hashgraph-online 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
| 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 |
Script
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.
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:
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:
- Promise and first-payoff diagnosis.
- Value map or script output summary.
- Highest-risk sections.
- Revision recommendations ordered by expected engagement impact.
- Beta-reader comment/dropoff interpretation when data exists.
- 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.
Source: hashgraph-online/awesome-codex-plugins → plugins/LVTD-LLC/skills/skills/manuscript-engagement-analytics/SKILL.md