Write Like Me

Write Like Me

A single skill with two modes:

• Build mode. No voice profile exists yet. Runs a 7-stage workflow: analyzes your writing corpus using linguistic and psychological measurement methods, mines your style rules, and generates a personalized voice profile saved to voices/<name>/.
• Write mode. A voice profile exists. Reads it and writes or revises prose in that voice. The universal anti-AI baseline (references/00-universal-baseline.md) is always active, on top of whatever the voice profile specifies.

Multiple voices are supported. Each voice is a named subfolder in voices/. You can have one for your own writing, one for a different register, one built from an admired author's work.

Session start. Mode detection

On every invoke:

1. Check the voices/ directory.
2. If empty: say "No voice profiles found. I can build one from your writing corpus. Do you want to start?" then begin Stage 1.
3. If one voice exists: say "Found voice: [name]. Write using it, or build a new one?" Wait for choice.
4. If multiple voices exist: list them. Ask which to write in, or whether to build a new one. Wait for choice.

Write mode

When a voice is selected:

1. Read voices/<name>/01-generative.md. Positive patterns, quantitative targets, exemplars.
2. Read voices/<name>/02-corrective.md. Hard bans, scan checklist.
3. Read references/00-universal-baseline.md. Always active, applies to every voice.
4. State in one sentence what you will write or revise. Wait for confirmation.

After delivering prose:

1. Save the draft to a file (or pipe it) and run the checker: python scripts/voice_check.py <draft.md> --profile voices/<name>/profile.json --register <register>
2. Fix every block hit and run again until the last line is VOICE_CHECK_PASS or VOICE_CHECK_REVIEW.
3. Read each review hit and each drift line. Fix it or say in one line why it stays.
4. Then scan for the judgment rules in voices/<name>/02-corrective.md that no command can check.

Build mode. 7-stage workflow

Stage 1. Discover

Read references/01-corpus-discovery.md.

Ask the user three questions in one message:

1. What is the corpus? Their own writing: a folder path, a list of files, or a description of what to look for. Or an admired author's published work they want to analyze as an influence layer.
2. Are there conversation exports to include? (e.g., Claude.ai exports, ChatGPT history exports) If yes, what marker identifies their turns? (e.g., ## You, **User:**)
3. Are there existing style notes, corrections, or rule files to incorporate?

Also confirm: is this writing theirs, or do they have permission to analyze it?

Corpus type matters:

• Own writing → sets hard rules (what they never do, what they actually do). The floor.
• Admired author → sets an influence layer (positive patterns to reach toward). Not hard rules.

State what was found and ask for confirmation before proceeding.

Stage 2. Extract and Preview

Read references/02-author-filtering.md.

For conversation exports: run scripts/extract_author_turns.py with the confirmed author marker. Show 5 random extracted samples and confirm the filter worked. Flag:

• Extracted content < 10% of source file (filter may be wrong)
• Content contains AI-output markers ("I'll help you", "Certainly!", "Here's a")
• Total corpus < 20,000 words after filtering (warn: distributions unreliable below this)

For prose files: confirm file count and approximate word count.

Stage 3. Analyze

Run scripts/stylometry.py over the full filtered corpus and over each register subset separately (e.g., formal essays, casual writing, conversation).

For each register:

python scripts/stylometry.py <path> --register <name> --output voices/<name>/<register>.stylometry.json

Report word counts per register and any warnings.

Stage 4. Mine Rules

Read references/04-rule-mining.md.

Three sources, in order:

1. Existing rule files. Read verbatim, extract every stated rule, correction, prohibition.
2. Extreme statistics. Feature usage at < 0.5% or > 5× general-English baseline → candidate rule.
3. Negative space. Sentence shapes never used, connective patterns absent, lengths never reached.

Compile candidate rules.

Stage 5. Review (STOP, wait for user)

Present mined rules as a numbered checklist. For each: state the rule and its evidence source.

Ask the user to:

• Mark false positives
• Add missing rules
• Confirm register labels

Do not proceed until the user responds.

Stage 6. Emit

Read references/05-exemplar-selection.md and references/06-skill-emission.md.

Using the approved rules and corpus statistics:

1. Select 3–5 exemplar passages from the corpus
2. Write the voice profile to voices/<name>/ using the templates:
   • voices/<name>/01-generative.md
   • voices/<name>/02-corrective.md
   • voices/<name>/03-corpus-source.md
3. Write the standalone stylometric report to the corpus root
4. Initialize the machine-readable profile and validate it: python scripts/voice_profile.py init --voice <name> --register <primary-register> --from-stylometry voices/<name>/<primary-register>.stylometry.json --output voices/<name>/profile.json Then edit bans and the per-register policies to match the approved rules from Stage 5, and run python scripts/voice_profile.py validate voices/<name>/profile.json.
5. Write voices/<name>/goldens.jsonl from every approved BAD and GOOD example in 02-corrective.md (BAD becomes block or review, GOOD becomes pass), then save the baseline: python eval/voice_eval.py baseline --goldens voices/<name>/goldens.jsonl --profile voices/<name>/profile.json --output voices/<name>/eval-baseline.json
6. Write voices/<name>/claude-ai-skill.md using templates/generated-claude-ai-skill.md. This file is self-contained: all rules, patterns, and exemplars are inlined directly with no references to external files. It is ready to upload to Claude.ai → Settings → Customize → Skills, or to paste into the Skills instruction field.

Ask if the user wants to inspect any file before verification.

Stage 7. Verify

Read references/07-verification.md.

Hold out 2–3 corpus samples not used as exemplars. Generate text on the same topics using the new voice.

Part one is mechanical and automated. Run voice_check.py on the generated paragraph with the new profile and require VOICE_CHECK_PASS or VOICE_CHECK_REVIEW, then run the eval gate and require VOICE_EVAL_OK.

Part two keeps the held-out generation comparison from references/07-verification.md for sentence length, hedge density, and concession rate, which the drift lines now report.

Report pass/fail. Flag any rule that needs sharpening.

Routing table

Scripts

What this skill does not do

• Does not generate ideas, arguments, or research. It captures and applies how someone writes, not what they write about
• Does not compare against external benchmarks or evaluate quality in the abstract
• Does not handle non-English writing conventions
• Does not invent exemplars. All exemplars come from the actual corpus