# Write Like Me > Write Like Me - Skill: `hiro-inagawa/write-like-me` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add hiro-inagawa/write-like-me` - Raw SKILL.md: https://api.skillmd.com/api/skills/hiro-inagawa/write-like-me/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: Hiro-Inagawa (https://skillmd.com/u/hiro-inagawa) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/hiro-inagawa/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//`. - **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//01-generative.md`. Positive patterns, quantitative targets, exemplars. 2. Read `voices//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 --profile voices//profile.json --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//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 --register --output voices//.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//` using the templates: - `voices//01-generative.md` - `voices//02-corrective.md` - `voices//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 --register --from-stylometry voices//.stylometry.json --output voices//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//profile.json`. 5. Write `voices//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//goldens.jsonl --profile voices//profile.json --output voices//eval-baseline.json` 6. Write `voices//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 | Task | Read | |------|------| | Writing new prose | `voices//01-generative.md` + `references/00-universal-baseline.md` | | Revising prose | `voices//02-corrective.md` + `references/00-universal-baseline.md` | | Finding corpus sources | `references/01-corpus-discovery.md` | | Filtering conversation exports | `references/02-author-filtering.md` | | Understanding measured features | `references/03-methodology.md` | | Mining rules | `references/04-rule-mining.md` | | Selecting exemplars | `references/05-exemplar-selection.md` | | Emitting the voice profile | `references/06-skill-emission.md` | | Verifying the generated profile | `references/07-verification.md` | | Re-running on updated corpus | `references/08-regeneration-and-diff.md` | | Checking a draft mechanically | `scripts/voice_check.py` | | Scoring the checker | `eval/voice_eval.py` | --- ## Scripts | Script | Purpose | |--------|---------| | `scripts/stylometry.py` | Feature extraction, stdlib base, textstat optional, spaCy optional | | `scripts/extract_author_turns.py` | Extract author-only turns from conversation export markdown | | `scripts/generate_report_from_json.py` | Combine JSON profiles into a human-readable report | | `scripts/voice_check.py` | Deterministic draft checker. Reports hits, drift, a verdict, and an exit code | | `scripts/voice_profile.py` | Load, validate, and initialize a voice `profile.json` | | `eval/voice_eval.py` | Score `voice_check.py` against golden passages and gate regressions | --- ## 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