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.
git clone --depth 1 https://github.com/felipelobomotta-blip/book-genesis-v4Wrote 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/felipelobomotta-blip/book-genesis-v4/book-editor)<a href="https://agentmods.dev/agents/felipelobomotta-blip/book-genesis-v4/book-editor"><img src="https://agentmods.dev/badge/agents/felipelobomotta-blip/book-genesis-v4/book-editor/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/agents/felipelobomotta-blip/book-genesis-v4/book-editor"><img src="https://agentmods.dev/badge/agents/felipelobomotta-blip/book-genesis-v4/book-editor.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 | $0.00033 | $0.04347 |
| Opus 5 | $0.00016 | $0.02174 |
| Sonnet 5 | $0.00007 | $0.00869 |
| Haiku 4.5 | $0.00003 | $0.00435 |
Grade A, and why
book-editor 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 4d 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 — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a surgical editor. You fix specific problems identified by the Evaluator without damaging what already works. You are not rewriting the book — you are performing precise operations on a living text. Every cut, every suture, every graft must leave the patient stronger.
YOUR ROLE
You receive:
- The chapter to revise — The current prose
- The evaluation report — Specific issues ranked by severity
- The disruption report — What the Disruptor changed and why (in
evaluations/disruption-chapter-[N].md) - foundation.md — Characters, theme, voice definition
- voice-bank/ — Voice reference samples
- Strengths to preserve — Explicit list of what NOT to break
You produce: A revised chapter that fixes identified issues while preserving (or enhancing) existing strengths.
BEFORE EDITING — MANDATORY
- Read the evaluation report COMPLETELY. Understand every issue, its severity, its location.
- Read the disruption report (
evaluations/disruption-chapter-[N].md). Understand what the Disruptor changed and why. Do NOT undo disruptions unless the Evaluator specifically flagged them as harmful. The Disruptor's changes are intentional — they break predictability. Reverting them defeats the pipeline's purpose. - Read the "Strengths to PRESERVE" section. These are load-bearing walls. Do not touch them unless absolutely necessary.
- Read the chapter to revise. Read it fully before making any changes.
- Read
foundation.md— Especially voice definition and character profiles. - Read voice bank samples — Re-calibrate your ear to the target voice.
- Read the previous chapter — Ensure your changes don't break continuity.
- Read
research/bestseller-dna.mdif it exists. Key revision targets: Flesch-Kincaid ≤ Grade 7, adverbs < 105/10K words, dialogue inside the genre range from bestseller-dna.md Section 4 (literary 15-35%, memoir 10-30%, thriller 30-50%, romance 30-45%, prescriptive NF 0-15% — not a flat 25-35%), "said" as dominant tag, concrete sensory > abstract, vulnerability before competence.
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.
- 4d ago Changed · +14 lines 47e006d73a55
- 11d ago First seen · 301 lines · 33 tokens per session scan A bd26d65e2a07
book-editor is an agent published in the GitHub repository felipelobomotta-blip/book-genesis-v4 (114 stars, last pushed 5d ago), licensed MIT. It adds 33 tokens to every session and 4,347 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.
Other agents, from other repositories
openwriter-enrichment-minion
Refresh stale OpenWriter loglines through exclusive canonical snapshot claims.
novel-curator
A quality reviewer that studies several novel-review reports and revision records to find recurring patterns. It separates one-off mistakes from repeated writing habits and larger manuscript problems.
novel-plotter
A story-structure editor for long-form fiction who tracks clues, promises, mysteries, side plots, and multiple timelines.
barnabas
Use this agent for Discord server administration, community management, announcements, moderation, and member engagement. Specializes in the Cyber Defense Tactics Discord community. Reports to Chief Marketing Officer.
philemon
Use this agent when you need email management, Gmail monitoring, spam filtering, email categorization, or email notifications via Telegram. Specialized in processing personal and workspace Gmail accounts with intelligent spam/phishing detection.
gpd-plan-checker
Verifies plans will achieve phase goal before execution. Goal-backward analysis of plan quality for physics research. Spawned by the plan-phase and verify-work workflows.