By xiaolai
Auto-corrects English errors in prompts and refines text with :: prefix; offers grammar, punctuation, tone, and technical writing review for developer prose (commits, docs, PRs). Tracks recurring mistakes and improvement trends.
Configure claude-english-buddy — set language, strictness, toggle auto-correction. <example> Context: User wants to inspect the currently active merged configuration. user: "/claude-english-buddy:config --show" assistant: "Reading project and plugin-data config files and displaying the merged active settings with their source." </example> <example> Context: User wants to raise the strictness level for their next session. user: "/claude-english-buddy:config --set strictness=strict" assistant: "Updating .claude-english-buddy.json with strictness=strict and showing the updated merged config." </example>
Spot-quiz on your top recurring English mistakes — presents one sentence per drill round with an intentional error matching your top-3 mistake categories, then asks you to correct it. Learning tool, not an evaluator. <example> Context: User has several weeks of correction history and wants active practice on their blind spots. user: "/claude-english-buddy:drill" assistant: "Loading your top-3 recurring mistake categories and generating a drill sentence." </example> <example> Context: User wants to focus a drill on just one category. user: "/claude-english-buddy:drill --category article" assistant: "Running a drill round focused on article errors from your history." </example>
Your top recurring English mistakes — all-time patterns that need attention. <example> Context: User wants the default top-20 all-time recurring mistakes. user: "/claude-english-buddy:mistakes" assistant: "Loading all-time correction history and ranking your top 20 recurring patterns by frequency." </example> <example> Context: User wants only the top 5 patterns to focus on. user: "/claude-english-buddy:mistakes --top 5" assistant: "Showing your top 5 recurring mistakes grouped by category with focus areas." </example>
Dry-run review — show what WOULD be corrected in a prompt WITHOUT submitting it or triggering auto-correction. Useful before important prompts, commit messages, or PR descriptions. <example> Context: User is about to submit a high-stakes prompt and wants to see corrections first. user: "/claude-english-buddy:preview refactor the autentication modul, its got too many responsibilties" assistant: "I'll run a preview review and show you what would be corrected before you submit." </example> <example> Context: User drafted a commit message and wants a dry-run of the hook's corrections. user: "/claude-english-buddy:preview Fixed parser bug, updated tests also" assistant: "Previewing the text through the same correction pipeline the hook uses." </example>
Deep English review of any text — commit messages, PR descriptions, docs, emails. <example> Context: User pastes a draft PR description inline and wants a deep review before posting. user: "/claude-english-buddy:review This PR fix the parser bug and add tests for edge case" assistant: "Reviewing the inline text for grammar, clarity, tone, structure, and technical accuracy." </example> <example> Context: User wants to review a longer document saved as a file. user: "/claude-english-buddy:review docs/release-notes.md" assistant: "Reading docs/release-notes.md and producing a full review with corrected version, changes table, and summary." </example>
Sentence-level clarity review — flags run-on sentences, ambiguous references, deeply nested clauses, and terminology drift. Suggests restructuring, not re-wording. Does not correct grammar or judge tone. <example> Context: User wrote a long paragraph with several nested clauses and wants a second opinion on readability. user: "Is this paragraph too hard to follow?" assistant: "I'll use the clarity-enhancer agent to flag nested clauses and ambiguous references." <commentary> Clarity is a distinct axis from grammar and tone. This agent focuses purely on structure and reference integrity. </commentary> </example> <example> Context: User has a draft README where the same concept is referred to as "function", "method", and "handler" in different paragraphs. user: "Check this README for inconsistent terminology." assistant: "I'll dispatch the clarity-enhancer to flag terminology drift and ambiguous pronouns." <commentary> Terminology consistency is a clarity concern, not a grammar concern — the individual words are fine but the reader has to hold three labels for one concept. </commentary> </example>
Fast mechanical grammar and punctuation check — flags spelling, agreement, tense, article, preposition, and punctuation errors against established rules. Does not evaluate tone, clarity, or structure. <example> Context: Orchestrator needs a first pass on a short paragraph before deeper review. user: "Run a grammar check on this README blurb." assistant: "I'll dispatch the grammar-checker agent for a mechanical pass." <commentary> Grammar-checker is the cheap first pass — it catches typos, agreement, and punctuation errors without spending judgement cycles on tone. </commentary> </example> <example> Context: Writing-reviewer orchestrator needs per-sentence error counts for a long doc. user: "Check the API reference for grammar mistakes." assistant: "I'll use the grammar-checker agent to list errors sentence by sentence." <commentary> Because this agent runs on haiku, it is suitable for high-volume mechanical passes where the cost of sonnet-level review is not justified. </commentary> </example>
Judgement-heavy tone evaluation — given a text and its destination (commit / PR / doc / email / chat), scores how well the tone fits and flags mismatches. Does not touch grammar or punctuation. <example> Context: User drafted a commit message and wants tone feedback before running a deeper review. user: "Does this commit message sound right?" assistant: "I'll use the tone-calibrator agent with context-type=commit to score the tone." <commentary> Tone issues (e.g. past tense, trailing period, filler words) are out of scope for grammar-checker. The tone-calibrator is where those judgements happen. </commentary> </example> <example> Context: User pasted an email draft to a senior colleague and is unsure about formality. user: "Is this too casual for an email to my manager?" assistant: "I'll dispatch the tone-calibrator with context-type=email to judge formality and match it to the recipient." <commentary> Register and formality are judgement calls; that is why this agent runs on sonnet, not haiku. </commentary> </example>
Deep English text reviewer — orchestrates three specialist subagents (grammar-checker, tone-calibrator, clarity-enhancer) and merges their findings into one report. Preserves the historical `/review` output format. <example> Context: User wants a thorough review of a long piece of text user: "Review this README draft for English quality" assistant: "I'll use the writing-reviewer agent to orchestrate grammar, tone, and clarity specialists." </example> <example> Context: User wrote a PR description and wants it polished user: "Check if this PR description sounds professional" assistant: "I'll dispatch the writing-reviewer; it will delegate grammar, tone, and clarity to three specialists and merge the findings." </example>
Recurring error patterns from non-native English speakers in developer contexts: article misuse, preposition confusion, tense mismatch, 'I am agree'-style anti-patterns, and other frequent L2 slips. Use when scanning for patterns a reader can map back to familiar mistakes rather than rediscover from first principles.
Core English grammar rules most likely to trip non-native developers: articles, subject-verb agreement, tense consistency, prepositions, countable vs mass nouns, comparatives. Use when reviewing grammar correctness of prose written for developer contexts (commits, docs, emails).
Punctuation conventions for developer prose: commas (serial, Oxford, clause-joining), semicolons, colons, hyphens vs en-dashes vs em-dashes, apostrophes, quotation marks. Use when reviewing punctuation of documentation, commit messages, or any prose where mis-pointing changes meaning.
Technical writing patterns for developer prose: API documentation structure, README shape, error-message wording, terminology consistency, and the active vs passive voice trade-off. Use when the text under review is documentation, a README, an API reference, or an error string.
Per-context tone rubrics for developer communication: commit messages, PR descriptions, code comments, API docs, emails, inline chat. Use when judging whether the register and formality of a piece of text fit its destination, not just whether it is grammatical.
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English language coach for non-native speakers who use Claude Code daily.
LLMs understand broken English perfectly. They never correct you. They never push back. They just... comply.
This means every typo, every grammar mistake, every awkward phrasing you type goes unchallenged. Over months of daily AI interaction, bad patterns calcify. Your English doesn't improve — it quietly degrades, because the feedback loop that would catch your mistakes no longer exists.
You're not getting worse at English. You're losing the signal that would make you better.
claude-english-buddy restores the feedback loop. It sits between you and Claude, silently correcting your prompts and showing you what was wrong — every time, automatically, with zero friction.
You type: "refactor the autentication modul, its got too many responsibilties"
You see: Refactor the authentication module. It has too many responsibilities.
(autentication>authentication; modul>module; its got>it has; responsibilties>responsibilities)
Claude sees: the corrected version, responds normally.
When your prompt is clean — silence. No noise. Silence means correct.
Over weeks, you start noticing fewer corrections. That's the feedback loop working.
flowchart TD
A["You type a prompt"] --> B{Language?}
B -->|"English"| C{Errors?}
B -->|"Non-English"| D["Translate to English"]
B -->|":: prefix"| E["Refine into precise prompt"]
C -->|"Yes"| F["Correct + show fixes"]
C -->|"No"| G["Pass through silently"]
D --> H["Show translation"]
E --> I["Show refined version"]
F --> J["Claude acts on corrected version"]
G --> J
H --> J
I --> J
J --> K["You see corrections + Claude's response"]
Four modes, one hook, zero friction:
| Mode | Trigger | What Happens |
|---|---|---|
| Correct | English prompt with errors | Fixes typos/grammar, shows what changed |
| Translate | Non-English detected | Translates to English, shows translation |
| Refine | :: prefix | Rewrites into a precise, structured prompt |
| Summarize | summary_language configured | Claude appends native-language summary |
Two install paths — both reach the same code. Pick one:
Via Anthropic's official community marketplace (curated; updates lag the maintainer's marketplace by up to ~24h):
/plugin marketplace add anthropics/claude-plugins-community
/plugin install claude-english-buddy@claude-community
Via the xiaolai marketplace (latest version lands here first):
/plugin marketplace add xiaolai/claude-plugin-marketplace
/plugin install claude-english-buddy@xiaolai
Install fails with "Plugin not found in marketplace 'xiaolai'"? Your local marketplace clone is stale. Run
claude plugin marketplace update xiaolaiand retry —plugin installdoes not auto-refresh. (The community marketplace doesn't have this caveat — Anthropic's CI keeps it current.)
| Command | Description |
|---|---|
/claude-english-buddy:today | Today's correction report — mistakes, patterns, lessons, trend |
/claude-english-buddy:stats | Long-term trends — error rate over weeks, improvement trajectory |
/claude-english-buddy:mistakes | All-time recurring mistakes — your blind spots |
/claude-english-buddy:config | Configure language, strictness, domain terms |
/claude-english-buddy:review | Deep review of any text (docs, PRs, emails) |
The most powerful feature. Run /claude-english-buddy:today at the end of your day:
# Today's Language Report — 2026-04-01
## Overview
| Metric | Today | Yesterday | 7-day avg |
|--------|------:|----------:|----------:|
| Prompts | 34 | 41 | 37 |
| Corrections | 8 (24%) | 14 (34%) | 11 (30%) |
| Clean prompts | 24 (71%) | 27 (66%) | 25 (68%) |
## Today's Corrections
| # | You Wrote | Corrected | Pattern |
|---|-----------|-----------|---------|
| 1 | "its got too many" | "it has too many" | its vs it's |
| 2 | "autentication" | "authentication" | spelling |
| 3 | "the modul is" | "the module is" | spelling |
| ...
## Lessons of the Day
1. **"who" vs "that"** — Use "who" for people, "that" for things.
Wrong: "the function who handles auth"
Right: "the function that handles auth"
## Trend
You're improving. Error rate down 37% in 3 weeks.
.claude-english-buddy.json){
"auto_correct": true,
"summary_language": "Chinese",
"strictness": "standard",
"domain_terms": ["Tailscale", "Headscale", "MagicDNS"]
}
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