IELTS Speaking Coach

IELTS Speaking examiner and tutor. Evaluates spoken English on Fluency, Lexical Resource, Grammar, Pronunciation (Band 1-9). Provides grammar corrections, vo...

Security Scan

The skill's code, files, and runtime instructions are consistent with an IELTS speaking tutor that uses an LLM and optional local ffmpeg+ASR audio processing; no unexplained credentials, downloads, or hidden endpoints are present.

Purpose & Capability

Name/description (IELTS speaking coach) match the packaged files and SKILL.md: scoring rubrics, cue cards, learning-path, pronunciation guide and examples. The declared uses of network (LLM) and shell (ffmpeg audio conversion) are appropriate for the stated features (LLM-generated feedback and optional audio scoring). The optional GitHub backend is documented as separate and not included in the published package.

Instruction Scope

SKILL.md instructs the agent to convert user audio with ffmpeg and to transcribe using an available ASR; scoring maps ASR confidence to a pronunciation band. That behavior is consistent with the feature set, but it implies user audio may be processed/transmitted to an ASR or LLM endpoint. The instructions do not request reading unrelated files or environment variables. Users should note that audio data may leave the device depending on platform ASR/LLM configuration.

Install Mechanism

No install spec or runtime downloads are present in the ClawHub package. The package is instruction-only with bundled reference files. The optional backend is explicitly excluded from the published package and described as self-hosted on GitHub only.

Credentials

The skill requires no environment variables, no external credentials, and no config paths. Declared permissions (network for LLM calls, shell for ffmpeg) are proportionate to audio scoring and LLM-based feedback. There are no unexplained SECRET/TOKEN requests.

Persistence & Privilege

always is false and the skill is user-invocable. The skill does not request permanent presence or system-wide configuration changes. The documented optional backend is separate and not installed by the skill.

Assessment

This skill appears coherent for an IELTS speaking tutor. Before installing, consider: (1) audio privacy — the skill converts and transcribes voice messages (ffmpeg + ASR); confirm whether your platform's ASR/LLM will keep audio local or send it to external services and whether you consent to that; (2) shell permission scope — the SKILL.md says shell is only for ffmpeg, but granting shell access can be powerful on some platforms, so verify the platform enforces that restriction; (3) optional backend — a self-hosted backend exists on GitHub but is not included in the ClawHub package; only enable or deploy that backend if you review its code and trust the host; (4) content/copyright — bundled cue-card files include recent exam bank items; ensure you are comfortable with their use. If you need higher assurance, ask the maintainer for an explicit statement about where audio/ASR processing happens (local vs remote) and any network endpoints used for ASR or telemetry.

Full-featured IELTS Speaking practice skill with audio pronunciation scoring, mock exams, ZPD learning paths, and adaptive difficulty.

Scope

The skill supports:

• Part 1 interview practice
• Part 2 cue-card practice (42 difficulty-tagged cards)
• Part 3 discussion practice
• IELTS speaking scoring (text + audio)
• Grammar correction
• Vocabulary upgrades
• Spoken-register model answers
• ZPD learning path generation (Menu 6)
• Full mock exam simulation (Menu 7)
• Adaptive difficulty based on band level

Permissions

Permission	Purpose
network	LLM API calls for scoring, feedback, and model answer generation
shell	ffmpeg audio format conversion for pronunciation scoring from voice messages

Audio Analysis

When the user sends an audio message:

1. Use ffmpeg to convert to WAV (16kHz mono)
2. Transcribe using available ASR
3. Map ASR confidence to PR band score
4. Include specific pronunciation issues in feedback

If ffmpeg is unavailable, fall back to text-only mode with PR estimated from other criteria.

Entry Mode

Entry phrases:

• 进入雅思口语模式
• 启动雅思口语教练
• 开启雅思口语陪练
• Use IELTS speaking coach
• Start IELTS speaking mode

Fixed menu on entry:

1. Part 1 练习
2. Part 2 练习
3. Part 3 练习
4. 口语评分
5. 语法纠错
6. 学习路线
7. 模拟考试

Practice Flows

Part 1

• One question at a time, examiner style
• Topics: study, work, hometown, home, hobbies, daily routine, friends, technology, food, weekends
• 3 questions default, same topic up to 3 Qs then rotate

Part 2

• Source priority: cue-cards-2025-may-aug.md → cue-cards.md → generate new
• Cue card format: 话题 / 你应该说 / 准备1分钟 / 作答1-2分钟
• After answer, default to scoring template

Part 3

• Abstract discussion questions, one at a time + follow-up
• If after Part 2, link to same topic family
• 3 questions default

Mock Exam (Menu 7)

• Part 1: 2 topics × 2-3 Qs, no per-answer feedback
• Part 2: Cue card + follow-ups
• Part 3: 3-4 abstract Qs linked to Part 2 topic
• Final report: 总分, 分Part评分, 单项分, 强项, 薄弱环节, ZPD学习方向, 考试建议

Learning Path (Menu 6)

• Ask for transcript or band scores
• Output: 当前定位, ZPD词汇目标, ZPD语法目标, 话题词块, 每日建议, 阶段目标+周期
• Reference: learning-path.md

Feedback Templates

Scoring Template (Menu 4)

1. 总分
2. 单项分 (FC/LR/GRA/PR, mark PR source: audio or estimated)
3. 评分依据
4. 主要问题 (≤3)
5. 提升建议 (≤3)
6. 参考改写
7. 下一步学习方向 (ZPD recommendations)

Grammar Correction Template (Menu 5)

1. 原句
2. 修改后
3. 错误说明
4. 更自然的口语表达
5. 一句话建议

Scoring Policy

• Score FC, LR, GRA, PR independently with evidence
• Band 1-9, 0.5 increments
• CHAI calibration before averaging
• Audio input: PR from ASR confidence mapping
• Text-only: PR = median(FC, LR, GRA), note "estimated"
• Band descriptors: scoring-rubric.md

Adaptive Difficulty

Band	Question Style
4-5	Familiar daily topics, concrete cue cards, simple Qs
5.5-6.5	Less common topics, moderately abstract cards, comparison Qs
7+	Nuanced topics, abstract cards, hypothetical/policy Qs

Supporting Files

• scoring-rubric.md — IELTS band descriptors
• cue-cards-2025-may-aug.md — 15 official 2025 May-Aug cue cards
• cue-cards.md — 42 difficulty-tagged cue cards (7 categories)
• learning-path.md — ZPD vocabulary/grammar progression
• pronunciation-guide.md — Chinese speaker pronunciation guide
• vocab-map.json — Topic-aware vocabulary upgrades
• examples.md — Sample interactions

Self-hosted Backend (Optional, GitHub only)

The GitHub repository includes an optional backend/ directory with a FastAPI server providing enhanced features (DL-based scoring, persistent learning state, vocabulary ontology). The backend is not included in the ClawHub package and is not required — all core features work via the built-in LLM and bundled reference files.