Transcript to Vocabulary

VerifiedSafe

Converts podcast JSON transcripts into vocabulary TXT files for B2-C1 English learners, using keywords and AI to extract taught words.

Sby Skills Guide Bot
ContentIntermediate
7/24/2026
Claude Code
#vocabulary#language-learning#transcript-processing#json#education

Recommended for

Automated summary

Converts All Ears English podcast JSON transcripts into B2-C1 vocabulary study TXT files by combining the provided keyword list with expressions the hosts actively teach in the transcript.

Strengths

  • Merges two sources: the pre-labeled `keywords` fields and the `fullText`, where hosts explain additional expressions
  • Enforces a consistent output format (word, definition, examples, `(idiom)` reserved for true idioms)
  • Batch mode skips files that already have a matching `.txt`
  • Reports which files were created and how many entries each contains

Limitations

  • Paths and JSON schema are hardcoded to this specific podcast (`cleaned_transcripts/`, `name` and `definition` fields)
  • The `(part_of_speech) ... "example"` definition parsing can break on irregular formatting
  • Entries derived from `fullText` depend on model judgment and may need review
When to use it

Use it when you have All Ears English transcript JSON files and want vocabulary sheets generated in bulk.

When not to use it

Avoid it for podcasts with a different JSON schema, or when you need verified dictionary-grade definitions rather than context-based ones.

Security analysis

Safe
Quality score88/100

The skill only reads local JSON transcripts and writes TXT vocabulary files in a specified directory. It declares no tools, invokes no shell/network commands, and contains no destructive, exfiltrating, obfuscated, or safety-disabling instructions.

No concerns found

How this analysis is made

Examples

Single episode
/transcript-to-vocab 2494.json
Process the backlog
Run /transcript-to-vocab with no file name to generate vocabulary files for every transcript in cleaned_transcripts that doesn't have a .txt yet, then summarize what was created.
Check output
/transcript-to-vocab 2501.json — then show me the resulting TXT file and how many items came from keywords vs. the full transcript.

transcript-to-vocab

Convert All Ears English podcast JSON transcripts into vocabulary learning TXT files for B2-C1 level English learners.

When to use

When the user runs /transcript-to-vocab with a JSON file name or asks to process transcript files into vocabulary files.

Instructions

When invoked, do the following:

1. Determine which files to process

  • If a file name is given (e.g. 2494.json), process only that file from cleaned_transcripts/
  • If no file is given, process all .json files in cleaned_transcripts/ that don't already have a corresponding .txt

2. For each JSON file

Read the file. It contains:

  • keywords: array of vocabulary items the course explicitly labels, each with name and definition
  • fullText: full podcast transcript

3. Build the vocabulary list using both sources

Step A — From keywords list: For each keyword, parse its definition field. The format is one of:

  • (part_of_speech) definition text "Example sentence."
  • alternate form - (part_of_speech) definition text "Example sentence."

Extract:

  • The definition text only (drop the part of speech label — but keep (idiom) if it is an idiom)
  • The example sentence from inside the quotes

Step B — From fullText (this is the LLM part): Read the fullText carefully. The hosts are English teachers. Identify every word, phrase, or expression they are actively teaching — meaning they explain its meaning, contrast it with similar words, or highlight its usage for learners. This includes words not in the keywords list.

For each such word/phrase found in fullText that is NOT already in the keywords list, add it to the vocabulary list with:

  • Definition: write a short, clear definition based strictly on how it is used and explained in the transcript. Do not bring in other meanings or contexts.
  • Example: one natural example sentence reflecting the transcript context

4. Output format

Write a .txt file with the same base name (e.g. 2494.txt) inside cleaned_transcripts/.

Format each entry as:

word (idiom): definition
 - Example sentence from definition field or written by you
 - Example sentence from fullText if the word is used naturally there (skip if the sentence is definitional like "the next phrase is X" or "X means Y")

Rules:

  • Only mark (idiom) if it is an idiom. No other part-of-speech labels.
  • Each entry is separated by a blank line
  • If a fullText sentence just names or defines the word rather than using it naturally, skip it
  • Stay faithful to the transcript context. Do not invent unrelated meanings or usages.

5. Confirm when done

After writing the file(s), report which files were created and how many vocabulary items each contains (keywords count + additionally found count).

From the file to a first result

The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.

View the source and supporting files

This download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.

1. Download the file

Download SKILL.md

From the root of a test project, save the file as .skill-downloads/transcript-to-vocabulary-mryiqh7l/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.

Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.

mkdir -p '.skill-downloads/transcript-to-vocabulary-mryiqh7l' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/transcript-to-vocabulary-mryiqh7l/download' --output '.skill-downloads/transcript-to-vocabulary-mryiqh7l/SKILL.md.part' && mv '.skill-downloads/transcript-to-vocabulary-mryiqh7l/SKILL.md.part' '.skill-downloads/transcript-to-vocabulary-mryiqh7l/SKILL.md'

2. Check prerequisites

Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.

No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.

The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.

3. Load the skill

This file does not provide a usable name/description header for this native procedure. It remains available to download and inspect; use the manual method below.

Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.

4. Try a first task

Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:

Read .skill-downloads/transcript-to-vocabulary-mryiqh7l/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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