Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/kalyvask/winning-writing/humanizenpx skills add kalyvask/winning-writing --skill humanizegit clone --depth 1 https://github.com/kalyvask/winning-writingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kalyvask/winning-writing/humanize)<a href="https://agentmods.dev/skills/kalyvask/winning-writing/humanize"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/humanize.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.02000 |
| Opus 5 | $0.00055 | $0.01000 |
| Sonnet 5 | $0.00022 | $0.00400 |
| Haiku 4.5 | $0.00011 | $0.00200 |
Grade A, and why
humanize 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize
Source: points/ai-writing-rules.md and the centaur-writer thesis. The point of this skill is the inverse of every other skill in this repo — most of them sharpen, this one de-sharpens deliberately.
Operator note (2026-05-07): Dialed back from the original aggressive setting (too many typos and missing words landed in finals), then re-tuned for a middle ground: a few safe roughening moves are still welcome even in short pieces. The bias is "fewer types of typos, but still some texture" — not "skip everything."
The premise
A perfect email is suspicious. Real people:
- Use contractions inconsistently ("it's" once, "it is" once in the same email — humans aren't consistent)
- Vary sentence punctuation (sometimes a period where a comma would be cleaner)
- Use parentheses inconsistently
- Repeat a word from the previous sentence sometimes (real attention drift)
A model output is too clean. This skill leaves a small amount of real-person residue in. Note: residue, not damage. Missing required words and unsafe typos are damage; do not introduce them.
Two modes
Mode 1 — Shorten + roughen (default)
Take the draft and:
- Cut 10–20% of the words
- Convert most "I am / it is / they are / cannot" to contractions, but mix in one full form somewhere for inconsistency
- Drop the subject pronoun in one casual opener if the draft has one (never drop articles)
- Vary one sentence's punctuation in a slightly imperfect way (a period instead of a comma; a sentence fragment)
- Optionally combine two short adjacent paragraphs into one (or keep an aside on the same line instead of breaking) — humans don't always hit return where AI does
- Apply at most ONE safe typo (see below). Pieces under ~150 words: max 1 typo. Pieces over 300 words: still max 2 typos total. Never accumulate.
Mode 2 — Roughen only (preserve length)
Same as above, no length cut.
Safe roughening moves (use freely, even in short pieces)
These are not typos — they are rhythm choices that read as a real person rather than a model. Apply 1–2 per piece without worry:
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.
- 6d ago First seen · 139 lines · 109 tokens per session scan A 4a014d647633
humanize is a skill published in the GitHub repository kalyvask/winning-writing (13 stars, last pushed today), licensed MIT. It adds 109 tokens to every session and 2,000 once invoked, about $0.0005 per session on Opus 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 2026-08-30.
Other skills, from other repositories
project-memory
Generate a project-specific context file from a brief so an AI assistant remembers your editorial constraints, voice, audience, and quality bar across sessions.
project-retrospective
Generate a LESSONS.md from a finished project: what worked, what didn't, what to reuse, what to retire — formatted for next-project carry-over.
template-selector
Recommend the right skill bundle, agent, and workflow sequence for a new project — so media professionals can start producing instead of browsing a 394-skill library.
jargon-flagger
Scans a draft and flags every instance of technical jargon, unexplained acronyms, and insider language that a general-audience reader would not understand — with a plain-language alternative for each.
multi-author-harmonizer
Reviews a text written or assembled by multiple authors and produces a detailed inconsistency report — flagging voice shifts, terminology mismatches, tonal clashes, and formatting discrepancies — with specific harmonisation recommendations for each.
passive-voice-checker
Identifies every passive-voice construction in a draft, assesses whether each weakens or serves the prose, and offers active-voice alternatives for those that should be changed — while leaving justified passives alone.