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Positioning

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nahid-sparktales
positioning

Say what a product is for, which alternative it beats, and why — in one segment's own words, with every claim tied to something the product actually does today. Use when a homepage or deck says what the product is, when "who is this for" or "how is this different" has no crisp answer, when entering a new segment, or when sales keeps losing to one competitor. Not for writing the final copy, not for pricing, and not for deciding what to build next.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill namepositioning
Stars
58
Forks
5
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by nahid-sparktales on GitHub. Read the source before you install it.

Installation

Install the Positioning 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/nahid-sparktales/agent-dispatcher/tree/main/skills/knowledge/positioning
  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/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/knowledge/positioning .claude/skills/positioning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Positioning 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 Positioning 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 Positioning 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.

Positioning

Positioning is a claim about a comparison someone is already making. The buyer will pick a frame of reference whether or not you choose one; the work is choosing it deliberately, naming the alternative honestly, and holding every differentiator to something the product does today.

When this fires

The product has to be described to people who do not already know it — homepage, deck, category page, launch, a new segment — or the existing description is not landing: prospects ask "so what is it", sales keeps losing to the same alternative, or two internal descriptions disagree. It does not fire for individual interface strings or for an ad headline; those are downstream of this.

Two traps this exists to catch

  • Describing what you built rather than what it replaces. Feature lists are attributes. A buyer compares outcomes against the thing they do now.
  • Positioning against the competitor you want. The real alternative is usually a spreadsheet, a manual process, an incumbent nobody loves, or doing nothing. Name the one they would actually fall back to this quarter.

Procedure

  1. Pick one segment, and make it small enough to be wrong about. Who has this problem acutely, this quarter, with the authority to change it. "Teams that care about quality" is not a segment; "support leads at 50–200-person SaaS companies handling tickets in a shared inbox" is.
  2. Name the trigger. What happens in that segment's week that makes them go looking. No trigger means no urgency, and no amount of wording fixes that — report it as the finding.
  3. List the alternatives that segment would actually use, including the status quo and doing nothing. Source each from something real: lost-deal notes, support threads, review sites, what the user tells you. An alternatives list assembled from memory is a guess; label it one.
  4. Inventory what the product actually does today. Read the product, not the roadmap. Keep shipped, in progress and planned in separate columns and never let the last two into a claim.
  5. Reduce to the attributes only some alternatives have. Anything every alternative also has is table stakes — it belongs in the page, not in the differentiator. A useful test: would a competitor publish the opposite claim? If not, it distinguishes nothing.
  6. Turn each remaining attribute into the value that segment feels, in a sentence they would say themselves. Take the vocabulary from their words — tickets, reviews, call notes, the phrases they search — not from internal naming. Quote the source for at least the central ones.
  7. Choose the frame of reference: the category the buyer already understands and already budgets for, or the explicit "an alternative to X". A new category is a much larger bet than it looks, because you also have to teach the problem; take it only deliberately.
  8. Write it as one falsifiable statement. For [segment] who [trigger], [product] is a [category] that [value] — unlike [alternative], because [proof]. If any slot is filled with something unfalsifiable ("best-in-class", "seamless"), the slot is empty.
  9. Attach evidence to every claim. A benchmark that was actually run and its conditions, a named customer who said it, a capability you can demonstrate. A claim with nothing behind it gets cut, not softened.
  10. Hold comparative claims to a higher bar. Naming a competitor and asserting something about them is a public, legally consequential statement. Verify it against their current documented behaviour, date the check, and route it for human review before it goes anywhere public.
  11. List where this changes what is already published — homepage, pricing page, deck, app store listing, docs intro — as a diff of current wording against proposed. Publishing or editing any live public page is outward-facing: stop there and hand it over.

Checklist

  • One segment named, specifically enough that it could be shown wrong
  • The trigger written down, or its absence flagged
  • Alternatives listed with a source, status quo included
  • Every claim traced to shipped behaviour, with roadmap items excluded
  • Table-stakes attributes separated from real differentiators
  • Value stated in the segment's own words, with at least one quoted source
  • Frame of reference chosen explicitly
  • The statement written, with no unfalsifiable slot
  • Comparative claims verified and dated, and routed for review
  • Changes to live public pages proposed as a diff, not applied

Failure handling

  • No segment can be named — say so. Undifferentiated positioning is usually a product or strategy question wearing a copy question's clothes, and wording will not resolve it.
  • The only differentiator is price — that is a position, and a fragile one. Report it as the finding rather than dressing it as a capability.
  • Every differentiator turns out to be table stakes — stop and say the honest result: the product's advantage is not visible from outside, or does not currently exist.
  • The compelling claim is about something unshipped — it does not go in. Offer the shipped version of the claim, and flag the gap to whoever owns the roadmap.
  • Two segments both look plausible — do not average them into one statement that fits neither. Write both, name what each costs, and let the owner choose.
  • Evidence exists only as internal opinion — label it as an assumption to test, and say what would test it: lost-deal reasons, five customer conversations, a landing-page test.

Evidence to report

The positioning statement in full. The segment and the trigger, with what they came from. The alternatives list with sources. Each differentiator beside the shipped capability and the proof behind it. The buyer phrasing you adopted, quoted with its source. Which claims are assumptions rather than evidence, named as such. The proposed diffs to live pages, unapplied. And what you could not check — usually whether the segment agrees, which only they can answer.

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 Positioning AI skill do?

Say what a product is for, which alternative it beats, and why — in one segment's own words, with every claim tied to something the product actually does today. Use when a homepage or deck says what the product is, when "who is this for" or "how is this different" has no crisp answer, when entering a new segment, or when sales keeps losing to one competitor. Not for writing the final copy, not for pricing, and not for deciding what to build next.

Why use Positioning on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/knowledge/positioning. 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 Positioning?

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 Positioning?

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

Is the Positioning AI skill free?

Yes. It is published on GitHub by nahid-sparktales 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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