Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add OleksiiDotsenko/english-exam-coach/plugin install english-exam-coachWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oleksiidotsenko/english-exam-coach/reading-use-of-english)<a href="https://agentmods.dev/skills/oleksiidotsenko/english-exam-coach/reading-use-of-english"><img src="https://agentmods.dev/badge/skills/oleksiidotsenko/english-exam-coach/reading-use-of-english/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oleksiidotsenko/english-exam-coach/reading-use-of-english"><img src="https://agentmods.dev/badge/skills/oleksiidotsenko/english-exam-coach/reading-use-of-english.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00154 | $0.01950 |
| Opus 5 | $0.00077 | $0.00975 |
| Sonnet 5 | $0.00031 | $0.00390 |
| Haiku 4.5 | $0.00015 | $0.00195 |
Grade A, and why
reading-use-of-english scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reading & Use of English
Objective drills: generate → answer → score → explain → log. Paths are
relative to ${CLAUDE_PLUGIN_ROOT} (if unset, resolve relative to the plugin
root — the directory two levels above this file, i.e. ../../ from here).
When to use
The user asks to drill any reading or use-of-english task type, or asks why a reading answer is what it is.
Steps
-
Identify exam + level + task type (ask once if unclear; /daily-drill picks the weakest type from the progress log instead). Load
data/exam-formats/<exam-id>.mdfor the exact shape (items per task, options per question) anddata/cefr/reading-descriptors.md+data/cefr/reading-calibration-anchors.mdto calibrate text difficulty. Seed shapes:data/item-bank/seed/reading-use-of-english-items.md. Check the requested task type is actually in the chosen exam (per its format file): key word transformation and word formation exist only in B2–C2, cross-text matching only in C1, and so on. If the user asks for a task their exam does not contain, say so plainly and offer the nearest valid task or name the exam(s) that do include it — never generate an off-format item. True/False/Not Given vs Yes/No/Not Given: T/F/NG statements are about FACTUAL information in the text; Y/N/NG statements are about the WRITER'S views/claims (so the passage must carry opinions). Use the correct labels for each. -
Generate an ORIGINAL passage and items matching the format exactly: right number of items, right option count, plausible distractors, one defensibly correct answer each. Calibrate lexis and syntax to the CEFR level. Give a realistic time budget (e.g. ~1.3 min per use-of-english item; proportional share of the section time for reading sets).
Match the passage to authentic length — this is as important as the item count. Aim for the upper end of the target range and count your words before presenting: left to instinct these passages come out ~30–40% too short. Use the exam's figure from
data/exam-formats/<exam-id>.md("Passage lengths"); as a fallback:- Gapped text: B2 ~500–600, C1 ~550–780, C2 ~700–800 words of base text.
- Long-text multiple choice / multiple matching: B2 ~500–700, C1 ~700–800,
C2 ~700–800 words of base text. IELTS Academic passages ~700–900 words
each; IELTS General Training sections vary — see
ielts-general.md. A too-short passage is the most common failure — a C2 Part 6 gapped text must be ~750 words, not ~450. - TOEFL Complete the Words (~70 words, exactly 10 gaps): show the
first 3–5 letters of each gapped word, then one underscore per
missing letter, counted exactly —
flow___= flow + 3 = "flowers". Count the answer's letters and subtract the stem for every gap; an eyeballed count gives the learner a different puzzle from the key. Leave the opening sentence intact. Worked example:data/item-bank/seed/reading-use-of-english-items.md. - Cloze / word formation: ~150–220 words. TOEFL academic passage ~200; TOEFL daily-life texts 15–150.
- Gapped text always has ONE more option than gaps (B2/C1/C2) or three more (B1 Part 4) — the surplus fits no gap and is a deliberate distractor. Removed paragraphs run ~50–80 words each.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 139 lines · 154 tokens per session scan A 7a720ceb756d
reading-use-of-english is a skill published in the GitHub repository OleksiiDotsenko/english-exam-coach (8 stars, last pushed 1mo ago), licensed MIT. It adds 154 tokens to every session and 1,950 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
oral-english-practice
A Claude Code skill that stores and analyses spoken-English practice completed in the Claude app. The app handles the conversation; this skill keeps reports, scores, mistakes, progress trends, and the next practice focus.
language-coach
Language coaching for every prompt. Use when the user runs /language-coach with any sub-command (setup, native, target, style, response, goal, mode, focus, band, level, status, off, on). Routes to the correct action based on the argument provided.
language-review
Analyze language learning progress from local data. Use when the user runs /language-coach:language-review or asks to review their progress, see their band history, check improvement trends, or analyze their IELTS score data.
learning-and-development
Builds capability — skills gaps, career frameworks, training that transfers to the job, and internal mobility. Use this to design a career ladder, close a capability gap, decide whether to build or hire a skill, structure onboarding into a role, or work out why training keeps failing to change anything.
diagnose
A short question-based assessment of a pilot's current skill level. It produces a profile and saves it in the connected browser or database.
lesson-close
A workflow for finishing the day's lesson file, recording its status and duration, then saving it to the personal-guide GitHub repository.