the action.

Why this is useful for AI agents:

AI assistants can use this command to dynamically query your Anki collection without you having to manually provide information. For example:

• "List all my decks" → anki-llm query deckNames
• "Show me statistics for my Japanese deck" → anki-llm query getDeckStats '{"decks":["Japanese"]}'
• "Find all cards with tag 'vocabulary'" → anki-llm query findNotes '{"query":"tag:vocabulary"}'

The command outputs clean JSON that AI agents can parse and reason about, making it easy to build custom workflows or answer questions about your Anki collection.

Examples:

# Get all deck names
anki-llm query deckNames

# Get all model (note type) names
anki-llm query modelNames

# Find notes in a specific deck
anki-llm query findNotes '{"query":"deck:Japanese"}'

# Get detailed information about specific cards
anki-llm query cardsInfo '{"cards":[1498938915662]}'

# Get statistics for a deck
anki-llm query getDeckStats '{"decks":["Default"]}'

# Check AnkiConnect version
anki-llm query version

# Get full AnkiConnect API documentation (useful for AI agents to understand available actions)
anki-llm query docs

Example: Sampling random cards from decks

AI agents can use anki-llm query to discover information about your collection and then take action. Here's an example of Claude Code using the query command to sample random cards from multiple decks. Given the instruction: "Use anki-llm to pick random cards from Glossika decks, and print the English and Japanese fields for each, pick 10 cards from each deck, and save to a markdown file"

Full conversation

This demonstrates how the query command enables AI agents to build custom scripts for data analysis and extraction tasks autonomously.

Special actions:

• docs or help: Returns the complete AnkiConnect API documentation. This is especially useful for AI agents that need to understand what actions are available and how to use them. The agent can query this once to get the full documentation and then use that context to make informed decisions about which API calls to make.

See ANKI_CONNECT.md for the complete list of available actions and their parameters.

Let's say you have an Anki deck named "Japanese Core 1k" with 1000 notes. Each note has a Japanese field with a sentence and a Translation field with an English translation that you suspect is inaccurate. We'll use anki-llm and GPT-4o mini to generate better translations for all 1000 notes.

First, export the notes from your Anki deck into a YAML file. YAML is great for multiline text fields and for using git diff to see what has changed after processing is complete.

anki-llm export "Japanese Core 1k" notes.yaml

This command will connect to Anki, find all notes in that deck, and save them to a YAML file.

============================================================
Exporting deck: Japanese Core 1k
============================================================

✓ Found 1000 notes in 'Japanese Core 1k'.

Discovering model type and fields...
✓ Model type: Japanese Model
✓ Fields: Japanese, Translation, Reading, Sound, noteId

Fetching note details...
✓ Retrieved information for 1000 notes.

Writing to notes.yaml...
✓ Successfully exported 1000 notes to notes.yaml

The notes.yaml file will look something like this:

- noteId: 1512345678901
  Japanese: 猫は机の上にいます。
  Translation: The cat is on the desk.
- noteId: 1512345678902
  Japanese: 彼は毎日公園を散歩します。
  Translation: He strolls in the park every day.
# ... 998 more notes

Next, create a prompt file (prompt-ja-en.txt) to instruct the AI. Use {field_name} syntax for variables that will be replaced with data from each note. We want to process the Japanese field.

File: prompt-ja-en.txt

You are an expert Japanese-to-English translator.

Translate this Japanese sentence to English: {Japanese}

Guidelines:
- Translate accurately while preserving nuance and meaning.
- Be natural and idiomatic in English.
- If possible, structure the English so the original Japanese grammar can be inferred.

Instructions:
1. First, analyze the sentence structure and key elements.
2. Think through the translation choices and any nuances.
3. Provide your final translation wrapped in <result></result> XML tags.

Format your response like this:
- Analysis: [your analysis of the sentence]
- Translation considerations: [your thought process]
- <result>[your final English translation here]</result>

Now, run the process-file command. We'll tell it to use our notes.yaml file as input, write to a new notes-translated.yaml file, process the Translation field, and use our prompt template.

The tool will read the Japanese field from each note to fill the prompt, then the AI's response will overwrite the Translation field.

anki-llm process-file notes.yaml \
  --output notes-translated.yaml \
  --field Translation \
  --prompt prompt-ja-en.txt \
  --model gemini-2.5-flash \
  --batch-size 10 \
  --require-result-tag

• notes.yaml: The input file.
• --output notes-translated.yaml: The output file.
• --field Translation: The field we want the AI to generate and place its result into.
• --prompt prompt-ja-en.txt: Our instruction template.
• --model gemini-2.5-flash: The AI model to use.
• --batch-size 10: Process 10 notes concurrently for speed.
• --require-result-tag: Ensures the tool only saves the content inside the <result> tag, ignoring the AI's analysis.

You will see real-time progress as it processes the notes:

============================================================
File-Based Processing
============================================================
Input file:        notes.yaml
Output file:       notes-translated.yaml
Field to process:  Translation
Model:             gpt-4o-mini
Batch size:        10
...
============================================================

Reading notes.yaml...
✓ Found 1000 rows in YAML

Loading existing output...
✓ Found 0 already-processed rows

Processing 1000 rows...
Processing |████████████████████████████████████████| 100% | 1000/1000 rows | Cost: $0.0234 | Tokens: 152340

✓ Processing complete

============================================================
Summary
============================================================
- Successes:         1000
- Failures:          0
- Total Processed:   1000
- Total Time:        85.32s
- Model:             gpt-4o-mini
- Dry Run:           false
---
- Total Tokens:      152,340
- Input Tokens:      120,100
- Output Tokens:     32,240
- Est. Cost:         $0.02
============================================================

The final step is to import the newly generated translations back into Anki. The tool uses the noteId to find and update the existing notes.

anki-llm import notes-translated.yaml --deck "Japanese Core 1k"

• notes-translated.yaml: The file with our improved translations.
• --deck "Japanese Core 1k": The destination deck.

The model type will be automatically inferred from the existing notes in the deck. You can also explicitly specify it with --model "Japanese Model" if needed.

============================================================
Importing from notes-translated.yaml to deck: Japanese Core 1k
Model: Japanese Model
Key field: noteId
============================================================

✓ Found 1000 rows in notes-translated.yaml.

✓ Valid fields to import: Japanese, Translation, Reading, Sound

✓ Found 1000 existing notes with a 'noteId' field.

✓ Partitioning complete:
  - 0 new notes to add.
  - 1000 existing notes to update.

Updating 1000 existing notes...
✓ Update operation complete: 1000 notes updated successfully.

Import process finished.

That's it! All 1000 notes in your Anki deck have now been updated with high-quality translations.

Sentence flashcards often benefit from a focused vocabulary breakdown. You can use anki-llm to populate a dedicated Key Vocabulary field with structured HTML that spotlights the most important words in each sentence.

Create a prompt that instructs the model to reason about the sentence, pick the top 1–3 items, and return clean HTML. This example assumes your notes have Japanese and English fields. You can start from the full prompt example in examples/key_vocabulary.md.

File: prompt-key-vocab.txt

You are an expert Japanese vocabulary AI assistant designed for language learners. Your primary role is to analyze Japanese sentences, identify the most significant vocabulary words, and produce clear, concise, and educational explanations formatted in clean, semantic HTML.

The user is an intermediate learner who uses sentence flashcards to practice. Your output will populate a "Key Vocabulary" field on their Anki flashcard. The HTML you generate must be well-structured to allow for easy and flexible styling with CSS.

English: {English}
Japanese: {Japanese}

Analysis: Explain which vocabulary items you chose and why they matter for an intermediate learner.
Always produce between 1 and 3 key vocabulary entries using the following HTML structure (use dictionary form in the heading and include the kana reading in parentheses):

<h3>WORD (reading)</h3>
<dl class="vocab-entry">
  <dt>Type</dt>
  <dd>Part of speech</dd>

  <dt>Meaning</dt>
  <dd>Concise English definition</dd>

  <dt>Context</dt>
  <dd>Sentence-specific explanation, including any conjugation or nuance notes.</dd>
</dl>

Replace the placeholder content with the actual vocabulary analysis. Within the `<result>` tags, output only the completed HTML entries with no additional commentary.

<result>
</result>

Process your exported notes and overwrite the Key Vocabulary field with the HTML generated by the prompt:

anki-llm process-file sentences.yaml \
  --output sentences-key-vocab.yaml \
  --field "Key Vocabulary" \
  --prompt prompt-key-vocab.txt \
  --model gemini-2.5-flash-lite \
  --require-result-tag

• --field "Key Vocabulary": Updates that specific field on each note.
• --require-result-tag: Keeps only the HTML between <result> tags and drops the analysis from the prompt.

When you open the processed YAML/CSV, the generated field will look like this:

Key Vocabulary: |
  <h3>控える (ひかえる)</h3>
  <dl class="vocab-entry">
    <dt>Type</dt>
    <dd>Ichidan verb</dd>

    <dt>Meaning</dt>
    <dd>To refrain; to hold back</dd>

    <dt>Context</dt>
    <dd>Appears as 控えていて, the te-form plus いる to show an ongoing act of self-restraint in the scene.</dd>
  </dl>

  <h3>さっぱり (さっぱり)</h3>
  <dl class="vocab-entry">
    <dt>Type</dt>
    <dd>Adverb</dd>

    <dt>Meaning</dt>
    <dd>Completely; entirely (with a nuance of 'not at all' when paired with negatives)</dd>

    <dt>Context</dt>
    <dd>Modifies わからない to emphasize that the speaker has absolutely no understanding.</dd>
  </dl>

After verifying the results, import the updated file back into Anki to add the structured vocabulary explanations to your cards.

Let's create several new example flashcards for the Japanese word 会議 (meeting) and add them to our "Japanese::Vocabulary" deck.

First, run the generate-init wizard. It will ask you to select your deck and note type, then use an LLM to analyze your existing cards and generate a prompt file tailored to your collection.

Follow the interactive prompts. The wizard will use an AI model (defaults to gpt-5 or gemini-2.5-flash depending on your API key) to analyze existing cards in your deck and create a smart prompt that matches their style and formatting. When it's done, it will save a new file, for example japanese-vocabulary-prompt.md.

The generated file will look something like this:

File: japanese-vocabulary-prompt.md

---
deck: Japanese::Vocabulary
noteType: Japanese (recognition)
fieldMap:
  en: English
  jp: Japanese
  context: Context
---

You are an expert Japanese language assistant. Your goal is to create {count}
distinct, high-quality, contextual Anki flashcards for a vocabulary term. The
term to create cards for is: **{term}**

IMPORTANT: Your output must be a single, valid JSON array of objects and nothing
else. Each object in the array should represent a unique flashcard. All field
values must be strings. Use `<b>` tags to highlight the term in the example
sentence.

Follow the structure shown in this example precisely:

```json
[
  {
    "en": "The <b>meeting</b> is scheduled for 3 PM.",
    "jp": "<b>会議</b>は午後3時に予定されています。",
    "context": "Used in a formal business context."
  }
]
```

Return only a valid JSON array matching this structure. Ensure you generate
{count} varied cards that highlight different nuances, contexts, or usage
examples of the term.

You can now edit this file to further refine the instructions for the AI.

Now, use the generate command with your new prompt file to create card examples for 会議.

anki-llm generate "会議" -p japanese-vocabulary-prompt.md -m gemini-2.5-flash

The tool will make a single API call asking the LLM to generate 3 distinct cards and show progress:

🔄 Generating 3 card candidates for "会議"...
✓ Generation complete: 3 succeeded, 0 failed
  Cost: $0.0027 (930 input + 954 output tokens)

🔍 Checking for duplicates...

After generation, an interactive checklist appears in your terminal. You can use the arrow keys and spacebar to select the cards you want to add to Anki.

📋 Select cards to add to Anki (use Space to select, Enter to confirm):

❯ ◯ Card 1
│     English: The *meeting* was very productive.
│     Japanese: その*会議*は非常に生産的でした。
│     Context: General business context.
│
│ ◯ Card 2
│     English: I have a *meeting* with a client tomorrow.
│     Japanese: 明日、クライアントとの*会議*があります。
│     Context: A common phrase for scheduling.
│
│ ◯ Card 3 (⚠️  Duplicate)
│     English: The *meeting* is scheduled for 3 PM.
│     Japanese: *会議*は午後3時に予定されています。
│     Context: Used in a formal business context.

Here, we see three options. Card 3 has been flagged as a potential duplicate because a similar card already exists in the deck. Let's select the first two cards and press Enter.

The selected cards are immediately added to your Anki deck.

📥 Adding 2 card(s) to Anki...

✓ Successfully added 2 new note(s) to "Japanese::Vocabulary"

That's it! You have successfully generated, reviewed, and imported multiple high-quality, contextual Anki cards from a single command.

Use tsx to run the CLI directly from TypeScript source without rebuilding:

pnpm tsx src/cli.ts export "My Deck" notes.yaml

Use pnpm link to test the command globally:

pnpm link --global
anki-llm export "My Deck" notes.yaml

Note: The linked command uses compiled JavaScript from dist/. Run pnpm run build after making changes to see them reflected.

To unlink: pnpm unlink --global