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Planning Products

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rileyhilliard
planning-products

Defines product features from a PM perspective (JTBD, competitive research, scope negotiation) before technical planning. Use when scoping features, writing product specs, defining user problems, or choosing what to build.

Overview

Publisherrileyhilliard
Repositoryclaude-essentials
Skill nameplanning-products
Stars
127
Forks
19
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Planning Products 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.
  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/rileyhilliard/claude-essentials.git /tmp/claude-essentials
mkdir -p .claude/skills
cp -r /tmp/claude-essentials/plugins/ce/skills/planning-products .claude/skills/planning-products
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Planning Products 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 Planning Products 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 Planning Products 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.

Planning Products

Core principle: Define the problem worth solving and the experience worth building before deciding how to build it. Research what exists, understand who needs it, scope what matters.

Planning Phase

Load the relevant reference based on where you are in the planning process:

PhaseLoadFile
Researching market, competitors, existing patternsDiscoveryreferences/discovery.md
Framing the problem, users, and jobs to be doneDefinitionreferences/definition.md
Defining how the feature should feel (UX or DX)Experiencereferences/experience.md
Drawing scope boundaries, MVP, phasingScopingreferences/scoping.md
Writing the actual product spec documentTemplatereferences/feature-spec-template.md

Start with Discovery when beginning from scratch. Start with Definition when the problem is already understood. Load Experience alongside either when the feature has significant interaction design. Load Scoping when you need to cut.


The Planning Sequence

Discovery → Definition → Experience → Scoping → [Hand off]
    ↓           ↓            ↓           ↓
 Research    Problem      How it      What's in
 & patterns  & users      feels       & what's out

Not always linear. Discovery can reframe the problem. Experience constraints can force scope changes. Scoping can send you back to discovery. But this is the default order.


Product Type Adjustments

Different products need different emphasis. Adjust the planning weight:

Product typeHeavy onLight onKey question
Consumer (B2C)Experience, discoveryFormal definition"Would I use this?"
B2B / EnterpriseDefinition, scopingVisual polish"Does this solve a workflow?"
Developer toolsDX experience, API ergonomicsMarketing polish"Is this the obvious API?"
Internal toolsScoping, definitionDiscovery"Does this save time?"
Platform / APIDefinition, experience (DX)Consumer UX"Is this composable?"

Universal Principles

Start with the customer's problem, not your solution

You are not designing features. You are solving problems for specific people. If you can't name the person and their frustration, you're not ready to plan.

Research before inventing

Before designing something new, find out what already exists. Users have expectations shaped by other products. Meeting those expectations is usually better than surprising people with novelty. Novelty costs learning; familiarity is free.

Hypothesis-driven, not conviction-driven

Frame decisions as hypotheses that could be wrong. "We believe [user] will [behavior] because [evidence]." If you can't state the evidence, it's a guess. Guesses are fine as long as you know they're guesses.

Type 1 vs Type 2 decisions

Decision typeCharacteristicsPlanning rigor
Type 1 (one-way door)Hard to reverse, high switching costFull discovery + definition
Type 2 (two-way door)Easy to change, low cost to undoLightweight spec, ship and learn

Most product decisions are Type 2. Don't over-plan reversible choices.

Define success before building

Every feature needs measurable success criteria before work begins. Not vanity metrics. Outcomes tied to the user problem you're solving.

Bad metricGood metric
"Page views""% of users who complete the core task"
"API calls""Time to first successful integration"
"DAU""Users who return within 7 days"

Anti-Patterns

PatternProblem
Solution-first planning ("Let's build X")Skips problem validation, builds the wrong thing
Feature lists without user storiesNo context for why, impossible to prioritize
Copying competitors without understanding whyImports their problems along with their solutions
"Users" as a monolith (no persona distinction)Different users need different things
Scope that only grows, never shrinksShip date slides, value dilutes
Planning without researchInvents patterns users already know differently
Perfecting the spec before testing assumptionsSpec fiction, not product planning
DX as afterthought ("devs will figure it out")Developers are users too, bad DX kills adoption

Handoff Points

This skill's output feeds into other skills:

  • Technical implementation: Use Claude's built-in plan mode to produce task breakdowns from the product spec
  • UI/UX craft: Skill(design) takes the experience requirements and produces the visual implementation
  • Architecture decisions: Skill(architecting-systems) takes the technical constraints from the spec and produces system design
  • Strategy context: Skill(strategy-writer) operates upstream, informing the "why this, why now" that feeds into discovery

Writing tone for specs and product docs: Use Skill(writer) with The PM persona.

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 Planning Products AI skill do?

Defines product features from a PM perspective (JTBD, competitive research, scope negotiation) before technical planning. Use when scoping features, writing product specs, defining user problems, or choosing what to build.

Why use Planning Products on TypingMind?

Because you install it once and use it with any model. Planning Products 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 Planning Products in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rileyhilliard/claude-essentials/tree/main/plugins/ce/skills/planning-products. 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 Planning Products?

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 Planning Products?

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

Is the Planning Products AI skill free?

Yes. It is published on GitHub by rileyhilliard 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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