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 tell-them-something-newgit 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/tell-them-something-new)<a href="https://agentmods.dev/skills/kalyvask/winning-writing/tell-them-something-new"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/tell-them-something-new/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/tell-them-something-new"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/tell-them-something-new.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 | $0.00130 | $0.01875 |
| Opus 5 | $0.00065 | $0.00937 |
| Sonnet 5 | $0.00026 | $0.00375 |
| Haiku 4.5 | $0.00013 | $0.00187 |
Grade A, and why
tell-them-something-new 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 11d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tell them something new
Source: points/cold-email-rules.md rule 2, points/core-rules.md rule 4, Konrad's "tell me a secret about the future" guidance.
The premise
The most-failed sentence in cold writing is the opener. The default move is to recap something the recipient already knows: their job, their accomplishments, their stated thesis, their company's recent press release. They lived it. Telling them about themselves wastes the 15 seconds you have.
The fix isn't a different recap. The fix is a sentence that contains new information — a secret about the future, a number they don't have, a contradiction in their own data, a connection between two things they hadn't linked.
What "they already know" looks like
These are the seven flavors of opener-failure. Cut them all:
1. Flattery about their accomplishments
❌ "You've transformed industry after industry — Google Maps, the Like button, Salesforce, OpenAI."
He knows. He lived it. The reader's first thought is: get to the point.
2. Their own stated thesis, recited back
❌ "I've been thinking a lot about your point that 'agents are the new product surface' — it really resonated."
He said it. He doesn't need to hear it again, with adverbs.
3. Public biographical recap
❌ "As the CEO of a $2B AI company and the chair of OpenAI's board…"
His title is in his email signature. His company's valuation was the lede of last week's TechCrunch piece. Useless.
4. Recent news they were the subject of
❌ "Congratulations on the Fragment acquisition!"
He was on the press release. The all-hands was Tuesday. "Congratulations on X" is what every other cold email opens with that week.
5. Generic industry truisms
❌ "As we all know, AI is transforming enterprise software."
True for everyone, news to no one. Dead on arrival.
6. Self-introduction
❌ "My name is
[your name]and I'm a[your school / role]passionate about AI."
His email client put your name and address at the top of the message. The introduction is structural, not sentence-level.
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.
- 11d ago First seen · 154 lines · 130 tokens per session scan A a1dc65639ff6
tell-them-something-new is a skill published in the GitHub repository kalyvask/winning-writing (13 stars, last pushed 6d ago), licensed MIT. It adds 130 tokens to every session and 1,875 once invoked, about $0.0006 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.
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.
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.
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.