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Agent Skill

Discovery Process skill

discovery-processdeanpeters/Product-Manager-Skillsskills/discovery-process/SKILL.md

Run a full discovery cycle from problem hypothesis to validated solution. Use when a team needs a structured path through framing, interviews, synthesis, and experiments.

7.6k

always on

34 tokname + description

once used

4,453 tokSKILL.md body

bundled files

39 KB

repo stars

7.6kGitHub

Install the Discovery Process skill

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

npx skills add https://github.com/deanpeters/Product-Manager-Skills --skill discovery-process

Claude Code, by hand (all projects)

git clone --depth 1 https://github.com/deanpeters/Product-Manager-Skills.git /tmp/Product-Manager-Skills
mkdir -p ~/.claude/skills
cp -R /tmp/Product-Manager-Skills/skills/discovery-process ~/.claude/skills/discovery-process

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 description34 tok
When the agent uses itThe SKILL.md instructions4,453 tok
Only if opened3 bundled files (9 KB)varies

Until it is used, discovery-process costs about 40× less than the median MCP server we measured (1,362 tokens on every turn). Installing all 77 skills from deanpeters/Product-Manager-Skills would load 2,883 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.

  • Purpose
  • Input
  • Key Concepts
  • What is the Discovery Process?
  • Why This Works
  • Anti-Patterns (What This Is NOT)
  • When to Use This
  • When NOT to Use This
  • Facilitation Source of Truth
  • Application
  • Phase 1: Frame the Problem (Day 1-2)
  • Activities
  • Outputs from Phase 1
  • Decision Point 1: Do we have enough context to start research?
  • Phase 2: Research Planning (Day 3)
  • Activities
  • Outputs from Phase 2
  • Phase 3: Conduct Research (Week 1-2)
  • Activities
  • Outputs from Phase 3
  • Decision Point 2: Have we reached saturation?
  • Phase 4: Synthesize Insights (End of Week 2)
  • Activities
  • Outputs from Phase 4

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