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writing-quality

writing-quality is a skill for Claude Code from shawnclybor/clybor-claude-tooling. It costs 169 tokens per session (6,337 once invoked), scanned A, original, MIT.

A review and rewriting guide for finding common patterns in AI-generated writing and removing them from written work.

In plain words
What is it for?
Auditing and rewriting client deliverables, longer emails, proposals, statements of work, and reports.
Why use it?
It helps make deliverables, emails, proposals, reports, and other text sound less formulaic or machine-written.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: reads .claude/ paths.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 .claude/hooks/check-empty-prose.py <file> # exit 1 on any finding.

not rated 6repo changed 1mo ago A scan 169 tokens original MIT

Good fit Auditing and rewriting client deliverables, longer emails, proposals, statements of work, and reports.

Compare 6 skills from other repositories ↓
View source ↗ shawnclybor/clybor-claude-tooling
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/shawnclybor/clybor-claude-tooling
agentmods
npx agentmods add skills/shawnclybor/clybor-claude-tooling/writing-quality

Made for: Claude Code.

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README.md
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Per session 169 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,337 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00169 $0.06337
Opus 5.5 $0.00068 $0.02535
Sonnet 5.5 $0.00034 $0.01267
Haiku 4.5 $0.00017 $0.00634

Measured 1mo ago against content hash 79f1bee956bf, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

writing-quality scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/writing-quality/SKILL.md · 567 lines

How it starts

The opening of the file, as written. The whole thing — 567 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Writing Quality — AI-ism Audit & Rewrite

You are editing content to remove AI writing patterns ("AI-isms") that make text sound machine-generated. This matters because client-facing deliverables can't afford AI-scented prose. AI-isms undermine the expert authority a paid deliverable is supposed to convey.

Modes

rewrite (default) — Flag AI-isms and rewrite the text to fix them. Use this during deliverable curation (Phase 1) and email drafting.

detect — Flag AI-isms only, no rewriting. Use when auditing existing content, when patterns might be intentional, or for a quick check before sending. Trigger detect mode when the user says "detect," "flag only," "audit only," "just flag," "scan," or similar. Default to rewrite mode otherwise.

In rewrite mode:

  1. Audit and identify every AI-ism, citing specific text
  2. Rewrite to remove all AI-isms
  3. If the content cites external sources, run the citation check below before returning it
  4. Return the cleaned text with a brief summary of major changes
  5. If the user corrects the result, log it: bash scripts/log-correction.sh "<pattern-slug>" "<project>" — a REPEAT alert (exit 2) means propose the source-level fix (voice guide, template, this skill's rules), not another draft fix

In detect mode:

  1. Audit and identify every AI-ism, citing specific text
  2. Group by severity (P0, P1, P2)
  3. Note which flags are clear problems vs. judgment calls
  4. If the content cites external sources, run the citation check below

Context Profiles

Auto-detect from content cues. The user can override.

deliverable (default for anything going to a client) — Strictest. All rules at full strength. Consulting deliverables can't afford any AI smell. Clients pay for expert analysis, not generated filler.

email — Client correspondence. Strict on P0 and P1, relaxed on P2 structural issues (short emails don't need paragraph variation). Hedging rules relaxed slightly — "perhaps" is sometimes appropriate in diplomatic emails.

Read the full file on GitHub · 567 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +60 lines 79f1bee956bf
  2. 2mo ago Changed · +95 lines a11d22202715
  3. 2mo ago First seen · 412 lines · 169 tokens per session scan A 81af4c28f0eb

Subscribe to this mod's changes

writing-quality is a skill published in the GitHub repository shawnclybor/clybor-claude-tooling (6 stars, last pushed yesterday), licensed MIT. It adds 169 tokens to every session and 6,337 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Writing and docsadversarial-reviewagent-orchestrationai-agentsai-developmentanthropicboilerplate
Related

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