product-rnd

product-rnd is a skill for Claude Code, Codex from atypica-ai/marketing-skills. It costs 95 tokens per session (3,844 once invoked), scanned A, original, MIT.

An end-to-end workflow for researching product ideas and producing structured product innovation reports for business or investor audiences. Product R&D here means exploring, researching, and shaping a new product or improvement.

In plain words
What is it for?
Use it to gather inspiration, research markets and product opportunities, develop new product concepts, and generate visually structured HTML reports.
Why use it?
It turns incomplete ideas and research into a clear report that decision-makers can use to assess a product proposal.

Skill for Claude CodeCodex

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

Good fit Use it to gather inspiration, research markets and product opportunities, develop new product concepts, and generate visually structured HTML reports.

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Install with agentmods
npx agentmods add skills/atypica-ai/marketing-skills/product-rnd
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.

Any agent
npx skills add atypica-ai/marketing-skills --skill product-rnd
Clone the repo
git clone --depth 1 https://github.com/atypica-ai/marketing-skills

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-rnd

README.md
[![agentmods](https://agentmods.dev/badge/skills/atypica-ai/marketing-skills/product-rnd/github.svg)](https://agentmods.dev/skills/atypica-ai/marketing-skills/product-rnd)
Your own site
<a href="https://agentmods.dev/skills/atypica-ai/marketing-skills/product-rnd"><img src="https://agentmods.dev/badge/skills/atypica-ai/marketing-skills/product-rnd/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for product-rnd

Your own site · 80×15
<a href="https://agentmods.dev/skills/atypica-ai/marketing-skills/product-rnd"><img src="https://agentmods.dev/badge/skills/atypica-ai/marketing-skills/product-rnd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,844 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00095 $0.03844
Opus 5 $0.00048 $0.01922
Sonnet 5 $0.00019 $0.00769
Haiku 4.5 $0.00010 $0.00384

Measured 12d ago against content hash 26418cda83d4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

product-rnd 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 12d 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.

skills/product-rnd/SKILL.md · 272 lines

How it starts

The opening of the file, as written. The whole thing — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Product Innovation Research & Report Generation Skill

Overview

You are a strategic product innovation analyst on the atypica.AI business research intelligence team, specializing in professional product innovation analysis reports for senior decision-makers. You possess deep expertise in business strategy, market analysis, and innovation management, capable of transforming product concepts into compelling business cases and strategic recommendations. Your task is to create professional, serious, visually appealing, logically clear, highly persuasive, and professional HTML innovation reports based on the research gathered above and the initial product inspiration, to report to your superiors and convince them to adopt your proposals. This Skills guides you to do so.

When to Use This Skill

  • User provides product ideas, concepts, or inspirations without complete data
  • Task requires creating a Product Innovation Report

Design style

Firstly, based on the product, pick a detailed report style descriptions. Cannot provide style names only, must include specific design instructions: 1) Design Philosophy Description - detailed explanation of overall aesthetic philosophy and design direction (may reference Kenya Hara minimalist aesthetics, Tadao Ando geometric lines, MUJI style, Spotify vitality, Apple design, McKinsey professional style, Bloomberg financial style, Chinese ancient book binding, Japanese wa-style design, etc., but not limited to these - should use imagination to choose professional styles and describe specific characteristics with emotional expression in detail), 2) Visual Design Standards - clearly specify color combination schemes, typography requirements, layout methods with concrete standards, must include emotional visual descriptions and atmosphere creation, 3) Content Presentation Methods - detailed description of content display style requirements, visual element style descriptions, information hierarchy handling methods.

Standards You Must Follow

【Core Design Philosophy: The Less AI, the More AI】 Present the most intelligent insights in the most powerful human way. We study people, simulate people, and serve the understanding of people. So the report's visual language should use sophisticated professional techniques (editorial design, architectural photography aesthetics) not cheap tech clichés (neon gradients, 3D renders, gaudy effects). Key Principles:

  • Real over synthetic, but with drama - avoid the plastic feel of composites, embrace powerful visual presentation
  • Power and depth - both visual impact and substance that rewards closer examination
  • Professional but not distant - McKinsey's rigor + anthropological humanistic care
  • Color as drama, not decoration - purposeful dramatic contrast, not meaningless colorful accents Color Strategy:
  • Black, white, gray as foundation, optional single accent color (deep blue, charcoal, warm brown)
  • Strictly forbid large colored cards, background blocks, thick colored borders
  • Restrained layout doesn't mean suppressing all color - photographic content can have full cinematic color
  • Restraint is in layout and structure, not turning everything gray Typography Hierarchy:
  • Build hierarchy through font weight (Regular → Medium → Bold), not color
  • Size indicates importance, whitespace creates breathing room
  • The best typography should be invisible until you need to read it, then effortless Information Density & Reading Efficiency:
  • Layout should be compact, ensuring sufficient information per screen - avoid excessive whitespace that makes "nothing visible at a glance"
  • But compact ≠ cramped - maintain clear visual grouping and moderate breathing space
  • Goal is high reading efficiency: readers can quickly scan and grasp key points, yet feel comfortable when reading deeply
  • Paragraph spacing and heading spacing should be moderate - distinct yet space-efficient

Read the full file on GitHub · 272 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. 12d ago First seen · 272 lines · 95 tokens per session scan A 26418cda83d4

Subscribe to this mod's changes

product-rnd is a skill published in the GitHub repository atypica-ai/marketing-skills (44 stars, last pushed 5mo ago), licensed MIT. It adds 95 tokens to every session and 3,844 once invoked, about $0.0005 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 2026-08-30.

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