---
name: series-planner-181479fe65fd
promoted_to: content
description: "Plan multi-part content series: structure, cross-linking, cadence."
user-invocable: false
allowed-tools:
  - Read
  - Write
  - Bash
  - Grep
  - Glob
  - Edit
  - Task
command: /series
routing:
  triggers:
    - "plan series"
    - "multi-part content"
    - "content series"
    - "article series"
    - "content arc"
  category: content-creation
  pairs_with:
    - content
---

# Series Planner Skill

Plan multi-part content series: assess viability, select structure, produce plan with cross-linking and cadence. Three phases with strict gates.

## Deep References

| Signal | Load | Why |
|---|---|---|
| Publishing frequency, delay handling | `references/cadence-guidelines.md` | Schedule selection criteria |
| Navigation links, Hugo implementation | `references/cross-linking.md` | Prev/next nav, shortcodes, landing pages |
| Plan output format | `references/output-format.md` | Complete plan template |
| Series types, selection matrix | `references/series-types.md` | Three type templates with examples |

## Usage

```
/series [topic]
/series --type=progressive [topic]     # Force type
/series --parts=5 [topic]              # Target count
/series --with-landing [topic]         # Include landing page
/series --minimal [topic]              # Titles and scope only
```

## Phase 1: ASSESS

Determine viability and natural divisions.

1. Analyze topic: scope (narrow/medium/broad), natural divisions, audience progression.
2. Check viability: minimum 3 distinct subtopics, each stands alone, logical progression exists, no filler.
3. Detect series type:

| Signal | Type |
|--------|------|
| learn, master, deep dive | Progressive Depth |
| build, create, project | Chronological Build |
| why we chose, migration, debugging | Problem Exploration |

**Gate**: 3+ natural divisions identified. If fewer, recommend single post or scope adjustment.

## Phase 2: DECIDE

Select type, part count (strict 3-7 bounds), and structure.

1. Select type and justify. Define part count and word estimate.
2. Draft breakdown per part: title, scope (1 sentence), standalone value, adjacent links.
3. Validate standalone value for every part:
   - Reader learns something complete and actionable from this part alone
   - Working code/config/output possible without other parts
   - No critical info deferred to other parts
   - Search landing on this part alone yields value

Red flags (reject): "read Part 1 first" dependency, mid-implementation endings, concepts only in earlier parts.

4. Select cadence (see `references/cadence-guidelines.md`). Default: weekly.

**Gate**: All parts pass standalone test. Part count 3-7. Type justified.

## Phase 3: GENERATE

Produce the complete plan.

1. Build plan: series header, per-part breakdown, cross-linking (see `references/cross-linking.md`), publication dates, Hugo frontmatter.
2. Final validation checklist:
   - Every part has standalone value
   - Word counts 800-1500/part, within 20% variance
   - Cross-linking complete (prev/next for all)
   - No cliff-hangers, no filler
   - Part count within bounds
3. Output using `references/output-format.md` format.

**Gate**: All checks pass. Plan complete.

## Series Types

| Type | Structure | Reader flexibility |
|------|-----------|-------------------|
| **Progressive Depth** | Shallow to deep mastery | Beginners stop at Part 1; advanced skip ahead |
| **Chronological Build** | Step-by-step creation | Each part produces working output |
| **Problem Exploration** | Problem to solution journey | Even failed approaches are instructive |

## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Topic too narrow | Fewer than 3 natural divisions | Recommend single post or scope expansion |
| Topic too broad | Would need 8+ parts | Split into multiple series; narrow scope |
| No logical progression | Parts are loosely related, not building | Consider standalone posts instead of series |
| Standalone value missing | Parts depend on each other | Merge dependent parts or add context inline |


---
Source attribution: notque
Original project: https://github.com/notque/vexjoy-agent/blob/8d93f3e12672bb1abfc8ca00176fe109cbf2bdf5/skills/content/series-planner/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Claude Code Agents Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: notque
Original project: https://github.com/notque/vexjoy-agent/blob/8d93f3e12672bb1abfc8ca00176fe109cbf2bdf5/skills/content/series-planner/SKILL.md
Attribution basis: Source repository record

Fields of work: Marketing
Roles: Content Marketer, Influencer Marketer, Marketing Analyst, SEO Specialist, Social Media Manager
Specialties: API, Database, DevOps, Word Documents, Frontend, Testing
Tools: Tool not specified
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: e5de253d64a5e3d4f9affd4872dcc626e4dc71492463078a2399c8004a5f26ee
