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 ronmkr/PromptBook --skill publication-structure-designergit clone --depth 1 https://github.com/ronmkr/PromptBookWrote 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/ronmkr/promptbook/publication-structure-designer)<a href="https://agentmods.dev/skills/ronmkr/promptbook/publication-structure-designer"><img src="https://agentmods.dev/badge/skills/ronmkr/promptbook/publication-structure-designer/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/ronmkr/promptbook/publication-structure-designer"><img src="https://agentmods.dev/badge/skills/ronmkr/promptbook/publication-structure-designer.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 | $0.00078 | $0.01037 |
| Opus 5 | $0.00039 | $0.00518 |
| Sonnet 5 | $0.00016 | $0.00207 |
| Haiku 4.5 | $0.00008 | $0.00104 |
Grade A, and why
publication-structure-designer 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 7d 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.
This is a copy
100% identical to writing-shape — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The user has passed (or will pass) a markdown file of raw material. Treat it as the input pile — anything from a tidy list of fragments to a wall of unstructured prose to a transcript. The format does not matter. Read it end-to-end before doing anything else.
Then run a shaping session that produces a separate article document. Do not edit the raw material file — it is read-only to this skill.
If the user did not say where to save the article, ask once and remember the path. The user will be editing the article file during the session; always re-read it before writing so their edits are preserved.
The loop
- Read the pile. Read the input file in full. Form a sense of what's in it.
- Draft 2–3 candidate openings. Each opening should imply a different thesis or angle for the article. Show all of them. Force the user to pick or compose a hybrid. The chosen opening defines what the rest of the article must do.
- Grow paragraph by paragraph. After the opening lands, ask "given this opening, what does the reader need to hear next?" Pull material from the pile to answer. Argue about whether the next beat is a paragraph, a list, a table, a callout, a quote, a code block. Each format choice should be deliberate and defensible.
- Append to the article file as you go. Don't batch. Write each agreed paragraph or block immediately so the user can see the article taking shape.
- Loop step 3 until the article is done. The user decides when it's done.
Conversational feel
This is a grilling session inverted. In ideation, the question was "what are you actually noticing?" Here it's "what is this article actually arguing, and in what order does the reader need to hear it?" Push back. Refuse to let weak transitions slide. If a paragraph doesn't earn its place, cut it.
Specific moves to keep using:
- "What does this paragraph do for the reader that the previous one didn't?"
- "If I cut this, what breaks?"
- "Is this prose, or should it be a list? Why prose?"
- "This sentence is doing two jobs — split it or pick one."
- "The opening promised X. We've drifted to Y. Either re-thread it or change the opening."
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.
- 7d ago First seen · 65 lines · 78 tokens per session scan A a4d41b865104
publication-structure-designer is a skill published in the GitHub repository ronmkr/PromptBook (2 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 78 tokens to every session and 1,037 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to writing-shape, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
browser-use
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, or extract information from web pages.
pi-autoresearch-loop
Skill "pi-autoresearch-loop" from twaldin/flt, covering pi-autoresearch — autonomous experiment loop, installation, quick start, core concepts and two-file persistence model.
grill
Rigorous requirements gathering through Socratic questioning. Use when the user says "/grill", "grill me", "ask me questions until we understand", or wants to be interrogated about implementation details before writing code. Forces deep thinking about edge cases, behavior, and design decisions before any code is…
grill-with-docs
Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.
find-skills
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
improve-codebase-architecture
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.