product-positioning

product-positioning is a skill for Claude Code, Codex from DojoGenesis/mcp. It costs 75 tokens per session (2,302 once invoked), scanned A, original, MIT.

A product-strategy method for finding the distinct value of each option in a keep-or-remove, build-or-buy, or native-versus-web decision.

In plain words
What is it for?
Use it when deciding whether to maintain, replace, buy, or build a product or feature, and when writing its strategic positioning.
Why use it?
It replaces a simple either-or decision with a clearer view of what each option is uniquely good at.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/dojogenesis/mcp/product-positioning
Any agent
npx skills add DojoGenesis/mcp --skill product-positioning
Clone the repo
git clone --depth 1 https://github.com/DojoGenesis/mcp

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for product-positioning

README.md
[![agentmods](https://agentmods.dev/badge/skills/dojogenesis/mcp/product-positioning.svg)](https://agentmods.dev/skills/dojogenesis/mcp/product-positioning)
Your own site
<a href="https://agentmods.dev/skills/dojogenesis/mcp/product-positioning"><img src="https://agentmods.dev/badge/skills/dojogenesis/mcp/product-positioning.svg" alt="Measured on agentmods" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,302 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00075 $0.02302
Opus 5 $0.00037 $0.01151
Sonnet 5 $0.00015 $0.00460
Haiku 4.5 $0.00007 $0.00230

Measured 6d ago against content hash 176e6f62b608, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

product-positioning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

internal/skills/bundled/strategic-thinking/product-positioning/SKILL.md · 245 lines

How it starts

The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Product Positioning Skill

Version: 1.1 Purpose: To guide the process of reframing a binary product decision into a strategic positioning opportunity by identifying the unique, contextual value of a product or feature.


I. The Philosophy: Beyond the Binary

The most common trap in product strategy is the binary choice: keep or kill, build or buy, deprecate or maintain. These choices are limiting because they assume the value of a product is fixed.

This skill operates on a different principle: value is contextual. A product's worth isn't inherent -- it's determined by the context in which it's used. A web app isn't "redundant" because a desktop app exists. It's uniquely good at discovery and onboarding -- things the desktop app can't do.

The core technique is the unlocking question: "What is this uniquely good at that the other thing isn't?" This question shatters the binary and opens up new strategic possibilities.

The core insight: The reframe is the prize, not the initial answer. The before/after shift in how you think about the product is more valuable than the decision itself.


II. When to Use This Skill

  • When facing a decision about whether to keep or deprecate a feature or product
  • When a product or feature seems redundant or is underperforming
  • When planning a multi-surface product strategy (web, desktop, mobile)
  • At the beginning of a strategic planning cycle
  • Before using /scout to ensure the question is properly framed
  • When two products or features feel like they're competing rather than complementing

When NOT to use:

  • When the product genuinely has no unique value (sometimes deprecation is the right call)
  • When the decision is about timing, not positioning (use /scout instead)
  • When you need to explore many options without a binary starting point (use /scout; this skill is for binary reframes)

III. The Workflow

This is a 5-step workflow for reframing a product decision.

Step 1: Identify the Binary Trap

Read the full file on GitHub · 245 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 245 lines · 75 tokens per session scan A 176e6f62b608

Subscribe to this mod's changes

product-positioning is a skill published in the GitHub repository DojoGenesis/mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 75 tokens to every session and 2,302 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.