transform-style

name

transform-style

description

Apply a stylistic / formatting transformation to a transcript using a named prompt from the user's Text-Transformation-Prompt-Library (206 transformations covering blog outlines, briefs, business correspondence, analysis documents, meeting notes, and more). Use when the user asks to "convert this to <style>", "format like a <type>", or names a specific transformation. Fetches the catalog on demand and caches it; never commits the prompts into this plugin.

Transform style

Take a transcript (any stage) and reformat it according to a named transformation from the user's prompt library at https://github.com/danielrosehill/Text-Transformation-Prompt-Library.

Source of transformations

Authoritative URL:

https://raw.githubusercontent.com/danielrosehill/Text-Transformation-Prompt-Library/main/prompts.json

Schema (per entry):

{
  "name": "Analysis Document",
  "description": "",
  "system_prompt_text": "Your task is to take the text provided by the user, …",
  "expected_output_format": "",
  "delivers_structured_output": "",
  "converted_at": "2025-05-28"
}

206 entries as of 2026-04. The catalog evolves upstream — never commit a snapshot into this plugin.

Cache

~/.cache/claude-transcription/transformations/prompts.json
~/.cache/claude-transcription/transformations/.last-refresh   (timestamp)

Refresh policy: fetch on first use, then re-fetch if cache is older than 7 days or the user passes --refresh. Fall back to cached version silently if upstream is unreachable.

Picking a transformation

Three input modes:

1. Exact name: transform-style "Analysis Document" <transcript> → match name case-insensitively
2. Fuzzy / partial: transform-style "blog outline" <transcript> → fuzzy-match against name and description; if multiple hits, present the top 5 and ask
3. No name given: list categories (derived from name patterns) and offer browse:

   What kind of transformation? e.g.:
     - Blog / writing:       "blog outline", "analysis document", "newsletter"
     - Business:             "business correspondence", "brief", "meeting summary"
     - Personal / notes:     "diary entry", "journal", "reflection"
     - Structural:           "basic text fixes", "bullet points", "executive summary"
   Or pass --list to dump all 206.
   

Applying the transformation

Send Claude (current session) two parts:

• system: the entry's system_prompt_text (verbatim)
• user: the transcript content

Don't add wrapping text or modify the system prompt. The library's prompts are the spec.

If delivers_structured_output is truthy and indicates a specific format (JSON, table, etc.), respect it in the output filename suffix.

Output convention

<stem>.<transform-slug>.md

Slug = name lowercased, spaces → hyphens, non-alphanumerics stripped.

Examples:

• recording.structured.md + "Analysis Document" → recording.structured.analysis-document.md
• recording.cleaned.md + "Blog Outline" → recording.cleaned.blog-outline.md

If the transformation produces JSON, suffix .json instead of .md.

Outputs go in transcripts/ (same folder as source).

Header

Each output begins with:

_Transformed from <source-filename> via "<transformation-name>" — 2026-04-28_

So provenance is traceable.

Flags

• --refresh — force re-fetch of the prompt catalog
• --list — dump all 206 transformation names (one per line) and exit
• --show <name> — print the full prompt for a transformation without applying it (for inspection)
• --out-dir <path> — override default transcripts/

Composing with other skills

This skill is terminal — its output is what the user wanted. But it composes naturally:

• Run iterative-refine first (to get from raw → structured), then transform-style for the stylistic conversion. Most transformation prompts assume cleanish input.
• Run translate-transcript after transform-style if the output should be in a different language than the source. (Translating then transforming usually works worse — the transformation prompts are written in English and assume English input.)

What not to do

• Don't bundle / commit the prompts.json into this repo. It's the user's separate repo with its own update cadence.
• Don't modify system_prompt_text in any way — even small tweaks defeat the point of having a curated library.
• Don't apply multiple transformations in one pass. Chain them as separate invocations so each intermediate is preserved.
• Don't overwrite the source. Always emit a new file with the transform-slug suffix.

Failure modes

• Upstream unreachable, no cache: bail with a clear error pointing the user to the repo URL
• Name matches multiple entries: present top 5, ask user to pick
• Name matches nothing: suggest the 3 closest by string distance
• Transcript is huge (>50k tokens): warn before submitting; some transformations don't degrade well at length and a chunked approach may be needed (the library's prompts generally don't account for chunking)