product-discovery

product-discovery is a skill for Claude Code, Codex from magnus919/agent-skills. It costs 68 tokens per session (1,477 once invoked), scanned A, original, MIT.

A stakeholder discovery method for learning what a product needs by talking to the people involved. It helps choose who to interview, ask open questions, uncover hidden assumptions, resolve conflicting information, and turn conversations into structured specifications.

In plain words
What is it for?
Use it to plan interviews, map stakeholders, detect missing information, synthesize conversations, resolve conflicts, and prepare a software requirements document.
Why use it?
It reduces the risk of building from one person’s request or from assumptions that were never tested. It makes gaps and disagreements visible before detailed development work begins.

Skill for Claude CodeCodex

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

Good fit Use it to plan interviews, map stakeholders, detect missing information, synthesize conversations, resolve conflicts, and prepare a software requirements document.

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Install with agentmods
npx agentmods add skills/magnus919/agent-skills/product-discovery
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 magnus919/agent-skills --skill product-discovery
Clone the repo
git clone --depth 1 https://github.com/magnus919/agent-skills

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin product-discovery/plugin install product-discovery after adding the marketplace above.

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/magnus919/agent-skills/product-discovery/github.svg)](https://agentmods.dev/skills/magnus919/agent-skills/product-discovery)
Your own site
<a href="https://agentmods.dev/skills/magnus919/agent-skills/product-discovery"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/product-discovery/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

Your own site · 80×15
<a href="https://agentmods.dev/skills/magnus919/agent-skills/product-discovery"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/product-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,477 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00068 $0.01477
Opus 5 $0.00034 $0.00739
Sonnet 5 $0.00014 $0.00295
Haiku 4.5 $0.00007 $0.00148

Measured 9d ago against content hash e7a272e47a6b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

product-discovery 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 9d 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.

product-discovery/SKILL.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Product Discovery — Stakeholder Map

Pre-discovery work begins before any conversation. Load this reference first when planning discovery:

Reference Load when File
Stakeholder Mapping & Sequencing You need to decide who to interview and in what order references/stakeholder-mapping.md
Interview Protocol Design You're designing a question stack for a specific stakeholder type references/question-patterns.md
Gap Detection Techniques You're preparing to recognize what stakeholders don't say references/gap-detection.md

Pipeline: Phase 0 — Discovery

RAW NEED → [MAP] → [INTERVIEW] → [SYNTHESIZE] → [DISTILL] → [VALIDATE] → SPEC.md
              |           |              |            |            |
         Who to       What to        Resolve      Convert to   Stakeholder
         talk to      ask            conflicts    structured   review

Load the reference for the phase you're entering.

Core principles

  • Closed questions confirm what you already think; open questions discover what you didn't know to ask. If the answer can be "yes" or "no," you're validating, not discovering.
  • Knowledge holders come first; authority holders validate — they don't originate. The person who knows the problem is not the same person who approves the budget.
  • Conflicts are almost never about what they appear to be. Surface disagreements are proxies for unstated differences in assumptions, risk tolerance, or incentives.
  • Every weasel word represents a gap. "Probably," "ideally," "eventually" — each is a known issue the stakeholder hasn't committed to addressing.

Quick Start — Where to Enter

You have this Start here
A vague idea or problem space Load references/stakeholder-mapping.md — identify who to interview
An interview scheduled with no protocol Load references/question-patterns.md and references/gap-detection.md — design your question stack
Raw interview notes from one or more sessions Load references/transcript-to-spec.md — distill into structured spec components
A draft spec that needs stakeholder verification Load references/transcript-to-spec.md (Interpretation Audit Trail section) — run the validation loop
Stakeholders who disagree on requirements Load references/conflict-resolution.md — classify and resolve before spec
Limited time with a stakeholder Load references/time-constrained-discovery.md — maximize signal in minimal time
An AI agent conducting interviews Load references/ai-conducted-discovery.md — account for sycophancy and trust dynamics

Read the full file on GitHub · 108 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. 9d ago First seen · 108 lines · 68 tokens per session scan A e7a272e47a6b

Subscribe to this mod's changes

product-discovery is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 1,477 once invoked, about $0.0003 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 2026-09-03.

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