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 op-ed-coachgit 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/op-ed-coach)<a href="https://agentmods.dev/skills/kalyvask/winning-writing/op-ed-coach"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/op-ed-coach/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/op-ed-coach"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/op-ed-coach.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00113 | $0.01008 |
| Opus 5 | $0.00056 | $0.00504 |
| Sonnet 5 | $0.00023 | $0.00202 |
| Haiku 4.5 | $0.00011 | $0.00101 |
Grade A, and why
op-ed-coach 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Op-ed coach
Source: points/frameworks.md (op-ed structure section), points/kramon-master.md section VII, points/examples-and-critiques.md.
The three-element rule (Kramon's tightest framing)
Every op-ed has three elements, in this order:
- Story. A specific scene with one character. Date, place, sensory detail. Open here.
- Statistics. A few — not many. Two or three numbers that prove the story isn't isolated.
- Solution. What to do about it. Don't be a Debbie Downer.
If a draft is missing any of the three, fix the missing element before any other pass. The most common gap: drafts heavy on story and statistics, weak or absent on solution.
The five questions below produce the three elements; the three elements are what reaches the reader. Use both: questions to plan, elements to structure.
The five questions to answer before writing
If the user can't answer all five in one sentence each, do not start the draft. Push back and ask.
- Who is your audience? — name the reader, not "everyone"
- Which publication? — NYT, WSJ, Atlantic, Substack, LinkedIn?
- Why you? — were you in the room? Lived experience? What makes your voice unique on this topic?
- Why now? — what's the news hook? Why this week and not last year?
- What's your solution? — don't be a Debbie Downer. Provide answers, not just complaints.
Structure (in order)
- Cinematic opener — a real person, a vivid scene, sensory detail. Date, place, image.
- Thesis — one sentence. Bold and arguable. Not a neutral description.
- Specific examples — real names, real numbers, real interviews
- Refute the best counterargument — not a strawman. The strongest version of the other side.
- Actionable solution — concrete, not abstract
- Connect to broader trend — what wave does this sit on?
What gets published
- Interview real people — in person or Zoom. Editors can tell when you only used web research.
- Co-author with a recognized expert — dramatically increases publication odds
- Cinematic examples — real people as heroes
- Why-you is essential — the Stanford Law student got published because she was in the room
- Universality — the best pieces make readers say "that's happening everywhere"
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 · 92 lines · 113 tokens per session scan A b39263365397
op-ed-coach is a skill published in the GitHub repository kalyvask/winning-writing (13 stars, last pushed 5d ago), licensed MIT. It adds 113 tokens to every session and 1,008 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
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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.