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Agent skill

Manuscript Engagement Analytics

by hashgraph-online in hashgraph-online/awesome-codex-plugins

Analyze nonfiction manuscripts for reader engagement signals, including heading-level word counts, slow starts, long slogs, weak takeaway titles, value pacing, beta-reader comment dropoff, and…

MITAuto-check passed

Install Manuscript Engagement Analytics

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill manuscript-engagement-analytics -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins manuscript-engagement-analytics --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/LVTD-LLC/skills/skills/manuscript-engagement-analytics .claude/skills/manuscript-engagement-analytics && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
manuscript-engagement-analytics
GitHub stars
1.3k
Token cost
~875 tokens
SKILL.md length
333 words
Files
8 (incl. scripts, references)
Skills in repo
716
Repo updated
First seen
Licence
MIT

At a glance

Analyze nonfiction manuscripts for reader engagement signals, including heading-level word counts, slow starts, long slogs, weak takeaway titles, value pacing, beta-reader comment dropoff, and…

  • Works in 5 steps: Establish The Reader Promise → Generate A Structure Map → Mark Value Events → …
  • Auditing a book
  • SKILL.md covers Core Lens, Reference Routing, Script and Workflow, plus 2 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Manuscript Engagement Analytics is an agent skill from hashgraph-online/awesome-codex-plugins. Analyze nonfiction manuscripts for reader engagement signals, including heading-level word counts, slow starts, long slogs, weak takeaway titles, value pacing, beta-reader comment dropoff, and abandonment risks. Use when auditing a book, guide, manual, course-like draft, or technical manuscript for value density, reader experience, or beta-feedback engagement patterns.

Its SKILL.md is about 880 tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/core/checklist.md` and `references/core/examples.md`). Compatibility notes: Codex, Claude Code, and other Agent Skills-compatible clients.

The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is MIT.

When your agent uses it

  • Auditing a book
  • Course-like draft
  • Technical manuscript for value density
  • Reader experience

Example prompts

  • “/manuscript-engagement-analytics”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Codex, Claude Code, and other Agent Skills-compatible clients.

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Establish The Reader Promise
  2. Generate A Structure Map
  3. Mark Value Events
  4. Diagnose Engagement Risks
  5. Recommend Revision Actions

What it can do on your machine

Read from SKILL.md and the folder at commit 3e1456a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Codex, Claude Code, and other Agent Skills-compatible clients.

    From compatibility in the SKILL.md frontmatter.

Context cost

Manuscript Engagement Analytics loads about 875 tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 101 tokens; SKILL.md has 333 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~101
When it runs · the whole SKILL.md, loaded when a task matches
~875
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 3e1456a, republished under its MIT licence (© hashgraph-online). 333 words, ~875 tokens.

Download SKILL.mdSave it as .claude/skills/manuscript-engagement-analytics/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
manuscript-engagement-analytics
description
Analyze nonfiction manuscripts for reader engagement signals, including heading-level word counts, slow starts, long slogs, weak takeaway titles, value pacing, beta-reader comment dropoff, and abandonment risks. Use when auditing a book, guide, manual, course-like draft, or technical manuscript for value density, reader experience, or beta-feedback engagement patterns.
compatibility
Codex, Claude Code, and other Agent Skills-compatible clients.
license
MIT
metadata.version
0.1.0
metadata.displayName
Manuscript Engagement Analytics
metadata.category
Writing
metadata.tags
writing,books,nonfiction,analytics,reader-experience

Manuscript Engagement Analytics

Core Lens

Reader engagement can be approximated by mapping value over reading time. A manuscript with long stretches between useful payoffs, vague topic headings, or reader-comment dropoff is signaling where readers may get bored, confused, or stuck.

Use this skill to:

  • Generate heading-level word-count maps.
  • Find slow starts and long slogs.
  • Audit whether headings promise reader takeaways.
  • Interpret beta-reader comment locations and abandonment.
  • Produce a revision queue for value pacing.

Reference Routing

NeedRead
Engagement analytics conceptsreferences/core/knowledge.md
Analysis rules and thresholdsreferences/core/rules.md
Example maps and findingsreferences/core/examples.md
Fast audit checklistreferences/core/checklist.md
Step-by-step engagement auditworkflows/audit-engagement.md

Script

Use scripts/analyze_manuscript.py for deterministic Markdown structure analysis:

bash
python3 skills/manuscript-engagement-analytics/scripts/analyze_manuscript.py manuscript.md

It outputs a table of headings, line numbers, word counts, cumulative words, and heuristic flags. Use the script output as evidence, then apply judgment from the references.

Workflow

1. Establish The Reader Promise

Identify the target reader, book promise, and first meaningful payoff. If these are unclear, use book-toc-lab first.

2. Generate A Structure Map

Run the script or manually build a table:

text
Section | Line | Words | Cumulative words | Reader takeaway | Risk
3. Mark Value Events

Mark where the reader gets:

  • A usable idea.
  • A decision frame.
  • A checklist.
  • A worked example.
  • A lab or exercise.
  • A troubleshooting answer.
4. Diagnose Engagement Risks

Look for:

  • Too many words before first payoff.
  • Long sections with weak takeaways.
  • Back-to-back setup sections.
  • Vague headings that hide the reader value.
  • Beta-reader comments stopping near the same section.
5. Recommend Revision Actions

Prefer structural fixes:

  • Move value earlier.
  • Cut or compress low-payoff setup.
  • Rename headings around reader outcomes.
  • Split long sections.
  • Convert theory into examples, checklists, labs, or decisions.

Output Format

When auditing engagement, return:

  1. Promise and first-payoff diagnosis.
  2. Value map or script output summary.
  3. Highest-risk sections.
  4. Revision recommendations ordered by expected engagement impact.
  5. Beta-reader comment/dropoff interpretation when data exists.
  6. Follow-up checks after revision.

Quality Bar

Use metrics as signals, not verdicts. Word counts and comment dropoff show where to inspect; the final recommendation should explain what reader value is missing, delayed, or unclear.

© hashgraph-online, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in plugins/LVTD-LLC/skills/skills/manuscript-engagement-analytics of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • agents/openai.yaml
  • references/core/checklist.md
  • references/core/examples.md
  • references/core/knowledge.md
  • references/core/rules.md
  • scripts/analyze_manuscript.py
  • workflows/audit-engagement.md

Open the folder on GitHubat commit 3e1456a

Compare with similar skills

Manuscript Engagement Analytics next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Manuscript Engagement Analytics compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Manuscript Engagement Analytics this skillhashgraph-online/awesome-codex-plugins1.3k—~875Automated safety check: PassMIT
Designing Adversary Engagement With Mitre Engagemukul975/Anthropic-Cybersecurity-Skills34k—~3kAutomated safety check: PassApache-2.0
SignalsPostHog/posthog40k—~4.3kAutomated safety check: PassCustom licence
Trader Signalruvnet/ruflo74k—~605Automated safety check: NotesMIT
Heading Hierarchythedaviddias/Front-End-Checklist74k—~516Automated safety check: PassMIT
Empty Headingthedaviddias/Front-End-Checklist74k—~426Automated safety check: PassMIT

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Questions about Manuscript Engagement Analytics

What does Manuscript Engagement Analytics do?

Analyze nonfiction manuscripts for reader engagement signals, including heading-level word counts, slow starts, long slogs, weak takeaway titles, value pacing, beta-reader comment dropoff, and…. Manuscript Engagement Analytics is an agent skill from hashgraph-online/awesome-codex-plugins. Analyze nonfiction manuscripts for reader engagement signals, including heading-level word counts, slow starts, long slogs, weak takeaway titles, value pacing, beta-reader comment dropoff, and abandonment risks.

When should I use Manuscript Engagement Analytics?

Manuscript Engagement Analytics fits situations like: auditing a book; course-like draft; technical manuscript for value density; reader experience.

How do I install Manuscript Engagement Analytics in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill manuscript-engagement-analytics -a claude-code`. Or copy the skill folder (plugins/LVTD-LLC/skills/skills/manuscript-engagement-analytics in hashgraph-online/awesome-codex-plugins) into .claude/skills/manuscript-engagement-analytics in your project. Claude Code loads it when a task matches its description.

How do I install Manuscript Engagement Analytics in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill manuscript-engagement-analytics -a codex`. Or copy the skill folder (plugins/LVTD-LLC/skills/skills/manuscript-engagement-analytics in hashgraph-online/awesome-codex-plugins) into .agents/skills/manuscript-engagement-analytics in your project. Codex loads it when a task matches its description.

Can I use Manuscript Engagement Analytics in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill manuscript-engagement-analytics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/manuscript-engagement-analytics, .gemini/skills/manuscript-engagement-analytics, .github/skills/manuscript-engagement-analytics and .opencode/skills/manuscript-engagement-analytics in your project.

What does Manuscript Engagement Analytics need to run?

Going by SKILL.md and its folder, Manuscript Engagement Analytics needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3. Compatibility (from SKILL.md): Codex, Claude Code, and other Agent Skills-compatible clients..

Does Manuscript Engagement Analytics access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Manuscript Engagement Analytics safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Manuscript Engagement Analytics use?

Manuscript Engagement Analytics is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Manuscript Engagement Analytics use?

About 875 tokens (SKILL.md is roughly 3.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.9k tokens, read only when the agent opens those files.

What are the alternatives to Manuscript Engagement Analytics?

Skills that share tags, products or a category with Manuscript Engagement Analytics: Designing Adversary Engagement With Mitre Engage (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Signals (PostHog/posthog, 40k stars), Trader Signal (ruvnet/ruflo, 74k stars) and Heading Hierarchy (thedaviddias/Front-End-Checklist, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Manuscript Engagement Analytics?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,267 GitHub stars. The repository holds 716 skills in this directory. The repository was last updated on October 10, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.