Series Planner logo

Series Planner

Community
notque
series-planner

Plan multi-part content series: structure, cross-linking, cadence.

Overview

Publishernotque
Repositoryvexjoy-agent
Skill nameseries-planner
Stars
443
Forks
50
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by notque on GitHub. Read the source before you install it.

Installation

Install the Series Planner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/notque/vexjoy-agent/tree/main/skills/content/series-planner
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/notque/vexjoy-agent.git /tmp/vexjoy-agent
mkdir -p .claude/skills
cp -r /tmp/vexjoy-agent/skills/content/series-planner .claude/skills/series-planner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Series Planner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Series Planner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Series Planner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

SignalLoadWhy
Publishing frequency, delay handlingreferences/cadence-guidelines.mdSchedule selection criteria
Navigation links, Hugo implementationreferences/cross-linking.mdPrev/next nav, shortcodes, landing pages
Plan output formatreferences/output-format.mdComplete plan template
Series types, selection matrixreferences/series-types.mdThree 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:
SignalType
learn, master, deep diveProgressive Depth
build, create, projectChronological Build
why we chose, migration, debuggingProblem 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.

  1. 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

TypeStructureReader flexibility
Progressive DepthShallow to deep masteryBeginners stop at Part 1; advanced skip ahead
Chronological BuildStep-by-step creationEach part produces working output
Problem ExplorationProblem to solution journeyEven failed approaches are instructive

Error Handling

ErrorCauseSolution
Topic too narrowFewer than 3 natural divisionsRecommend single post or scope expansion
Topic too broadWould need 8+ partsSplit into multiple series; narrow scope
No logical progressionParts are loosely related, not buildingConsider standalone posts instead of series
Standalone value missingParts depend on each otherMerge dependent parts or add context inline

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Series Planner AI skill do?

Plan multi-part content series: structure, cross-linking, cadence.

Why use Series Planner on TypingMind?

Because you install it once and use it with any model. Series Planner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Series Planner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/notque/vexjoy-agent/tree/main/skills/content/series-planner. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Series Planner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Series Planner?

As many as you like. As long as a model supports skills, you can use Series Planner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Series Planner AI skill free?

Yes. It is published on GitHub by notque under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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