Skip to content
mcp/skillhub

Agent Skill

Product Discovery skill

product-discoverycbrock84/headcountplugins/product/skills/product-discovery/SKILL.md

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.

2.1kMIT

always on

93 tokname + description

once used

759 tokSKILL.md body

bundled files

0

repo stars

2.1kGitHub

Install the Product Discovery skill

Skills CLI (Claude Code, Codex, Cursor and others)

npx skills add https://github.com/cbrock84/headcount --skill product-discovery

Claude Code, by hand (all projects)

git clone --depth 1 https://github.com/cbrock84/headcount.git /tmp/headcount
mkdir -p ~/.claude/skills
cp -R /tmp/headcount/plugins/product/skills/product-discovery ~/.claude/skills/product-discovery

For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.

What it costs your context

Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.

WhenWhat loadsCost
Every sessionName and description93 tok
When the agent uses itThe SKILL.md instructions759 tok
Only if openedNo bundled files—

Until it is used, product-discovery costs about 15× less than the median MCP server we measured (1,363 tokens on every turn). Installing all 150 skills from cbrock84/headcount would load 11,321 tokens of descriptions. Skills vs MCP study

Checks

Our checks found nothing to flag: it follows the Agent Skills specification's naming and size guidelines and doesn't pipe downloads into a shell.

Checked 2026-10-22 · method

What's inside

The headings of its SKILL.md. Read the full file on GitHub.

  • Product discovery
  • Start from the assumption that would sink this if it were false
  • Recruit the people who have the problem, not the people who are easy to reach
  • Interview about the past, not about the future
  • Weigh what people do over what they say
  • Test with the cheapest thing that could produce a real signal
  • End with a decision
  • Never

More skills from cbrock84/headcount

All skills in this repo
  1. access-and-identitycbrock84/headcount

    Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process. Use this to design a permissions model, run an access review, reduce standing privilege, handle offboarding, set up SSO or MFA, manage service and machine credentials, or diagnose why permissions have sprawled.

    2.1k75 tok idle · 860 in use
  2. account-based-marketingcbrock84/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 inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.

    2.1k97 tok idle · 894 in use
  3. activationcbrock84/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. Use this to design or fix signup and onboarding, diagnose why signups do not convert to active use, reduce time-to-value, or decide what a new user must accomplish first.

    2.1k74 tok idle · 646 in use
  4. agent-hierarchycbrock84/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 script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.

    2.1k1130 tok idle · 1,023 in use
  5. ai-ml-governancecbrock84/headcount

    Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.

    2.1k82 tok idle · 755 in use
  6. ai-research-analystcbrock84/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. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.

    2.1k92 tok idle · 671 in use
  7. ai-search-optimizationcbrock84/headcount

    Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.

    2.1k79 tok idle · 588 in use
  8. app-store-optimizationcbrock84/headcount

    Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.

    2.1k64 tok idle · 706 in use