Linkedin Content Planner

LinkedIn Content Planner

Produce a 7-day LinkedIn plan built around the 3-pillar discipline (Authority 40-50%, Personal Narrative 30-40%, Community 20-30%). Optionally adds a Product/Offer pillar at 10-15%.

When to use

• User asks "plan my week" or "what should I post this week"
• User wants to escape ad-hoc shipping and establish rhythm
• Before a launch week (user needs product-pillar alignment)

Input

• Theme (optional): e.g., "AI agents shipping in production", "first 6 months of Co.Actor"
• Audience description: e.g., "B2B founders, AI ops leaders, marketing VPs"
• Pillar mix (optional): defaults to 40% Authority / 30% Narrative / 20% Community / 10% Product
• Posting days (optional): defaults to Tue/Wed/Thu/Fri (4 posts)
• Voice samples (optional): paths to past posts for voice calibration

Output

A markdown plan with:

7-day calendar

| Day | Time | Pillar | Format | Hook formula | 1-line angle | CTA type | |---|---|---|---|---|---|---| | Mon | — | (commenting day) | — | — | — | — | | Tue | 8:00 AM local | Authority | Text | F7 Odd-Precision Money | "What 3 months of agent ops costs" | Question close | | Wed | 9:30 AM local | Narrative | Text | F4 Time-Anchor Confession | "Why I stopped publishing for 4 weeks" | Mirror question | | Thu | 8:00 AM local | Community | Poll | — | "Which metric matters most" | Poll | | Fri | 9:00 AM local | Authority | Carousel | F10 Contrarian Historical | "AI agencies have been dying since 2023" | Soft offer | | Sat/Sun | — | (off) | — | — | — | — |

Daily comment targets

For each posting day:

• 3-5 creators to engage (names or archetypes: "peer founders at 5-20k", "VCs with AI thesis", "BigCo CTOs")
• Comment pattern to apply (first-commenter, data-first, answer-their-question)
• Target count: 10-20 substantive comments per day

Weekly inbound-readiness check

• [ ] At least 1 vulnerability post (Narrative)
• [ ] At least 1 receipt/data post (Authority)
• [ ] At least 1 soft offer or CTA-driving post
• [ ] Comment strategy includes 70% peers, 20% aspirational, 10% prospects
• [ ] No pillar >60% of the week's posts
• [ ] No duplicate formula used twice in the same week

Rules

• 3 pillars minimum, 5 maximum. More than 5 dilutes signal.
• 3-5 posts per week. 6+/week triggers cannibalization signal in 360Brew.
• 10-20 comments/day on other creators. Comments drive more inbound than posts.
• Tue/Wed/Thu top for B2B. Avoid Fri after 2 PM, Sat/Sun (B2B 30-50% reach cut).
• One format per pillar per week. Don't stack 3 text posts for Authority — vary.
• Product/Offer pillar max 1 post/week. Overuse kills trust.

Formula → pillar mapping

| Pillar | Preferred formulas | |---|---| | Authority | F7 Odd-Precision Money, F10 Contrarian Historical, F8 Paid-vs-Free, F5 Self-Proving Meta | | Narrative | F4 Time-Anchor Confession, F3 Year-over-Year Pivot, F9 Curiosity-Gap | | Community | F6 Comment-Gate (use sparingly), poll posts, spotlight mentions | | Product/Offer | F2 R.I.P. Obituary (when pivoting category), F1 Anaphora (when framing product as fix) |

Steps

1. Gather inputs. Ask user for theme, audience, pillar preferences if not provided.
2. Validate pillar mix sums to 100%; warn if any pillar >60%.
3. For each posting day, pick:

• Pillar (rotate to match mix)
• Formula from that pillar's bank (don't repeat within 7 days)
• Format (alternating text / carousel / poll per pillar rules)
• Specific angle (user provides or skill generates)
• Posting time (audience-timezone aware)

1. For each posting day, add 3-5 comment targets with suggested pattern.
2. Run inbound-readiness check; flag anything missing.
3. Return as markdown + optional JSON for Notion/Airtable import.

Example

See references/example-plan-week.md for a filled-in 7-day plan.

Files

• SKILL.md — this file
• references/example-plan-week.md — worked example
• references/pillars-framework.md — the 3-pillar discipline explained

Related skills

• linkedin-post-writer — generate each day's draft from the plan
• linkedin-comment-drafter — execute the daily comment targets
• linkedin-thread-monitor — track inbound from the comment strategy
• linkedin-engager-analytics — segment audience on each post

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sergebulaev
• Source: sergebulaev/linkedin-skills
• License: MIT
• Homepage: https://github.com/sergebulaev/linkedin-skills

Install and usage instructions live in the source repository linked above.