voiceprint
This skill converts a person's spoken words into a finished article by cutting and editing their transcript, rather than composing new text.
Community author
- Agents
- Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode, claude.ai and API
- License
- MIT (permissive)
- Pin
- b8064ccpinned Oct 9, 2026
- Context cost
- about 1,600 tokenswhen the skill loads
- GitHub stars
- 66on charlie947/voiceprint
- Scripts
- None found
What it does
“Use when writing an article, newsletter, blog post or long social post that must genuinely be the user's own writing. Triggers on "voiceprint", "write this in my voice", "turn my recording into an article", "cut my transcript down", "make this actually mine", "write from my video". Turns the user's own spoken words into a finished piece by cutting, never by composing.”
Generated summary from the skill's own text
This skill converts a person's spoken words into a finished article by cutting and editing their transcript, rather than composing new text. It preserves the author's literal sentences and voice by maintaining roughly 85% of their original wording, achieving authentic human writing that cannot be replicated by AI composition alone. The agent structures, fact-checks, and polishes the material while keeping the author's own phrasing intact.
When to use it
- Converting recorded talks, podcasts, or video transcripts into published articles
- Writing newsletters or blog posts that must sound genuinely like the author
- Editing voice notes or dictated material into long-form content
- Turning webinar transcripts into polished pieces with preserved voice
When not to
- When the source material is less than 75% of target word count
- Creating content from written sources instead of spoken recordings
- Projects where AI-composed text is acceptable or preferred
Install
These commands install from the publisher's repository and need no account.
skills CLI
npx skills add charlie947/voiceprint --skill voiceprintInstalls from the repository's default branch, which may have changed since the commit we checked.
GitHub CLI 2.90 or later
gh skill install charlie947/voiceprint voiceprint@b8064ccfb358f0ccd212fc7fb9bb5ebf1709bb9fInstalls the commit we checked (b8064cc).
Copy by hand
Download the folder at b8064cc and put it in .claude/skills/voiceprint for Claude Code and in .agents/skills/voiceprint for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode.
Copy for your agent
A prompt for any coding agent. It installs this skill from GitHub at the pinned commit, copies the files as they are, and runs nothing unless you ask.
Prompt
Install the agent skill "voiceprint" by charlie947. Source, pinned to commit b8064ccfb358f0ccd212fc7fb9bb5ebf1709bb9f: https://github.com/charlie947/voiceprint/tree/b8064ccfb358f0ccd212fc7fb9bb5ebf1709bb9f/skill 1. Download that folder at exactly this commit. Do not use the default branch. 2. Copy every file unchanged, keeping the folder structure, into .claude/skills/voiceprint/ for Claude Code and .agents/skills/voiceprint/ for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode. Create the folders if they do not exist. 3. Do not edit the files, and do not run any script in the folder unless I ask you to. 4. When you are done, list the files you copied and where you put them. Listing and checks: https://www.agentnative.dev/skills/charlie947/voiceprint
SKILL.md at the pin
--- name: voiceprint description: Use when writing an article, newsletter, blog post or long social post that must genuinely be the user's own writing. Triggers on "voiceprint", "write this in my voice", "turn my recording into an article", "cut my transcript down", "make this actually mine", "write from my video". Turns the user's own spoken words into a finished piece by cutting, never by composing. --- # voiceprint Turns someone's own spoken words into a finished article. You do the research, the structure, the headings, the fact-checks and the polish. You do not write the sentences. They already did that, out loud. ## The rule, and why it is the whole skill Text reads as human in proportion to how much of the person's literal wording survives into it. Not their ideas. Their sentences. Measured on one article, same source recording, varying only the proportion of model-composed sentences: | Their own words | Verdict | |---|---| | 100% | 100% human | | 87.5% | 100% human | | 71.7% | 58% human | | 61.7% | 57% human | | 13.5% | 100% AI | It is a threshold, not a slope. Past roughly 85% the verdict flips, and adding more composed text after that barely changes it. The same article written from the same video by a model, using the person's facts and opinions, scored 100% AI. Sourcing the ideas from a human does not help. Only the sentences do. **So: about one sentence in eight may be yours. That is enough for joins and transitions. It is not enough for a section.** ## What this skill will not do It will not make model-written prose read as human. Style profiles, humaniser passes and register-matching have all been measured and they all make it worse, not better. If asked for one, say so once and offer this instead. It is not a detection-evasion tool. It produces writing that genuinely is the author's, because they said it. ## Step 1 — Get their words They need roughly **1.3x their target word count** in spoken material. About 75% of what someone says survives the cut. A 1,500-word article needs around 2,000 spoken words, which is 11 to 13 minutes of talking. Three ways in. Offer whichever fits. **A. Record now.** Best quality. Ask them to talk through the piece as if explaining it to one person. Give them the section list first so they cover everything, then get out of the way. **B. Paste a transcript.** They already have a video, podcast, voice note or webinar on the topic. Any of it works. **C. Dictate into the chat.** Phone keyboard mic, or Google Docs > Tools > Voice typing on desktop, which is free and has no length limit. Works in Chrome, Edge and Safari, not Firefox. If they have existing videos, ask for the transcripts of the ones on this topic. Old *written* work does not count — it can only teach you to imitate, which is the failure mode above. **Do not proceed without enough material.** If they give you 400 spoken words for a 1,500-word article, say so and ask for more. Filling the gap yourself is exactly what produces a 100% AI verdict. ## Step 2 — Do not clean the transcript The disfluency, the repetition, the odd constructions and the false starts are the signal. Never run a transcript through a "tidy this up" pass. That single step converts their prose into model prose before you have started. ## Step 3 — Plan, do not write Return a plan, not paragraphs: - Section order, and which chunk of transcript belongs in each - Headings, which are yours to write. Signposting is not voice. - What to cut and why - What is missing, as questions for them to answer in another recording No body text at this stage. None. ## Step 3.5 — Ask for what is missing The plan will expose gaps. Do not fill them yourself. Ask. Give them a short numbered list, five questions maximum, each aimed at one specific hole. Good questions force something only they have: 1. **A number.** "You said it saved time. How much, on what?" 2. **A moment.** "Walk me through the first time it broke." 3. **A failure.** "What did you try before this that did not work?" 4. **An opinion they would defend.** "What do people get wrong about this?" 5. **The objection.** "Why would someone sensible disagree?" Then say this plainly: **answer them out loud, in the same recording or a new one, and paste the raw transcript.** Not typed. Typed answers drift into written register and lose the thing this whole method depends on. Append their answers to the original transcript. They are now source material and get cut exactly like the rest. If they answer by typing anyway, use it, but tell them those sections will read differently from the spoken parts. **Never answer your own question.** If a gap stays open after asking, either cut that section from the plan or leave it as a visible gap in the draft. A gap they can see is fixable. A gap you filled is a failure they cannot see. ## Step 4 — Edit by deletion The transcript is the draft. The work is subtractive. **Allowed:** cut, reorder, join two of their fragments, split a run-on into paragraphs, fix what speech-to-text misheard (names, technical terms, homophones), add headings. **Not allowed:** rewriting a sentence they said into a better sentence, composing a sentence to make their point more elegantly, smoothing an odd construction, inventing any fact, number, quote or anecdote. The oddness is the voice. Leave it. Where a cut leaves a genuine gap, add the shortest possible bridge and flag it so they can replace it in their own words. ## Step 5 — Check it If they have Python, `trace.py` ships with this skill: ``` python3 trace.py draft.md transcript.txt ``` It classifies every sentence against the transcript and fails the draft below 85% their words, listing each composed sentence. **If they cannot run Python**, do it as a check instead. Go through the draft sentence by sentence against the transcript and list every sentence you cannot point to a source for. Quote the transcript line beside each one you can. If you cannot quote it, you wrote it. Count those words as a percentage of the whole and report the number honestly. Below 85%, cut the composed sentences rather than improving them. ## Step 6 — Hand it back Give them: 1. The draft. 2. Every sentence you composed, listed, for them to retype in their own words. Do not offer to improve those. Retyping them is the point. 3. Two or three places where a detail only they have would strengthen it. ## Honest limits - This predicts a detector's verdict, it does not guarantee one. Genuinely human writing is misclassified sometimes, and detectors misfire hardest on non-native English speakers, autistic writers and formulaic genres. - It only works for people willing to talk. No recording means no human sentences, and nothing invents them. - You can only write about what they have recorded. - Keep the recording, the raw transcript and the draft history. Provenance is worth more than any score if authorship is ever questioned.
From charlie947/voiceprint at b8064cc, shown as plain text under its license: MIT (permissive). The license text is in LICENSE.
View SKILL.md on GitHub at b8064ccFiles
- SKILL.md7.0 KB · bc0d77a49294
1 file, 7.0 KB in total. Each file has a sha256 in the pin's manifest; the first 12 characters are shown.
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