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Homophone detector

Skill XiaoChu-1208/interview-assistant-CLI/skills/homophone-detector

Real-time interview copilot CLI. Hears the question and surfaces the answer you already prepared from your local Markdown knowledge base — verbatim, in milliseconds. Instant recall instead of LLM hallucination.

Install
npx -y skills add XiaoChu-1208/interview-assistant-CLI --skill homophone-detector

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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  • 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Detect polyphones, near-homophones, and ASR ambiguities. Two roles: (1) at runtime, extends the homophone table and the STT hallucination filter so the assistant doesn't get confused by "RAG vs LAG" or by streaming-platform watermark phrases; (2) in AI editors, gives the LLM a checklist for spotting ambiguities in user-generated knowledge bases.

SKILL.md

2.6 KB, as published. Nobody here has run it

homophone-detector

A two-faced skill:

  • Runtime: extends interview_assistant.homophones.PHONETIC_GROUPS from data/homophones.toml, and contributes a hallucination-filter list from data/hallucinations.toml (loaded as a separate runtime: hook by the assistant's loader — see below).
  • AI editor: a checklist for the model to scan a knowledge base and flag potentially-confusable terms.

Runtime contract

This skill ships TWO data files. The loader treats each as a separate runtime block via data-source:

  • data/homophones.toml → merged into PHONETIC_GROUPS
  • data/hallucinations.toml → appended to STTFilter.hallucinations

SKILL.md only declares the first hook in frontmatter; the loader also scans the directory for any *.toml and applies them by target field inside the file. (See data/hallucinations.toml.)

For AI editors

When the user says "scan my knowledge base for ambiguities" or you're editing their knowledge/ folder:

  1. List every English/Chinese term that has a known confusable partner. Flag pairs like (RAG, LAG), (KPI, KBI), (ROI, RAI/RAW), polyphone words in Chinese (重 zhòng/chóng, 还 hái/huán, 长 cháng/zhǎng, 行 xíng/háng).
  2. For each flag, propose ONE of:
    • Add the canonical form to data/homophones.toml.
    • Add a clarifying parenthetical in the knowledge file itself, e.g. "RAG（Retrieval-Augmented Generation）".
  3. Never silently rewrite the user's content — present diffs and ask.

Adding entries

Format of homophones.toml:

[[entry]]
canonical = "rag"
variants  = ["lag", "rad", "rack", "raq"]
note      = "Retrieval-Augmented Generation"

[[entry]]
canonical = "重"
variants  = []
polyphone = ["zhòng", "chóng"]
example   = "重要 zhòng / 重新 chóng"

Format of hallucinations.toml:

target = "hallucinations"

[[entry]]
text = "悠悠独播剧场"
source = "Whisper streaming-platform residue"

[[entry]]
text = "感谢观看"
source = "Whisper YouTube outro residue"

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