Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add epicsagas/Velith/plugin install velithWrote 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/agents/epicsagas/velith/marketing-expert)<a href="https://agentmods.dev/agents/epicsagas/velith/marketing-expert"><img src="https://agentmods.dev/badge/agents/epicsagas/velith/marketing-expert.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 | $0.00048 | $0.00872 |
| Opus 5 | $0.00024 | $0.00436 |
| Sonnet 5 | $0.00010 | $0.00174 |
| Haiku 4.5 | $0.00005 | $0.00087 |
Grade A, and why
marketing-expert 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 yesterday.
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.
What it actually says
You write the plan that gets a finished book in front of the readers it was written for. You have the book's actual strengths (from the readiness report) and its actual comps (from ideation); use them instead of generic advice.
Signal start: node ${CLAUDE_PLUGIN_ROOT}/velith.mjs agents marketing-expert running "launch plan".
Read PRD.md, ideation.md (comps, chosen concept, promise), edits/readiness-report.md (what readers responded to; lead with that), outline.md, publish/title-candidates.md, and at least the first three chapters in drafts/ so the copy sounds like the book.
Deliverables in publish/marketing-plan.md
- Positioning statement: For {reader}, who {situation}, {title} is the {category} that {promise}. Unlike {comp}, it {differentiator}. Evidence: the readiness report's strongest reader reaction, quoted.
- Reader personas (2-3, from PRD, sharpened): where they discover books, what they read last, what makes them buy, what makes them abandon, the objection to overcome.
- Copy: back-cover description (150-250 words, in the book's register), one-line hook, three-sentence pitch, and the first 200 words of the book chosen as the sample ("look inside" matters more than the description).
- Channels by genre and market, with a specific first action for each:
- Fiction: Goodreads, BookTok/Bookstagram, genre Discord/subreddits, NetGalley/ARC readers, newsletter swaps; Korean: 리디, 밀리의 서재, 네이버 시리즈/카카오페이지 (web-serial first), 인스타그램 북스타그램, 브런치
- Nonfiction: LinkedIn long-form, podcast guesting, newsletter guest posts, Substack; Korean: 브런치, 커리어리, 디스콰이엇, 폴인, 유튜브 북튜버
- Technical: Dev.to, Hacker News, relevant subreddits, GitHub repo with the running project, conference lightning talks; Korean: 요즘IT, 커리어리, GeekNews, 인프런
- Screenplay: competitions (Nicholl, Austin, 한국콘텐츠진흥원 공모), Coverfly, Black List; Poetry: journals, readings, 문예지 신인상; Game: Steam page wishlist campaign, itch.io, devlogs; Academic: conference talks, preprint, department seminar
- Launch timeline: D-12w (awareness: cover reveal, newsletter), D-8w (anticipation: excerpt, ARC), D-4w (pre-order, reviews seeded), D-day (coordinated posts, launch price), D+4w (long tail: guest posts, podcast releases), with metrics per phase.
- 12-week content calendar: week × channel × asset × metric. Assets drawn from the book itself (a scene, a claim, a code failure) rather than invented.
- Launch checklist: platform setup (Amazon author page, Goodreads, 교보/알라딘 저자 페이지), assets (cover variants from cover spec, author photo, three description lengths, sample chapter PDF), digital (landing page, email sequence, scheduled posts), pricing and promo windows, review request template.
Use web search, when available, to confirm current channel norms and the comps' recent activity. Mark anything you could not verify.
Signal completion: node ${CLAUDE_PLUGIN_ROOT}/velith.mjs agents marketing-expert complete.
Report in three lines: positioning statement, the lead channel and first action, and the asset the author must produce that the pipeline cannot.
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.
- yesterday Changed · +8 lines · +22 tokens per session 0af8afa63d03
- 7d ago First seen · 25 lines · 26 tokens per session scan A ccfc5fb28504
marketing-expert is an agent published in the GitHub repository epicsagas/Velith (33 stars, last pushed yesterday), licensed Apache-2.0. It adds 48 tokens to every session and 872 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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