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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.
Passed 15 of 20 test cases with the skill, and 10 of 20 without it.
and 3 more in the full report
6 fixed11 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
Perform a full engagement audit on 'api_security_guide.md' and give me the final revision plan.
| Checked | Without skill | With skill |
|---|---|---|
| The audit includes concrete follow-up verification checks to run after revisions. | Missed | Passed |
The instructions your model reads when the skill loads.
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:
| Need | Read |
|---|---|
| Engagement analytics concepts | references/core/knowledge.md |
| Analysis rules and thresholds | references/core/rules.md |
| Example maps and findings | references/core/examples.md |
| Fast audit checklist | references/core/checklist.md |
| Step-by-step engagement audit | workflows/audit-engagement.md |
Use scripts/analyze_manuscript.py for deterministic Markdown structure analysis:
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.
Identify the target reader, book promise, and first meaningful payoff. If these are unclear, use book-toc-lab first.
test cases passed, vs 10 / 20 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
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