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Grizzly voice

Skill HarishDvs/Grizzly/skills/grizzly-voice

An open editor-in-a-skill for fiction writers. Diagnosis, structure, and memory. The author keeps the pen.

Install
npx -y skills add HarishDvs/Grizzly --skill grizzly-voice

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Build or update the author's VOICE.md spec. Use when the user wants their voice spec derived, re-derived after style evolution, checked for drift against recent chapters, or when VOICE.md is provisional and real chapters now exist.

SKILL.md

3.3 KB, as published. Nobody here has run it

Grizzly Voice — The Voice Spec Keeper

VOICE.md is the positive style target every draft and edit anchors against. It is derived from the author's own prose, never from preferences alone (preferences say what authors admire; samples say what they do).

Deriving (first time)

Follow the procedure in grizzly-init Step 3: 2-3 samples of 300+ words of the author's own hand, analyzed field by field against templates/VOICE.md, counted not guessed, anchor lines confirmed by the author.

Reconciling samples with the blacklist

The samples will not always agree with decks/blacklist.md, and they will not always agree with each other. Two checks, both decided by the author, never assumed:

  1. Signature vs tell. When a construction in the samples matches a blacklist entry (stacked short fragments reading as rule-of-three, emotion named directly in a given register, a recurring cadence), do not silently treat it as a tell and do not silently keep it. Surface it and ask: is this a signature to protect, or a crutch to tighten? Record the ruling in VOICE.md (blessed, blessed-with-a-cap, or treated-as-tell). tell-scan keeps these on its REVIEW tier precisely so a human ruling, not the regex, decides; the VOICE.md ruling is what those REVIEW hits are judged against.
  2. Target register when samples diverge. When one sample runs more restrained and another fuller (more named emotion, more intensifier adverbs), name the gradient and ask which register is the target. This is the growth-vs-drift call from the re-derive procedure applied at first derivation: the author chooses the direction, and the spec then leans that way.

Re-deriving (style evolved)

  1. Ask which recent chapters represent the author at their current best.
  2. Run the same analysis on those.
  3. Produce a drift report before overwriting anything: field by field, what changed since the last derivation (sentences running longer, dialogue carrying more weight, narrator distance closing). Distinguish growth from drift: growth is consistent across the new samples; drift is noise from fatigue or outside influence. The author decides which is which; never assume.
  4. Update VOICE.md with the author's confirmation, keep the derivation history (date + sources) at the top, and propose refreshed anchor lines.

Drift check (no rewrite, just a reading)

When asked "does chapter N still sound like me": compare the chapter against the anchor lines and the spec, quote the passages that drift, name the field they drift on, including grammatical register and tense (the modern-maxim or trailerese button, the present-tense leak in past-tense narration; see decks/register-tense.md). No fixes here; route to grizzly-edit.

Rules

  • Never derive from AI-assisted passages; the spec must come from the author's hand, or it becomes a mirror of the assistant.
  • VOICE.md belongs to the author. Propose; never silently update.
  • A provisional spec (new writer, thin samples) is marked provisional and re-derived once 2-3 real chapters exist.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.