product-discovery-synthesis

product-discovery-synthesis is a skill for Claude Code from prepforeverything/prepkit-product. It costs 47 tokens per session (1,913 once invoked), scanned A, original, MIT.

A product-discovery guide for turning feature requests and research into clearly stated user problems and opportunities. It uses Jobs-to-be-Done, a way to describe what people are trying to accomplish in a specific situation.

In plain words
What is it for?
Use it to reframe feature requests, combine discovery findings, map user needs, and shape opportunities for further product work.
Why use it?
It helps teams avoid choosing a solution before understanding the real need. It also brings scattered interviews and other evidence into one view and checks how trustworthy that evidence is.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the prepkit-product plugin — 9 skills, 1 agent shipped together

Good fit Use it to reframe feature requests, combine discovery findings, map user needs, and shape opportunities for further product work.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/prepforeverything/prepkit-product/product-discovery-synthesis
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 prepforeverything/prepkit-product --skill product-discovery-synthesis
Clone the repo
git clone --depth 1 https://github.com/prepforeverything/prepkit-product

Made for: Claude Code.

Or install prepkit-product, the plugin that ships this one along with the rest of its 9 skills, 1 agent.

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-discovery-synthesis

README.md
[![agentmods](https://agentmods.dev/badge/skills/prepforeverything/prepkit-product/product-discovery-synthesis/github.svg)](https://agentmods.dev/skills/prepforeverything/prepkit-product/product-discovery-synthesis)
Your own site
<a href="https://agentmods.dev/skills/prepforeverything/prepkit-product/product-discovery-synthesis"><img src="https://agentmods.dev/badge/skills/prepforeverything/prepkit-product/product-discovery-synthesis/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-discovery-synthesis

Your own site · 80×15
<a href="https://agentmods.dev/skills/prepforeverything/prepkit-product/product-discovery-synthesis"><img src="https://agentmods.dev/badge/skills/prepforeverything/prepkit-product/product-discovery-synthesis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,913 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 $0.00047 $0.01913
Opus 5 $0.00023 $0.00957
Sonnet 5 $0.00009 $0.00383
Haiku 4.5 $0.00005 $0.00191

Measured 10d ago against content hash c3536b4d43f5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

product-discovery-synthesis 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 10d 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-discovery-synthesis/SKILL.md · 106 lines

How it starts

The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Standalone Mode: This skill is part of the prepkit-product plugin.

  • spec/product-context.md is optional — provide context inline or create one from the template.
  • Output paths (research/, reports/) are relative to your current working directory.
  • Facilitation routing is advisory — invoke any skill directly.

Product Discovery Synthesis

When To Use

  • An incoming feature request needs reframing into a user problem
  • The team is debating solutions before the job and pain are clear
  • spec/product-context.md has weak or assumed discovery inputs
  • Discovery evidence exists but has not been shaped into an opportunity worth mapping
  • Discovery has interview evidence but opportunities feel fragmented across multiple touchpoints or phases

Key Concepts

  • Jobs-to-be-Done (JTBD): users hire products to make progress in a context
  • Opportunity space: unmet needs or frictions that exist before solution ranking
  • Switching forces: push, pull, anxiety, and habit explain why users change
  • Outcome framing: opportunities should connect to user progress and measurable change
  • Empathy mapping: collaborative synthesis artifact that organises interview observations into four quadrants — Says (verbatim quotes), Thinks (internal beliefs not spoken aloud), Does (observable behaviours), and Feels (emotional states). Use it after interviews and before JTBD synthesis to ensure the Thinks and Feels dimensions — which affinity mapping alone does not prompt for — are explicitly captured. Juxtapositions across quadrants (e.g. positive Does but negative Feels) often reveal the most actionable unmet needs. See references/empathy-mapping-synthesis.md.
  • Customer journey mapping: visualisation of the user experience across phases — capturing what users do, think, and feel at each stage — to surface friction points and opportunity areas across the full experience arc. ODI Job Map (eight functional steps) is the right tool for deep single-job analysis; journey mapping covers multi-touchpoint experiences, emotional peaks, and cross-channel friction that JTBD interviews alone may not surface. The opportunities layer of a journey map is a direct input to OST opportunity nodes. See references/journey-mapping-basics.md.
  • Design Thinking / HCD process framing: teams with design-background practitioners often use the Empathize-Define-Ideate-Prototype-Test model (Design Thinking) or the ISO 9241-210 four-activity HCD cycle as their process reference. These are complementary framings alongside JTBD/ODI, not replacements. Empathize maps to JTBD interviewing; Define maps to empathy and journey mapping synthesis; Ideate maps to OST solution generation; Prototype/Test map to assumption-driven experimentation. JTBD and ODI remain the primary analytical frameworks in this skill; Design Thinking provides cross-disciplinary orientation for mixed teams.
  • First-principles decomposition: when discovery is stuck, a framework produces conflicting signals, or the problem framing feels inherited rather than earned, decompose the core assumption to axiomatic truths before continuing. First-principles is a meta-tool that works alongside JTBD, HCD, RICE, and other frameworks — it clears the foundation so those frameworks produce better answers. See references/first-principles-thinking.md.

Read the full file on GitHub · 106 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. 10d ago First seen · 106 lines · 47 tokens per session scan A c3536b4d43f5

Subscribe to this mod's changes

product-discovery-synthesis is a skill published in the GitHub repository prepforeverything/prepkit-product (2 stars, last pushed 5mo ago), licensed MIT. It adds 47 tokens to every session and 1,913 once invoked, about $0.0002 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.

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