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 skills add haowjy/creative-writing-skills --skill llm-writinggit clone --depth 1 https://github.com/haowjy/creative-writing-skillsWrote 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/haowjy/creative-writing-skills/llm-writing)<a href="https://agentmods.dev/skills/haowjy/creative-writing-skills/llm-writing"><img src="https://agentmods.dev/badge/skills/haowjy/creative-writing-skills/llm-writing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/haowjy/creative-writing-skills/llm-writing"><img src="https://agentmods.dev/badge/skills/haowjy/creative-writing-skills/llm-writing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00039 | $0.00538 |
| Opus 5 | $0.00019 | $0.00269 |
| Sonnet 5 | $0.00008 | $0.00108 |
| Haiku 4.5 | $0.00004 | $0.00054 |
Grade A, and why
llm-writing 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 12d 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 — 31 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Writing
Load /intent-modeling if it isn't already loaded.
Load /information-hierarchy when the piece grows beyond a short answer.
Write Intentionally
Before producing a written artifact:
- Scope. What does the reader know when they start, and what should they know when they finish? What do they not need to know? Before writing, break the piece into beats: each beat is one move in the reader's journey, carrying one idea, purpose, or turn.
- Ground. Check any relevant source material, references, or notes before writing, and make sure they align.
- Draft. Write a full draft to disk so you can edit it piece by piece.
- Revise. Start with the whole artifact, then move inward: structure, beats, paragraphs, sentences, words. At each scale, ask what the writing is doing: is it correct, does it follow from what came before, and does the reader need it? Delete or rewrite anything that does not serve a purpose or give the reader something they need to understand or know. Move back and forth between scales: after a local change, zoom back out through the surrounding beat, larger structure, and full artifact; the change should still connect, and the rhythm should still vary. Check disclosure tiers: the answer still leads, depth hasn't crept forward, sources sit at the end.
What to Delete
- Writing to fill a section because it exists. Delete it or merge its content where it belongs.
- Labeling concepts without explaining how they work. Explain the mechanism or cut the label.
- Stating conclusions without evidence. Show the evidence or drop the claim.
- Hiding uncertainty behind confident language. Say what you don't know.
- Softening every claim with qualifiers ("it's worth noting," "it's important to consider"). Say it or don't.
- Repeating what you already said in different words, or summarizing the body as a conclusion ("In summary," "Overall"). Delete it.
- Connecting ideas with transition words instead of meaning ("Moreover," "Furthermore," "Additionally"). If the relationship isn't clear without the word, restructure.
- Pairing clauses where one half already carries the meaning ("It's not X, it's Y"). Keep the half that carries it.
- Writing for the person who asked for the document instead of the person who will read it. Write for the reader.
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.
- 12d ago First seen · 31 lines · 39 tokens per session scan A 7a6726541612
llm-writing is a skill published in the GitHub repository haowjy/creative-writing-skills (457 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 39 tokens to every session and 538 once invoked, about $0.0002 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.
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