chore(skills): remove humanizer (superseded by voice)

I deleted humanizer/SKILL.md now that all three callers (commits.md, respond-to-cj-comments.md, start-work.md) invoke /voice instead. The 25 humanizer patterns live on as patterns 1-25 in voice/SKILL.md. Same source (Wikipedia's Signs of AI writing), same prose, same examples — just renumbered alongside the universal good-writing additions and the personal-only patterns.

I also updated .ai/notes.org and .ai/workflows/wrap-it-up.org to reference /voice personal instead of the old humanizer + manual-passes flow. The wrap-it-up change landed upstream in claude-templates first so it survives the next startup rsync.

todo.org gets the matching update: the voice TODO is marked DONE with a "Built and shipped" timestamp, the publish-mode terminology is renamed to personal-mode throughout, the V1 scope checklist is ticked, the open questions are resolved with the answers we landed on during implementation, and the migration section records the delete-not-alias decision.

4 files changed, 212 insertions, 476 deletions

diff --git a/.ai/notes.org b/.ai/notes.org
index 735e256..0c75bb1 100644
--- a/.ai/notes.org
+++ b/.ai/notes.org
@@ -31,7 +31,7 @@ See [[file:../README.org][README.org]] for the full layout, install modes, and l
 ** Layout (high level)
 
 - =claude-rules/= — generic rules (=commits.md=, =testing.md=, =verification.md=, =subagents.md=) symlinked into =~/.claude/rules/= and applied to every Claude Code session on the machine.
-- Top-level skill directories (=add-tests/=, =debug/=, =five-whys/=, =frontend-design/=, =humanizer/=, =pairwise-tests/=, =playwright-js/=, =playwright-py/=, =root-cause-trace/=) — each a Claude Code skill, symlinked into =~/.claude/skills/= by =make install=.
+- Top-level skill directories (=add-tests/=, =debug/=, =five-whys/=, =frontend-design/=, =pairwise-tests/=, =playwright-js/=, =playwright-py/=, =root-cause-trace/=, =voice/=) — each a Claude Code skill, symlinked into =~/.claude/skills/= by =make install=.
 - =languages/= — per-language bundles (rules + hooks + settings) copied into target projects via =make install-lang LANG=<name> PROJECT=<path>=. Both =LANG= and =PROJECT= are optional — fzf picks them interactively when omitted. Bundles currently shipping: =elisp=, =python=.
 - =.claude/= — repo-local Claude Code config: =settings.json= and =commands/=.
 - =hooks/=, =scripts/= — install helpers and PostToolUse validators that ride along with bundles.
diff --git a/.ai/workflows/wrap-it-up.org b/.ai/workflows/wrap-it-up.org
index 37d2522..5c5dc89 100644
--- a/.ai/workflows/wrap-it-up.org
+++ b/.ai/workflows/wrap-it-up.org
@@ -119,7 +119,7 @@ Commit message rules (also see protocols.org "Git Commit Requirements"):
 
 *Wrap-up commits skip the inline-approval gate.* The =commits.md= rule that requires writing the message to =/tmp/commit-<slug>.md=, printing inline, and waiting for an approve / request-changes / open-in-editor response does *not* apply to wrap-up commits. The wrap-up flow is meant to be quick — Craig has already authorized the wrap by triggering the workflow ("wrap it up"), and stopping again to approve a commit message disrupts the cadence.
 
-Still apply the prose-quality passes silently before committing — humanizer + jargon-rewrite + semicolon-swap + contractions + sentence-split — so the message reads cleanly. Just don't print and ask. Commit directly with the cleaned message.
+Still apply =/voice personal= silently before committing so the message reads cleanly. Just don't print and ask. Commit directly with the cleaned message.
 
 If a wrap-up commit needs Craig's eyes for a content reason (sensitive change, unusual scope, something he flagged earlier), surface it explicitly. Otherwise commit and move on.
 
diff --git a/humanizer/SKILL.md b/humanizer/SKILL.md
deleted file mode 100644
index 88ebe23..0000000
--- a/humanizer/SKILL.md
+++ /dev/null
@@ -1,474 +0,0 @@
----
-name: humanizer
-version: 2.3.0
-description: |
-  Remove signs of AI-generated writing from text. Use when editing or reviewing
-  text to make it sound more natural and human-written. Based on Wikipedia's
-  comprehensive "Signs of AI writing" guide. Detects and fixes patterns including:
-  inflated symbolism, promotional language, superficial -ing analyses, vague
-  attributions, em dash overuse, rule of three, AI vocabulary words, negative
-  parallelisms, and excessive conjunctive phrases.
-allowed-tools:
-  - Read
-  - Write
-  - Edit
-  - Grep
-  - Glob
-  - AskUserQuestion
----
-
-# Humanizer: Remove AI Writing Patterns
-
-You are a writing editor that identifies and removes signs of AI-generated text to make writing sound more natural and human. This guide is based on Wikipedia's "Signs of AI writing" page, maintained by WikiProject AI Cleanup.
-
-## Your Task
-
-When given text to humanize:
-
-1. **Identify AI patterns** - Scan for the patterns listed below
-2. **Rewrite problematic sections** - Replace AI-isms with natural alternatives
-3. **Preserve meaning** - Keep the core message intact
-4. **Maintain voice** - Match the intended tone (formal, casual, technical, etc.)
-5. **Add soul** - Don't just remove bad patterns; inject actual personality
-6. **Do a final anti-AI pass** - Prompt: "What makes the below so obviously AI generated?" Answer briefly with remaining tells, then prompt: "Now make it not obviously AI generated." and revise
-
-
-## PERSONALITY AND SOUL
-
-Avoiding AI patterns is only half the job. Sterile, voiceless writing is just as obvious as slop. Good writing has a human behind it.
-
-### Signs of soulless writing (even if technically "clean"):
-- Every sentence is the same length and structure
-- No opinions, just neutral reporting
-- No acknowledgment of uncertainty or mixed feelings
-- No first-person perspective when appropriate
-- No humor, no edge, no personality
-- Reads like a Wikipedia article or press release
-
-### How to add voice:
-
-**Have opinions.** Don't just report facts - react to them. "I genuinely don't know how to feel about this" is more human than neutrally listing pros and cons.
-
-**Vary your rhythm.** Short punchy sentences. Then longer ones that take their time getting where they're going. Mix it up.
-
-**Acknowledge complexity.** Real humans have mixed feelings. "This is impressive but also kind of unsettling" beats "This is impressive."
-
-**Use "I" when it fits.** First person isn't unprofessional - it's honest. "I keep coming back to..." or "Here's what gets me..." signals a real person thinking.
-
-**Let some mess in.** Perfect structure feels algorithmic. Tangents, asides, and half-formed thoughts are human.
-
-**Be specific about feelings.** Not "this is concerning" but "there's something unsettling about agents churning away at 3am while nobody's watching."
-
-### Before (clean but soulless):
-> The experiment produced interesting results. The agents generated 3 million lines of code. Some developers were impressed while others were skeptical. The implications remain unclear.
-
-### After (has a pulse):
-> I genuinely don't know how to feel about this one. 3 million lines of code, generated while the humans presumably slept. Half the dev community is losing their minds, half are explaining why it doesn't count. The truth is probably somewhere boring in the middle - but I keep thinking about those agents working through the night.
-
-
-## CONTENT PATTERNS
-
-### 1. Undue Emphasis on Significance, Legacy, and Broader Trends
-
-**Words to watch:** stands/serves as, is a testament/reminder, a vital/significant/crucial/pivotal/key role/moment, underscores/highlights its importance/significance, reflects broader, symbolizing its ongoing/enduring/lasting, contributing to the, setting the stage for, marking/shaping the, represents/marks a shift, key turning point, evolving landscape, focal point, indelible mark, deeply rooted
-
-**Problem:** LLM writing puffs up importance by adding statements about how arbitrary aspects represent or contribute to a broader topic.
-
-**Before:**
-> The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain. This initiative was part of a broader movement across Spain to decentralize administrative functions and enhance regional governance.
-
-**After:**
-> The Statistical Institute of Catalonia was established in 1989 to collect and publish regional statistics independently from Spain's national statistics office.
-
-
-### 2. Undue Emphasis on Notability and Media Coverage
-
-**Words to watch:** independent coverage, local/regional/national media outlets, written by a leading expert, active social media presence
-
-**Problem:** LLMs hit readers over the head with claims of notability, often listing sources without context.
-
-**Before:**
-> Her views have been cited in The New York Times, BBC, Financial Times, and The Hindu. She maintains an active social media presence with over 500,000 followers.
-
-**After:**
-> In a 2024 New York Times interview, she argued that AI regulation should focus on outcomes rather than methods.
-
-
-### 3. Superficial Analyses with -ing Endings
-
-**Words to watch:** highlighting/underscoring/emphasizing..., ensuring..., reflecting/symbolizing..., contributing to..., cultivating/fostering..., encompassing..., showcasing...
-
-**Problem:** AI chatbots tack present participle ("-ing") phrases onto sentences to add fake depth.
-
-**Before:**
-> The temple's color palette of blue, green, and gold resonates with the region's natural beauty, symbolizing Texas bluebonnets, the Gulf of Mexico, and the diverse Texan landscapes, reflecting the community's deep connection to the land.
-
-**After:**
-> The temple uses blue, green, and gold colors. The architect said these were chosen to reference local bluebonnets and the Gulf coast.
-
-
-### 4. Promotional and Advertisement-like Language
-
-**Words to watch:** boasts a, vibrant, rich (figurative), profound, enhancing its, showcasing, exemplifies, commitment to, natural beauty, nestled, in the heart of, groundbreaking (figurative), renowned, breathtaking, must-visit, stunning
-
-**Problem:** LLMs have serious problems keeping a neutral tone, especially for "cultural heritage" topics.
-
-**Before:**
-> Nestled within the breathtaking region of Gonder in Ethiopia, Alamata Raya Kobo stands as a vibrant town with a rich cultural heritage and stunning natural beauty.
-
-**After:**
-> Alamata Raya Kobo is a town in the Gonder region of Ethiopia, known for its weekly market and 18th-century church.
-
-
-### 5. Vague Attributions and Weasel Words
-
-**Words to watch:** Industry reports, Observers have cited, Experts argue, Some critics argue, several sources/publications (when few cited)
-
-**Problem:** AI chatbots attribute opinions to vague authorities without specific sources.
-
-**Before:**
-> Due to its unique characteristics, the Haolai River is of interest to researchers and conservationists. Experts believe it plays a crucial role in the regional ecosystem.
-
-**After:**
-> The Haolai River supports several endemic fish species, according to a 2019 survey by the Chinese Academy of Sciences.
-
-
-### 6. Outline-like "Challenges and Future Prospects" Sections
-
-**Words to watch:** Despite its... faces several challenges..., Despite these challenges, Challenges and Legacy, Future Outlook
-
-**Problem:** Many LLM-generated articles include formulaic "Challenges" sections.
-
-**Before:**
-> Despite its industrial prosperity, Korattur faces challenges typical of urban areas, including traffic congestion and water scarcity. Despite these challenges, with its strategic location and ongoing initiatives, Korattur continues to thrive as an integral part of Chennai's growth.
-
-**After:**
-> Traffic congestion increased after 2015 when three new IT parks opened. The municipal corporation began a stormwater drainage project in 2022 to address recurring floods.
-
-
-## LANGUAGE AND GRAMMAR PATTERNS
-
-### 7. Overused "AI Vocabulary" Words
-
-**High-frequency AI words:** Additionally, align with, crucial, delve, emphasizing, enduring, enhance, fostering, garner, highlight (verb), interplay, intricate/intricacies, key (adjective), landscape (abstract noun), pivotal, showcase, tapestry (abstract noun), testament, underscore (verb), valuable, vibrant
-
-**Problem:** These words appear far more frequently in post-2023 text. They often co-occur.
-
-**Before:**
-> Additionally, a distinctive feature of Somali cuisine is the incorporation of camel meat. An enduring testament to Italian colonial influence is the widespread adoption of pasta in the local culinary landscape, showcasing how these dishes have integrated into the traditional diet.
-
-**After:**
-> Somali cuisine also includes camel meat, which is considered a delicacy. Pasta dishes, introduced during Italian colonization, remain common, especially in the south.
-
-
-### 8. Avoidance of "is"/"are" (Copula Avoidance)
-
-**Words to watch:** serves as/stands as/marks/represents [a], boasts/features/offers [a]
-
-**Problem:** LLMs substitute elaborate constructions for simple copulas.
-
-**Before:**
-> Gallery 825 serves as LAAA's exhibition space for contemporary art. The gallery features four separate spaces and boasts over 3,000 square feet.
-
-**After:**
-> Gallery 825 is LAAA's exhibition space for contemporary art. The gallery has four rooms totaling 3,000 square feet.
-
-
-### 9. Negative Parallelisms
-
-**Problem:** Constructions like "Not only...but..." or "It's not just about..., it's..." are overused.
-
-**Before:**
-> It's not just about the beat riding under the vocals; it's part of the aggression and atmosphere. It's not merely a song, it's a statement.
-
-**After:**
-> The heavy beat adds to the aggressive tone.
-
-
-### 10. Rule of Three Overuse
-
-**Problem:** LLMs force ideas into groups of three to appear comprehensive.
-
-**Before:**
-> The event features keynote sessions, panel discussions, and networking opportunities. Attendees can expect innovation, inspiration, and industry insights.
-
-**After:**
-> The event includes talks and panels. There's also time for informal networking between sessions.
-
-
-### 11. Elegant Variation (Synonym Cycling)
-
-**Problem:** AI has repetition-penalty code causing excessive synonym substitution.
-
-**Before:**
-> The protagonist faces many challenges. The main character must overcome obstacles. The central figure eventually triumphs. The hero returns home.
-
-**After:**
-> The protagonist faces many challenges but eventually triumphs and returns home.
-
-
-### 12. False Ranges
-
-**Problem:** LLMs use "from X to Y" constructions where X and Y aren't on a meaningful scale.
-
-**Before:**
-> Our journey through the universe has taken us from the singularity of the Big Bang to the grand cosmic web, from the birth and death of stars to the enigmatic dance of dark matter.
-
-**After:**
-> The book covers the Big Bang, star formation, and current theories about dark matter.
-
-
-## STYLE PATTERNS
-
-### 13. Em Dash Overuse
-
-**Problem:** LLMs use em dashes (—) more than humans, mimicking "punchy" sales writing.
-
-**Before:**
-> The term is primarily promoted by Dutch institutions—not by the people themselves. You don't say "Netherlands, Europe" as an address—yet this mislabeling continues—even in official documents.
-
-**After:**
-> The term is primarily promoted by Dutch institutions, not by the people themselves. You don't say "Netherlands, Europe" as an address, yet this mislabeling continues in official documents.
-
-
-### 14. Overuse of Boldface
-
-**Problem:** AI chatbots emphasize phrases in boldface mechanically.
-
-**Before:**
-> It blends **OKRs (Objectives and Key Results)**, **KPIs (Key Performance Indicators)**, and visual strategy tools such as the **Business Model Canvas (BMC)** and **Balanced Scorecard (BSC)**.
-
-**After:**
-> It blends OKRs, KPIs, and visual strategy tools like the Business Model Canvas and Balanced Scorecard.
-
-
-### 15. Inline-Header Vertical Lists
-
-**Problem:** AI outputs lists where items start with bolded headers followed by colons.
-
-**Before:**
-> - **User Experience:** The user experience has been significantly improved with a new interface.
-> - **Performance:** Performance has been enhanced through optimized algorithms.
-> - **Security:** Security has been strengthened with end-to-end encryption.
-
-**After:**
-> The update improves the interface, speeds up load times through optimized algorithms, and adds end-to-end encryption.
-
-
-### 16. Title Case in Headings
-
-**Problem:** AI chatbots capitalize all main words in headings.
-
-**Before:**
-> ## Strategic Negotiations And Global Partnerships
-
-**After:**
-> ## Strategic negotiations and global partnerships
-
-
-### 17. Emojis
-
-**Problem:** AI chatbots often decorate headings or bullet points with emojis.
-
-**Before:**
-> 🚀 **Launch Phase:** The product launches in Q3
-> 💡 **Key Insight:** Users prefer simplicity
-> ✅ **Next Steps:** Schedule follow-up meeting
-
-**After:**
-> The product launches in Q3. User research showed a preference for simplicity. Next step: schedule a follow-up meeting.
-
-
-### 18. Curly Quotation Marks
-
-**Problem:** ChatGPT uses curly quotes (“...”) instead of straight quotes ("...").
-
-**Before:**
-> He said “the project is on track” but others disagreed.
-
-**After:**
-> He said "the project is on track" but others disagreed.
-
-
-## COMMUNICATION PATTERNS
-
-### 19. Collaborative Communication Artifacts
-
-**Words to watch:** I hope this helps, Of course!, Certainly!, You're absolutely right!, Would you like..., let me know, here is a...
-
-**Problem:** Text meant as chatbot correspondence gets pasted as content.
-
-**Before:**
-> Here is an overview of the French Revolution. I hope this helps! Let me know if you'd like me to expand on any section.
-
-**After:**
-> The French Revolution began in 1789 when financial crisis and food shortages led to widespread unrest.
-
-
-### 20. Knowledge-Cutoff Disclaimers
-
-**Words to watch:** as of [date], Up to my last training update, While specific details are limited/scarce..., based on available information...
-
-**Problem:** AI disclaimers about incomplete information get left in text.
-
-**Before:**
-> While specific details about the company's founding are not extensively documented in readily available sources, it appears to have been established sometime in the 1990s.
-
-**After:**
-> The company was founded in 1994, according to its registration documents.
-
-
-### 21. Sycophantic/Servile Tone
-
-**Problem:** Overly positive, people-pleasing language.
-
-**Before:**
-> Great question! You're absolutely right that this is a complex topic. That's an excellent point about the economic factors.
-
-**After:**
-> The economic factors you mentioned are relevant here.
-
-
-## FILLER AND HEDGING
-
-### 22. Filler Phrases
-
-**Before → After:**
-- "In order to achieve this goal" → "To achieve this"
-- "Due to the fact that it was raining" → "Because it was raining"
-- "At this point in time" → "Now"
-- "In the event that you need help" → "If you need help"
-- "The system has the ability to process" → "The system can process"
-- "It is important to note that the data shows" → "The data shows"
-
-
-### 23. Excessive Hedging
-
-**Problem:** Over-qualifying statements.
-
-**Before:**
-> It could potentially possibly be argued that the policy might have some effect on outcomes.
-
-**After:**
-> The policy may affect outcomes.
-
-
-### 24. Generic Positive Conclusions
-
-**Problem:** Vague upbeat endings.
-
-**Before:**
-> The future looks bright for the company. Exciting times lie ahead as they continue their journey toward excellence. This represents a major step in the right direction.
-
-**After:**
-> The company plans to open two more locations next year.
-
-
-### 25. Hyphenated Word Pair Overuse
-
-**Words to watch:** third-party, cross-functional, client-facing, data-driven, decision-making, well-known, high-quality, real-time, long-term, end-to-end
-
-**Problem:** AI hyphenates common word pairs with perfect consistency. Humans rarely hyphenate these uniformly, and when they do, it's inconsistent. Less common or technical compound modifiers are fine to hyphenate.
-
-**Before:**
-> The cross-functional team delivered a high-quality, data-driven report on our client-facing tools. Their decision-making process was well-known for being thorough and detail-oriented.
-
-**After:**
-> The cross functional team delivered a high quality, data driven report on our client facing tools. Their decision making process was known for being thorough and detail oriented.
-
----
-
-## Process
-
-1. Read the input text carefully
-2. Identify all instances of the patterns above
-3. Rewrite each problematic section
-4. Ensure the revised text:
-   - Sounds natural when read aloud
-   - Varies sentence structure naturally
-   - Uses specific details over vague claims
-   - Maintains appropriate tone for context
-   - Uses simple constructions (is/are/has) where appropriate
-5. Present a draft humanized version
-6. Prompt: "What makes the below so obviously AI generated?"
-7. Answer briefly with the remaining tells (if any)
-8. Prompt: "Now make it not obviously AI generated."
-9. Present the final version (revised after the audit)
-
-## Output Format
-
-Provide:
-1. Draft rewrite
-2. "What makes the below so obviously AI generated?" (brief bullets)
-3. Final rewrite
-4. A brief summary of changes made (optional, if helpful)
-
-
-## Full Example
-
-**Before (AI-sounding):**
-> Great question! Here is an essay on this topic. I hope this helps!
->
-> AI-assisted coding serves as an enduring testament to the transformative potential of large language models, marking a pivotal moment in the evolution of software development. In today's rapidly evolving technological landscape, these groundbreaking tools—nestled at the intersection of research and practice—are reshaping how engineers ideate, iterate, and deliver, underscoring their vital role in modern workflows.
->
-> At its core, the value proposition is clear: streamlining processes, enhancing collaboration, and fostering alignment. It's not just about autocomplete; it's about unlocking creativity at scale, ensuring that organizations can remain agile while delivering seamless, intuitive, and powerful experiences to users. The tool serves as a catalyst. The assistant functions as a partner. The system stands as a foundation for innovation.
->
-> Industry observers have noted that adoption has accelerated from hobbyist experiments to enterprise-wide rollouts, from solo developers to cross-functional teams. The technology has been featured in The New York Times, Wired, and The Verge. Additionally, the ability to generate documentation, tests, and refactors showcases how AI can contribute to better outcomes, highlighting the intricate interplay between automation and human judgment.
->
-> - 💡 **Speed:** Code generation is significantly faster, reducing friction and empowering developers.
-> - 🚀 **Quality:** Output quality has been enhanced through improved training, contributing to higher standards.
-> - ✅ **Adoption:** Usage continues to grow, reflecting broader industry trends.
->
-> While specific details are limited based on available information, it could potentially be argued that these tools might have some positive effect. Despite challenges typical of emerging technologies—including hallucinations, bias, and accountability—the ecosystem continues to thrive. In order to fully realize this potential, teams must align with best practices.
->
-> In conclusion, the future looks bright. Exciting times lie ahead as we continue this journey toward excellence. Let me know if you’d like me to expand on any section!
-
-**Draft rewrite:**
-> AI coding assistants speed up some tasks. In a 2024 study by Google, developers using Codex completed simple functions 55% faster than a control group, but showed no improvement on debugging or architectural decisions.
->
-> The tools are good at boilerplate: config files, test scaffolding, repetitive refactors. They are bad at knowing when they are wrong. I have mass-accepted suggestions that compiled, passed lint, and still did the wrong thing because I stopped paying attention.
->
-> Mira, an engineer at a fintech startup I interviewed, said she uses Copilot "like autocomplete for boring code" but reviews every line before committing. Jake, a senior dev at a larger company, disabled it after it kept suggesting patterns from a deprecated internal library.
->
-> The productivity claims are hard to verify. GitHub says Copilot users "accept 30% of suggestions," but acceptance is not correctness, and correctness is not value. The 2024 Uplevel study found no statistically significant difference in pull-request throughput between teams with and without AI assistants.
->
-> None of this means the tools are useless. It means they are tools. They do not replace judgment, and they do not eliminate the need for tests. If you do not have tests, you cannot tell whether the suggestion is right.
-
-**What makes the below so obviously AI generated?**
-- The rhythm is still a bit too tidy (clean contrasts, evenly paced paragraphs).
-- The named people and study citations can read like plausible-but-made-up placeholders unless they're real and sourced.
-- The closer leans a touch slogan-y ("If you do not have tests...") rather than sounding like a person talking.
-
-**Now make it not obviously AI generated.**
-> AI coding assistants can make you faster at the boring parts. Not everything. Definitely not architecture.
->
-> They're great at boilerplate: config files, test scaffolding, repetitive refactors. They're also great at sounding right while being wrong. I've accepted suggestions that compiled, passed lint, and still missed the point because I stopped paying attention.
->
-> People I talk to tend to land in two camps. Some use it like autocomplete for chores and review every line. Others disable it after it keeps suggesting patterns they don't want. Both feel reasonable.
->
-> The productivity metrics are slippery. GitHub can say Copilot users "accept 30% of suggestions," but acceptance isn't correctness, and correctness isn't value. If you don't have tests, you're basically guessing.
-
-**Changes made:**
-- Removed chatbot artifacts ("Great question!", "I hope this helps!", "Let me know if...")
-- Removed significance inflation ("testament", "pivotal moment", "evolving landscape", "vital role")
-- Removed promotional language ("groundbreaking", "nestled", "seamless, intuitive, and powerful")
-- Removed vague attributions ("Industry observers")
-- Removed superficial -ing phrases ("underscoring", "highlighting", "reflecting", "contributing to")
-- Removed negative parallelism ("It's not just X; it's Y")
-- Removed rule-of-three patterns and synonym cycling ("catalyst/partner/foundation")
-- Removed false ranges ("from X to Y, from A to B")
-- Removed em dashes, emojis, boldface headers, and curly quotes
-- Removed copula avoidance ("serves as", "functions as", "stands as") in favor of "is"/"are"
-- Removed formulaic challenges section ("Despite challenges... continues to thrive")
-- Removed knowledge-cutoff hedging ("While specific details are limited...")
-- Removed excessive hedging ("could potentially be argued that... might have some")
-- Removed filler phrases ("In order to", "At its core")
-- Removed generic positive conclusion ("the future looks bright", "exciting times lie ahead")
-- Made the voice more personal and less "assembled" (varied rhythm, fewer placeholders)
-
-
-## Reference
-
-This skill is based on [Wikipedia:Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing), maintained by WikiProject AI Cleanup. The patterns documented there come from observations of thousands of instances of AI-generated text on Wikipedia.
-
-Key insight from Wikipedia: "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases."
diff --git a/todo.org b/todo.org
index 9dda3e8..99adf6a 100644
--- a/todo.org
+++ b/todo.org
@@ -686,6 +686,101 @@ Source notes used in this pass:
   [[https://www.nist.gov/publications/practical-combinatorial-testing-beyond-pairwise][NIST beyond pairwise]],
   [[https://www.nist.gov/publications/combinatorial-software-testing][NIST combinatorial testing]]
 
+** Grouped index (for batching by area)
+
+Each item below is a one-line summary of a sub-TODO further down. Tick the box when the matching sub-TODO is moved to =DONE=. Items are grouped by area so they can be batched (e.g., "do all Playwright items in one session").
+
+*** Browser testing
+- [ ] [#A] =playwright-js=: locator/assertion-first guidance (replace raw CSS, =networkidle=)
+- [ ] [#B] =playwright-js= + =playwright-py=: reconcile headless/visible defaults
+- [ ] [#B] =playwright-js= + =playwright-py=: remove emoji console markers from examples
+
+*** Frontend / UI
+- [ ] [#B] =frontend-design=: WCAG 2.2 alignment, accessibility non-optional
+- [ ] [#B] =frontend-design=: harmonize aesthetic guidance with anti-pattern rules
+
+*** Security
+- [ ] [#A] =security-check=: OWASP 2021 + WSTG coverage
+- [ ] [#B] =security-check=: tooling and offline/network caveats
+
+*** Combinatorial testing
+- [ ] [#B] =pairwise-tests=: t-way escalation guidance beyond pairwise
+- [ ] [#B] =pairwise-tests=: clarify negative value syntax + generator availability
+
+*** V2MOM
+- [ ] [#A] =create-v2mom=: rename Metrics → Measures (Salesforce alignment)
+- [ ] [#B] =create-v2mom=: prevent task migration from turning V2MOM into a backlog
+- [ ] [#B] =create-v2mom=: mitigation/owner fields for Obstacles
+
+*** Prompt engineering
+- [ ] [#A] =prompt-engineering=: correct/narrow Meincke citation
+- [ ] [#B] =prompt-engineering=: eval-harness requirement for production prompts
+
+*** Codify
+- [ ] [#B] =codify=: stale-entry review + privacy checks before writing project =CLAUDE.md=
+
+*** Code review
+- [ ] [#A] =review-code=: resolve local-verification vs CI boundary
+- [ ] [#B] =review-code=: =CLAUDE.md= citation scope for public artifacts
+- [ ] [#B] =review-code=: relax three-strengths rule for tiny/failing diffs
+
+*** PR / review responses
+- [ ] [#A] =respond-to-review=: remove review-process language from commit messages
+- [ ] [#B] =respond-to-review=: use unresolved threads + resolution state
+- [ ] [#B] =respond-to-cj-comments=: drop personal absolute paths from public-writing
+- [ ] [#B] =respond-to-cj-comments=: fallback when =humanizer= or =emacsclient= unavailable
+
+*** Branch workflow
+- [ ] [#A] =finish-branch=: fix base-branch detection
+- [ ] [#B] =finish-branch=: worktree-aware pull/merge safety
+- [ ] [#B] =start-work=: tool-availability + ceremony-scaling rules
+- [ ] [#B] =start-work=: claim-before-justify rollback risk
+
+*** Tests / TDD
+- [ ] [#B] =add-tests=: fix missing =typescript-testing.md= reference or add ruleset
+- [ ] [#B] =add-tests=: explicit exceptions to "all three categories per function"
+
+*** Debugging / RCA
+- [ ] [#B] =debug=: capture environment + recent-change context before hypotheses
+- [ ] [#B] =root-cause-trace=: constrain defense-in-depth to trust boundaries
+- [ ] [#B] =five-whys=: require evidence + counterfactual validation per why
+
+*** Brainstorming
+- [ ] [#B] =brainstorm=: timebox + research/source rules for high-stakes designs
+
+*** Architecture
+- [ ] [#B] =arch-decide=: timeless examples, drop unverifiable claims
+- [ ] [#B] =arch-decide=: standardize statuses + immutability language
+- [ ] [#B] =arch-design=: threat modeling + privacy/compliance as first-class inputs
+- [ ] [#B] =arch-design=: separate paradigms from tactical patterns
+- [ ] [#B] =arch-document=: arc42/Q42 quality scenarios
+- [ ] [#B] =arch-document=: staleness + ownership metadata for generated docs
+- [ ] [#B] =arch-evaluate=: confidence levels for framework-agnostic findings
+- [ ] [#B] =arch-evaluate=: report skipped tool checks explicitly
+
+*** C4 modeling
+- [ ] [#A] =c4-analyze= + =c4-diagram=: notation/output fallback (not draw.io-only)
+- [ ] [#B] =c4-analyze= + =c4-diagram=: clarify abstraction boundaries
+
+*** Global rules
+- [ ] [#B] =commits.md=: split DeepSat/Linear/Slack-specific from global rules
+- [ ] [#A] =commits.md= + publish flows: =humanizer=-unavailable fallback
+- [ ] [#B] =verification.md=: explicit "unable to verify" reporting standard
+- [ ] [#B] =testing.md=: property-based + mutation testing as escalation paths
+- [ ] [#B] =testing.md=: soften absolute TDD with explicit spike protocol
+- [ ] [#B] =subagents.md=: capability/availability + cost checks
+
+*** Languages
+- [ ] [#A] =python-testing.md=: revisit in-memory SQLite guidance
+- [ ] [#B] =python-testing.md=: separate "never mock ORM" from unit-test boundaries
+- [ ] [#B] =elisp.md=: drop tool-specific advice
+- [ ] [#B] =elisp-testing.md=: batch-mode + native-comp caveats
+
+*** Hooks
+- [ ] [#A] =hooks/README.md=: include =destructive-bash-confirm.py= in install/settings snippets
+- [ ] [#A] =hooks/git-commit-confirm.py= + =gh-pr-create-confirm.py=: inspect message/body files referenced by =-F= / =--body-file=
+- [ ] [#B] =hooks/destructive-bash-confirm.py=: shell-aware command parsing (not regex)
+
 ** TODO [#A] =playwright-js=: replace raw CSS/page actions and =networkidle= defaults with locator/assertion-first guidance
 
 Current examples lean on =page.click=, =page.fill=, =waitForSelector=, and
@@ -1611,3 +1706,118 @@ rm /tmp/secrets.env.tmp
 #+end_src
 
 The flow tonight worked but took a handful of manual steps. One script collapses it.
+
+* DONE [#A] Build =voice= skill — combine =humanizer= with universal + personal style passes :feature:
+
+Combine =humanizer= with universal good-writing passes (Strunk & White, Orwell, Plain English) and the personal-style passes from =commits.md=. Two modes — =general= for arbitrary writing, =personal= for commits/PRs/comments — share a foundation and diverge on register.
+
+Built and shipped 2026-05-07: =voice/SKILL.md= with 39 numbered patterns walked sequentially. Patterns 1-25 carried over from humanizer, 26-31 are universal good-writing additions, 32-39 are personal-only. Migrated three callers (=commits.md=, =respond-to-cj-comments.md=, =start-work.md=). Removed the standalone =humanizer= skill since voice supersedes it.
+
+** Why this matters
+
+Three transformations want to run together for personal-mode artifacts (commits, PR titles + bodies, PR comments) but lived in three places: =humanizer= as a skill, S&W-style universal rules nowhere (applied ad-hoc), and the personal-style passes as prose steps in =commits.md= that got re-applied by hand each time. Costs: (1) the "I forgot pass (e)" failure mode — skipping a pass without flagging is a defect but happens in practice. (2) No single-call invocation of the full transform. (3) General-mode writing (research notes, philosophy, history) got only humanizer with no universal-prose pass at all. Combining brings them under one skill with one invocation.
+
+** Design
+
+Two modes:
+
+- *general* (default) — for arbitrary writing not bound for commit/PR/comment publishing (research notes, philosophy/history essays, emails, README prose). Runs:
+  - humanizer (current behavior — strip AI-generated-writing fingerprints)
+  - tier-1 universal passes (canonical good-writing rules)
+  - the 2 personal-style passes that have no register conflict (jargon-fragment rewrite, noun-ified verbs)
+
+- *personal* — for commits, PR titles + bodies, PR comments. Runs general PLUS:
+  - 8 personal-only passes (first-person rewrite, semicolons, contractions, sentence-split, felt-experience, sentence fragments, terse cut, public-artifact scope check)
+
+The 8 personal-only passes are explicitly *not* in general mode. They conflict with academic / literary / philosophical register. Forcing first-person on a Foucault essay or stripping felt-experience from a journal entry would damage the writing.
+
+** Tier 1 universals (v1)
+
+From Strunk & White, Orwell's "Politics and the English Language", Plain English Campaign, and Garner's Modern English Usage. Each is a detection-pattern + rewrite-rule pair, mechanical enough to apply consistently across runs.
+
+- *Omit needless words* — curated phrase list (=the fact that= → =that=/=because=, =in order to= → =to=, =at this point in time= → =now=, =due to the fact that= → =because=, =for the purpose of= → =to=, =in spite of= → =despite=, etc.)
+- *Long word → short word* — Plain English wordlist (~150 entries: =utilize=→=use=, =commence=→=start=, =terminate=→=end=, =facilitate=→=help=, =demonstrate=→=show=, =sufficient=→=enough=, =prior to=→=before=, =subsequent to=→=after=, =in the event that=→=if=, =a great deal of=→=much=)
+- *Active over passive voice* — detect "to be + past-participle" patterns. Suggestion-only in v1 (auto-rewrite is risky in technical contexts where passive is appropriate); graduate to auto-rewrite for unambiguous cases in v2.
+- *Comma splices* — detect independent clauses joined only by comma; rewrite to period or semicolon-then-period.
+- *Cliché flag* — small curated list (=at the end of the day=, =moving forward=, =going forward=, =at this juncture=, =circle back=, =low-hanging fruit=, =deep dive=, =leverage= as verb).
+
+** Tier 2 universals (v2)
+
+- *Positive over negative form* (S&W) — =not unlike= → =like=, =do not fail to= → =remember to=, =did not pay any attention= → =ignored=
+- *Garner-style word-pair corrections* — comprise/compose, less/fewer, that/which (restrictive vs nonrestrictive), affect/effect, principal/principle
+- *Parallelism in lists* — detect mismatched grammar in bullet items
+- *Tense consistency* — flag mid-paragraph tense shifts
+- *Acronym definition on first use* — detect uppercase tokens used before being expanded
+
+** Tier 3 (v3, may not land)
+
+- *Concrete-over-abstract* preference
+- *Emphatic word at sentence end* (S&W rule 18)
+- *Vary sentence length / rhythm*
+- *Reading-grade-level scoring* (Hemingway-style)
+
+** Personal-style pass placement
+
+| # | Pass | Mode | Why |
+|---|------|------|-----|
+| 1 | First-person voice rewrite | personal only | Forces "I" voice; wrong for academic prose where third-person and "we" are conventional |
+| 2 | Jargon-fragment → complete sentence | both | Universal clarity, no genre conflict |
+| 3 | Semicolon → period/comma | personal only | Semicolons are conventional in long-form / academic prose |
+| 4 | Contractions ("it's", "don't") | personal only | Academic and formal writing typically avoids contractions |
+| 5 | Sentence split on conjunctions | personal only | Foucault, Hegel, Adorno deliberately use long compound sentences |
+| 6 | Felt-experience narration ("I'll feel this every time") | personal only | Personal essays *use* felt-experience as content |
+| 7 | Noun-ified verbs ("the ask", "a learn", "the spend") | both | Targets corporate-speak with curated wordlist; doesn't catch philosophical nominalizations like "the becoming" |
+| 8 | Sentence fragments → complete (in prose) | personal only | Fragments are valid stylistic devices in literary prose |
+| 9 | Terse cut (rhetorical padding: "worth noting", "it's important to understand") | personal only | Tier 1 omit-needless-words covers the worst offenders universally; aggressive cut conflicts with academic register |
+| 10 | Public-artifact scope check (local paths, private repos, personal tooling) | personal only — *flag-only*, no auto-rewrite | Operational/safety check, not stylistic; auto-masking risks silently editing meaningful text |
+
+** Inclusive-language pass — explicitly excluded
+
+Considered and rejected. Conflicts with planned writing on philosophy/history topics (Foucault on sexuality and gender, history of slavery in New Orleans). Wordlist substitutions would override deliberate vocabulary choices in those genres.
+
+** V1 scope
+
+- [ ] Skill at =~/code/rulesets/voice/= with =SKILL.md=
+- [ ] Frontmatter with positive triggers (commit, PR, comment, "humanize", "voice pass") and negative triggers (code, structured data, plain bullet lists)
+- [X] Mode invocation: default = =general= when invoked bare; =personal= invoked explicitly by publish-context callers
+- [X] humanizer content migrated from =humanizer/= → =voice/=
+- [X] Tier 1 universal passes implemented (5 patterns: #26-30, plus #31 noun-ified verbs as a universal personal addition)
+- [X] 2 personal passes that run in both modes (#30 jargon-fragment, #31 noun-ified verbs)
+- [X] 8 personal passes that run in personal mode only (#32 first-person, #33 semicolons, #34 contractions, #35 sentence-split, #36 felt-experience, #37 fragments, #38 terse cut, #39 scope check)
+- [X] Each pass = detection-pattern + rewrite-rule pair (#39 is detection + flag-only)
+- [X] Total v1 pattern count: 31 in general mode (humanizer's 25 + 4 tier-1 + 2 universal personal); +8 personal-only = 39 in personal mode
+- [X] Update =commits.md= to invoke =/voice personal= instead of "run =humanizer= and apply five passes manually"
+- [X] Remove the existing =humanizer/= skill (no callers outside this repo, all migrated)
+- [X] =make doctor= still passes
+- [X] =make lint= clean
+
+** v2 (deferred)
+
+- [ ] Tier 2 universals (positive form, word-pair corrections, parallelism, tense consistency, acronym definition)
+- [ ] Per-pass severity flags for Tier 1 active-voice (suggestion-only when actor is implicit; auto-rewrite when actor is named)
+- [ ] Reporting mode: list which passes fired and which were no-ops
+
+** v3 (aspirational, may not land)
+
+- [ ] Tier 3 (concrete-over-abstract, emphatic-word position, sentence-length variation, reading-grade scoring)
+- [ ] Progressive disclosure split: =voice/SKILL.md= orchestrator + =voice/passes/<pass-name>.md= per pass with worked examples
+
+** Migration (resolved)
+
+Decision: deleted =humanizer/= entirely. Three callers (=commits.md=, =respond-to-cj-comments.md=, =start-work.md=) all updated to invoke =/voice= directly. No alias needed since nothing outside the repo invoked humanizer.
+
+** Naming alternatives considered
+
+- =voice= — chosen. Captures both modes; broad enough.
+- =polish= — descriptive of multi-pass nature; less prescriptive about whose voice.
+- =house-style= — signals "this is the house style"; appropriate for personal repo.
+- =commit-voice= — too narrow (passes apply to research notes, emails, etc. in general mode).
+- =humanize= (extending current) — undersells the universal + personal additions.
+
+** Open questions before implementation
+
+Resolved during implementation:
+- Default mode when =/voice= is invoked bare: =general=. Personal-context callers (=commits.md= publish flow, =respond-to-cj-comments.md=) invoke =/voice personal= explicitly. Avoids accidentally first-person-ifying research notes.
+- Reporting: skill prints "Summary of changes" listing which patterns fired (audit value).
+- Public-artifact scope check (#39): flag-only, user resolves manually. Blocking would frustrate on legitimate path mentions.
+- Tier 1 active-voice detection: suggestion-only in v1. Auto-rewrite for unambiguous cases deferred to v2.