New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Product Discovery

by cbrock84 in cbrock84/headcount

Finds out whether a problem is real and a solution would work, before building it — recruiting the right people, interviewing without leading them, separating what users say from what they do…

MITAuto-check passedProduct & Project Management

Install Product Discovery

skills CLI
$ npx skills add cbrock84/headcount --skill product-discovery -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install cbrock84/headcount product-discovery --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/cbrock84/headcount.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/product/skills/product-discovery .claude/skills/product-discovery && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
product-discovery
GitHub stars
2k
Token cost
~1k tokens
SKILL.md length
616 words
Files
1
Skills in repo
178
Repo updated
First seen
Licence
MIT

At a glance

Finds out whether a problem is real and a solution would work, before building it — recruiting the right people, interviewing without leading them, separating what users say from what they do…

  • Tasks that involve Recruiting and HR
  • SKILL.md covers Start from the assumption that…, Recruit the people who have…, Interview about the past, not… and Weigh what people do over what…, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Product Discovery is an agent skill from cbrock84/headcount. Finds out whether a problem is real and a solution would work, before building it — recruiting the right people, interviewing without leading them, separating what users say from what they do, naming the riskiest assumption and testing that one first, and reaching a decision rather than a summary. Use this to validate a problem, test an idea cheaply, decide whether to build something, or fix a discovery process that keeps confirming what the team already believed.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Product & Project Management, covering Recruiting and HR. The repository describes itself as: An agent organization structured as a company — 15+ departments, 125+ skills, each independently installable, citing the standards and regulators that settle the question. Runs… The licence is MIT.

When your agent uses it

  • Tasks that involve Recruiting and HR

Example prompts

  • “Use the product-discovery skill to find out whether a problem is real and a solution would work, before building it — recruiting the right people…”
  • “/product-discovery”

What it can do on your machine

Read from SKILL.md and the folder at commit 98d1c17. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Product Discovery loads about 1k tokens when it runs. Until then it costs about 122 tokens; SKILL.md has 616 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~122
When it runs · the whole SKILL.md, loaded when a task matches
~1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from cbrock84/headcount at commit 98d1c17, republished under its MIT licence (© cbrock84). 616 words, ~1,023 tokens.

Download SKILL.mdSave it as .claude/skills/product-discovery/SKILL.md (or your agent's skills folder).
name
product-discovery
description
Finds out whether a problem is real and a solution would work, before building it — recruiting the right people, interviewing without leading them, separating what users say from what they do, naming the riskiest assumption and testing that one first, and reaching a decision rather than a summary. Use this to validate a problem, test an idea cheaply, decide whether to build something, or fix a discovery process that keeps confirming what the team already believed.

Product discovery

Discovery is how you find out you were wrong while it is still cheap. A process that never kills anything is not discovery, it is a preparation ritual with research attached.

Start from the assumption that would sink this if it were false

Every idea rests on a stack: that the problem exists, that people care enough to change what they do, that your approach solves it, that they would pay, that you can build and deliver it. They are not equally uncertain, and testing them in order of comfort is how teams spend six weeks confirming the safe one.

Write the assumptions down, mark the one that would be most damaging to be wrong about, and test that one first. Usually it is the second: the problem is real, and people are living with it comfortably enough not to move.

Recruit the people who have the problem, not the people who are easy to reach

Interviewing your friendliest customers produces reliable encouragement. Talk to people who churned, people who evaluated and chose something else, and people who solved it another way — those three groups carry most of the information.

Five to eight conversations in a segment usually exhausts the new material. If you are still hearing new things at eight, the segment is too broad.

Interview about the past, not about the future

People are poor at predicting their own behavior and generous when asked to react to an idea. They are reliable narrators of what they actually did.

  • Ask about the last time it happened. What triggered it, what they tried, what it cost them, what they did instead.
  • Follow the workaround. A spreadsheet someone maintains by hand every week is stronger evidence of a real problem than any amount of enthusiasm about a proposed feature.
  • Do not describe your solution until the end, and treat everything said after that point as weaker evidence.
  • Silence is a tool. Most of the useful material arrives after the pause you were tempted to fill.

"Would you use this?" and "would you pay for this?" produce answers that do not predict anything. What predicts is whether they have already spent money or time on the problem.

Show full SKILL.md (249 more words)Show less

Weigh what people do over what they say

Rank evidence honestly: what they have paid for, what they have built themselves, what they do in an unprompted usage log, what they say in an interview, what they say in a survey. A prototype someone tries in front of you sits high on that list; an enthusiastic reaction to a mockup sits low.

Test with the cheapest thing that could produce a real signal

Match the artifact to the assumption. Demand risk is testable with a landing page or a sales conversation, usability risk with a rough prototype, feasibility with a spike. Building a working version to test whether anyone wants it is the expensive way to answer the cheap question.

Define the outcome that would change your mind before you run it. A test with no failing threshold is a demonstration.

End with a decision

Discovery output is build, do not build, or a specific next test — not a deck of themes. Write what you learned, what you still do not know, and what you are doing about it, and keep the record of the ideas you killed and why. That record is what stops the same idea returning every six months.

Never

  • Test the assumption you are most confident about first.
  • Ask whether someone would use a thing instead of what they did last time it came up.
  • Recruit only from customers who already like you.
  • Run a test with no threshold that would have counted as failure.

© cbrock84, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/product/skills/product-discovery of cbrock84/headcount.

Open the folder on GitHubat commit 98d1c17

Compare with similar skills

Product Discovery next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Product Discovery compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Product Discovery this skillcbrock84/headcount2k—~1kAutomated safety check: PassMIT
UX Researchrampstackco/claude-skills945—~2.7kAutomated safety check: PassMIT
User Research PlanningProrise-cool/Claude-Code-Multi-Agent306—~3.7kAutomated safety check: PassNone
Expert Panelericosiu/ai-marketing-skills3.6k2 repos~2.1kAutomated safety check: PassMIT
Job Application Managerreactive-resume/reactive-resume44k—~13kAutomated safety check: PassMIT
TAM SAM SOM Calculatordeanpeters/Product-Manager-Skills7.2k1 repos~4.8kAutomated safety check: PassCustom licence

Similar skills

  • UX Research

    rampstackco/claude-skills

    Plan and execute user research including research planning, recruiting, interview design, qualitative synthesis, and translating findings into product decisions.

    945 GitHub stars~2.7k tokensUpdated 3 days ago
    Product & Project ManagementAuto-check passed

  • User Research Planning

    Prorise-cool/Claude-Code-Multi-Agent

    Plan user research studies - method selection, participant recruitment, study design, and research questions for generative and evaluative research.

    306 GitHub stars~3.7k tokensUpdated 24 days ago
    Product & Project ManagementAuto-check passed

  • Expert Panel

    ericosiu/ai-marketing-skills

    Score, evaluate, and iteratively improve any content or strategy using an auto-assembled panel of domain experts.

    3.6k GitHub starsUsed in 2 repos~2.1k tokens
    Frontend & DesignAuto-check passed

  • Job Application Manager

    reactive-resume/reactive-resume

    Runs the job-search pipeline. An agent skill from reactive-resume/reactive-resume.

    44k GitHub stars~13k tokensUpdated today
    Business, Finance & HRAuto-check passed

  • TAM SAM SOM Calculator

    deanpeters/Product-Manager-Skills

    Calculates total, serviceable available and serviceable obtainable market size for a product idea with explicit assumptions, methods and caveats.

    7.2k GitHub starsUsed in 1 repo~4.8k tokens
    Product & Project ManagementAuto-check passed

  • Mock Interview

    reactive-resume/reactive-resume

    Runs a mock interview for a specific job description. An agent skill from reactive-resume/reactive-resume.

    44k GitHub stars~11k tokensUpdated today
    Business, Finance & HRAuto-check passed

More from cbrock84/headcount

All 178 skills in this repo

  • Agent Hierarchy

    cbrock84/headcount

    Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a…

    2k GitHub stars~1.2k tokensUpdated 23 days ago
    Auto-check passed

  • Access And Identity

    cbrock84/headcount

    Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process.

    2k GitHub stars~1.1k tokensUpdated 23 days ago
    Auto-check passed

  • Account Based Marketing

    cbrock84/headcount

    Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group…

    2k GitHub stars~1.2k tokensUpdated 23 days ago
    Auto-check passed

  • Activation

    cbrock84/headcount

    Gets new users from signup to first real value — signup flow, onboarding, time-to-value, and the early experience that determines whether someone becomes a user or a lapsed account.

    2k GitHub stars~865 tokensUpdated 23 days ago
    Auto-check passed

  • AI ML Governance

    cbrock84/headcount

    Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire.

    2k GitHub stars~1k tokensUpdated 23 days ago
    Auto-check passed

  • AI Research Analyst

    cbrock84/headcount

    Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit.

    2k GitHub stars~916 tokensUpdated 23 days ago
    Auto-check passed

Categories

Questions about Product Discovery

What does Product Discovery do?

Finds out whether a problem is real and a solution would work, before building it — recruiting the right people, interviewing without leading them, separating what users say from what they do…. Product Discovery is an agent skill from cbrock84/headcount. Finds out whether a problem is real and a solution would work, before building it — recruiting the right people, interviewing without leading them, separating what users say from what they do, naming the riskiest assumption and testing that one first, and reaching a decision rather than a summary.

When should I use Product Discovery?

Product Discovery fits situations like: tasks that involve Recruiting and HR.

How do I install Product Discovery in Claude Code?

Run `npx skills add cbrock84/headcount --skill product-discovery -a claude-code`. Or copy the skill folder (plugins/product/skills/product-discovery in cbrock84/headcount) into .claude/skills/product-discovery in your project. Claude Code loads it when a task matches its description.

How do I install Product Discovery in Codex?

Run `npx skills add cbrock84/headcount --skill product-discovery -a codex`. Or copy the skill folder (plugins/product/skills/product-discovery in cbrock84/headcount) into .agents/skills/product-discovery in your project. Codex loads it when a task matches its description.

Can I use Product Discovery in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cbrock84/headcount --skill product-discovery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-discovery, .gemini/skills/product-discovery, .github/skills/product-discovery and .opencode/skills/product-discovery in your project.

What does Product Discovery need to run?

SKILL.md names no scripts, command-line tools or credentials: Product Discovery is instructions for the agent only.

Does Product Discovery access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Product Discovery safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Product Discovery use?

Product Discovery is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Product Discovery use?

About 1k tokens (SKILL.md is roughly 4.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Product Discovery?

Skills that share tags, products or a category with Product Discovery: UX Research (rampstackco/claude-skills, 945 stars), User Research Planning (Prorise-cool/Claude-Code-Multi-Agent, 306 stars), Expert Panel (ericosiu/ai-marketing-skills, 3.6k stars) and Job Application Manager (reactive-resume/reactive-resume, 44k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Discovery?

cbrock84 (a GitHub user) maintains it in cbrock84/headcount, which has 2,022 GitHub stars. The repository holds 178 skills in this directory. The repository was last updated on September 17, 2026.

Source: cbrock84/headcount on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.