suggest-next-post

name	suggest-next-post
description	Recommends what to write next. Analyzes the author's Topic Backlog, cluster balance, trends, and the algorithm. Prioritizes topics from Inbox into Prioritized. Helps avoid sameness and ride a trend.
disable-model-invocation	true
argument-hint	["Author"]
allowed-tools	Read, Grep, Glob

Suggest Next Post

What to write next? Prioritize topics from the Topic Backlog with cluster balance, trends, and the algorithm in mind.

Input

• $0 — author name (Seva, Kirill)

Required files

1. Author DIP: Knowledge-Base/01-Identity-Profiles/[Author]-DIP.md → Expertise Clusters, cluster distribution, Viral Angles

2. Topic Backlog: Posted/[Author]/Topic-Backlog.md → Inbox topics, Prioritized, Done

3. Posted (recent posts): Posted/[Author]/ → Last 10–15 posts for cluster and format analysis

4. Algorithm-Intelligence: Knowledge-Base/02-LinkedIn-Algorithm/Algorithm-Intelligence.md → Timing, formats, engagement rules

5. Current-Trends: Knowledge-Base/03-Viral-Trends/Current-Trends.md → What's trending now

6. Format-Performance-Data: Knowledge-Base/02-LinkedIn-Algorithm/Format-Performance-Data.md → Which formats perform better

Pipeline

Step 1: Current state analysis

1A. Cluster balance

Read the author's last 10–15 posts. Extract cluster: from each frontmatter.

Compare actual distribution against the DIP target:

Example:

Cluster	Target %	Actual %	Status
AI-Native Teams	30%	45%	OVER — reduce
Perf Marketing + Product	30%	20%	UNDER — add
Founder Journey	20%	25%	OK
Industry Insights	20%	10%	UNDER — add

1B. Format rotation

Last 5 posts → which formats were used? If 3+ in a row in the same format → recommend a different one.

1C. Timing

When was the last post? If >3 days ago — urgent. Optimal frequency: 3–5 posts per week.

Step 2: Topic Backlog analysis

Read the author's Topic-Backlog.md:

• Inbox — topics awaiting prioritization
• Prioritized — topics ready to write
• Done — already written

For each Inbox topic, score:

Criterion	Weight	How to score
Cluster fit	30%	Needed cluster per balance → +3, over → −2
Trend match	25%	Matches Current-Trends → +3
Unique angle	20%	Has contrarian / fresh perspective → +2
Source readiness	15%	Raw source available (transcript, Telegram) → +2
Urgency	10%	Timely topic (event, news) → +3

Step 3: Recommendations

## What to write next — [Author]

### Date: [today]
### Last post: [date, topic]
### Cluster balance: [OK / skew toward X]

---

### Top 3 recommendations

#### 1. [Topic] ⭐ PRIORITY

**Cluster:** [cluster] — [why this cluster is needed now]
**Format:** [recommended format]
**Angle:** [from DIP Viral Angles]
**Source:** [which raw source to use]
**Trend match:** [match with Current-Trends]
**Score:** [N]/10

#### 2. [Topic]

[same fields]

#### 3. [Topic]

[same fields]

---

### Topics for Inbox → Prioritized

| Topic | Score | Cluster | Recommendation |
|-------|-------|---------|----------------|
| ... | N/10 | ... | Prioritized / Keep in Inbox / Remove |

### Cluster gap

**Need more posts on:** [cluster] — [specific topics from the DIP]
**Can wait:** [cluster] — [already well covered]

Output

1. Top 3 recommendations with reasoning
2. Inbox prioritization — which topics to move into Prioritized
3. Cluster gap — where there's a shortfall
4. Format recommendation — what to use for the next post

If the Topic Backlog is empty

Suggest topics from:

1. DIP Expertise Clusters — uncovered angles
2. Current-Trends — what's relevant now
3. Telegram scan — what landed on Telegram (suggest running /scan-telegram)
4. Transcripts — what came up in recent calls