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 agentmods add agents/epicsagas/velith/chapter-writergit clone --depth 1 https://github.com/epicsagas/VelithWhat 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 | $0.00024 | $0.00228 |
| Opus 5 | $0.00012 | $0.00114 |
| Sonnet 5 | $0.00005 | $0.00046 |
| Haiku 4.5 | $0.00002 | $0.00023 |
Grade A, and why
chapter-writer 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 2d 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.
What it actually says
Generate single chapter draft. Supports parallel execution (independent context per chapter).
Context assembly: chapter spec → STYLE.md → prev chapter summary (not full text) → source excerpts.
Genre templates: Fiction (hook→conflict→turn→cliffhanger) · Non-fiction (hook→problem→evidence→framework→application→summary) · Technical (hook→concept→code→explanation→pitfalls→summary).
Self-QA: word target ±10% · frontmatter correct · heading structure · no STYLE.md violations · all key concepts · natural connection to prev chapter.
Output: drafts/ch{NN}-{slug}.md. Skip if already exists (idempotent).
Status: node {PLUGIN_ROOT}/velith.mjs agents chapter-writer <running|complete|error> [task]
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.
- 2d ago First seen · 15 lines · 24 tokens per session scan A 02ea603053c3
chapter-writer is an agent published in the GitHub repository epicsagas/Velith (33 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 24 tokens to every session and 228 once invoked, about $0.0001 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 agents, from other repositories
humanizer
识别并修复典型 AI 写作特征，从内容、语言、风格三个维度进行"净化"，并强化原稿中已有的观点、节奏、不确定性和个人视角。Humanizer 只改表达，不创造事实、经历或证据。包含严格的黑名单过滤和 50 分制质量自评。.
empathy-designer
社交货币与共情设计师。根据大纲和伤疤细节，设计文章的分享动因（Impression Management），建立 Share Map。由工作流导演在 Stage 4 显式调用。.
opening-tournament
开头赛马机制。根据前序策划信息，并行提供 3 种极具差异化的实战开头原型方案（字数 150-300 字/个），由工作流导演在 Stage 5.8 显式调用。.
wechat-reader-test
多平台读者压力测试器（保留 wechat-reader-test 名称以兼容既有工作流）。 从 01theme.md 读取发布平台，为公众号、今日头条、知乎选择不同的定性测试矩阵； 不伪造 CTR、完读率或推荐量预测。由工作流导演在 Stage 9 显式调用。.
topic-research
选题调研专家。验证选题是否值得写，提供热点扫描、爆款拆解、痛点验证和选题打分。由工作流导演在 Stage 0b 显式调用。.
supervisor-agent
The Supervisor Agent is responsible for evaluating agent collaboration events and deciding whether Shep should approve, reject, escalate, or advise on the next step.