| name | context-extraction |
| description | Writes meaningful ai_context values in Crowdin JSONL files for strings that need translator context. Use when extracting context, filling ai_context fields, adding context to ambiguous strings, or working with Crowdin JSONL context files. |
Context Extraction
Information sources (priority order)
For each string, use these JSONL fields in order - stop when you have enough to write a confident ai_context:
text - the source string itself; always the primary signalkey - often encodes structure (e.g. button.save, modal.title.delete_user, error.network.timeout)context - may contain a free-text description, a translator comment, a source file reference, or be empty; treat as supplementary and don't assume any specific format- Source files - only if
context contains a parseable file path + line number and the string is still ambiguous; read ±10–15 lines around the reference to identify UI element type, surrounding component, and props
Strings that need context
Prioritize these - skip strings that are already clear from text + key alone:
- Ambiguous short words - single verbs, nouns, or adjectives that change meaning depending on UI placement
- Color / status names - words that name a color, state, or category; could be a label, badge, or filter
- ICU / message format strings - strings with plural forms or named parameters whose meaning depends on what's being counted or substituted
- Strings with inline tags - text containing markup tags where the role of the tagged portion isn't clear from the string alone
- Short phrases with unclear scope - brief imperative or standalone phrases that could belong to multiple UI contexts
Writing good ai_context values
- 1–3 sentences, written for a translator - not a developer
- State the UI element type (button, label, tab, tooltip, modal title, dropdown option, etc.) and where it appears
- For plurals: what entity is counted, what
# is replaced with
- For inline tags: what the tagged portion renders as (link, code, bold text, etc.)
- For color/status names: whether it's a selectable option, badge, filter label, etc.
- Avoid file names and variable names unless they clarify meaning
Examples
| String | Key | Good ai_context |
|---|
"Red" | color.red | "Color option label in a color picker. Refers to the color red as a selectable choice." |
"Blue" | (none) | "Color name used as a selectable option or status label. Clarify based on surrounding UI." |
"{count, plural, one {# month} other {# months}}" | duration.months | "Displays a duration in months. '#' is replaced by the numeric count." |
"Edit <0>src/App.tsx</0> and save to test HMR" | (none) | "Instructional UI message. The tagged portion is rendered as an inline code element highlighting a filename." |
"New" | button.new | "Label for a button that creates a new item. The exact entity depends on the current page context." |
JSONL file safety rules
Only ever edit the ai_context field value. Never touch id, key, text, file, or context.
Editing procedure
Single-string (1–5 lines): Use StrReplace scoped to the exact line. Target the "ai_context":"" substring (or the full current value if already set):
old: "ai_context":""
new: "ai_context":"Your context here."
Batch (many strings): When dozens or hundreds of lines need context, work in batches instead of one string at a time.
- Read a portion of the file (or the whole file if manageable), identify lines that need
ai_context, fill context for a batch of those lines (parse line as JSON, set ai_context, serialize back to a single line), then write or apply that batch of changes. Repeat for the next batch until done.
- Prefer writing (or applying edits) in batches of lines rather than a single global read-then-write or hundreds of single-line replacements. Batch size is up to the implementer (e.g. tens or a hundred lines per batch).
- When batching, still apply the validity checklist below to a sample of changed lines before saving.
Validity checklist (verify before saving)
Character escaping
" → \"\ → \\- Newline → avoid; use a space instead
