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 kalyvask/winning-writing --skill vividnessgit 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/vividness)<a href="https://agentmods.dev/skills/kalyvask/winning-writing/vividness"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/vividness/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/kalyvask/winning-writing/vividness"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/vividness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00183 | $0.02989 |
| Opus 5.5 | $0.00073 | $0.01196 |
| Sonnet 5.5 | $0.00037 | $0.00598 |
| Haiku 4.5 | $0.00018 | $0.00299 |
Grade A, and why
vividness 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.
How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vividness
Source: points/core-rules.md rule 2 (know your audience), Kramon's rule that stories are 12× more memorable than statistics alone, Lauren Weinstein's guest lecture in Glenn Kramon's Winning Writing (GSB, Spring 2026).
What this skill does
Two modes behind one skill. Both push abstract → concrete; they operate at different scales:
| Mode | Replaces | Use when |
|---|---|---|
| noun-level | "dog" → "German shepherd," "customer" → "Sarah at JPMorgan," "many" → "47 of 100" | Draft has category nouns the reader has to fill in |
| scene-level | "I was angry" → body signal + room + dialogue + moment | Story is technically correct but flat |
Default --mode both runs noun-level first (fixes generic words), then scene-level (turns key moments into scenes). The order matters: noun replacements provide the concrete material a scene needs.
How to invoke
/vividness "draft text"
/vividness --mode noun-level "draft text"
/vividness --mode scene-level "draft text"
/vividness --mode both "draft text"
Without --mode, default to both.
A note on examples
Named "Sarah at JPMorgan" and "Priya at DoorDash" cases below are fictional people in fictional scenarios at real public companies. They exist as teaching shapes — named person + named institution + specific quote — not as anyone's real story. The real version of this skill uses real names.
Mode 1 — noun-level
Generic nouns force the reader to do work the writer should have done.
"A scientist studied dogs."
Forgettable. The reader pictures nothing.
"Dr. Chen at UC Davis studied 47 German shepherds at a sheep-herding farm in Petaluma."
The reader can see it. Specificity is the single highest-leverage rewrite a draft ever gets.
The replacement table
| Generic | Specific |
|---|---|
| dog | German shepherd, Alma my golden retriever, the puppy at the rescue |
| engineer | John, the SRE on the payments team |
| customer | Sarah at JPMorgan's options-trading desk |
| city | Bozeman (population 56,000, mountain west) |
| big company | Stripe (~7,000 employees, $1T+ payment volume annually) |
| recent study | the 2024 Stanford HAI paper on agent reliability |
| many people | 47 of the 100 PMs I interviewed |
| a long time | 38 minutes |
| somewhere | the Fillmore in 1989 (named venue, named year) |
| early | 2:47 a.m. last Wednesday |
| executives | the VP of Talent at Stripe |
| consultant | a McKinsey BA who left to start a SaaS company |
| AI tool | Claude Sonnet 4.6 with the web_search tool |
| school | Stanford GSB's section H |
| job | director of products at a 20-person fintech in Austin |
| money | $64M in net savings |
| feedback | "this is good but the part about latency is wrong" |
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.
- 1mo ago First seen · 265 lines · 183 tokens per session scan A eda8f100685e
vividness is a skill published in the GitHub repository kalyvask/winning-writing (16 stars, last pushed 1mo ago), licensed MIT. It adds 183 tokens to every session and 2,989 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 2026-08-30.
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