# Homeric Product Discovery

Continuous-discovery coaching skills (Teresa Torres's Continuous Discovery Habits + Strategyzer experiment practice) plus the free Product OKR skills, callable from Claude, Claude Code, Cursor, Windsurf, and Atlassian Rovo over the Model Context Protocol.

MCP endpoint: https://mcp.homeric.ai/product-discovery

## Connect your agent

Add https://mcp.homeric.ai/product-discovery as a remote MCP server (HTTP transport). On the first tool call your agent opens a browser to sign in with your Homeric account — the server is an OAuth resource server and WorkOS AuthKit issues the tokens.

Example mcpServers config (Claude Code, Cursor, Windsurf):

    {
      "mcpServers": {
        "product-discovery": {
          "type": "http",
          "url": "https://mcp.homeric.ai/product-discovery"
        }
      }
    }

## Skills (11)

### discovery_experiments_create
Experiment - Create

Turns one assumption into a runnable Experiment Card per Strategyzer's Testing Business Ideas: 1-3 recommended types from the 44-experiment library, the four statements on their stems, risk type, lifecycle fields, tree connections, and the artifact the test needs.

### discovery_experiments_review
Experiment - In-Depth Review

The deep coaching read of an Experiment Card: a markdown narrative walking hypothesis quality, experiment fit, evidence discipline, criteria rigor, plus learnings when completed, with the why and framework references. Use experiment-score for the fast scorecard.

### discovery_experiments_score
Experiment - Score

A fast Experiment Card scorecard: star ratings for Hypothesis, Test, Metric, Criteria, plus Learnings when the card is completed, with a couple of coaching nudges to improve it. Scored against Strategyzer's Testing Business Ideas. Use experiment-review for the deep read.

### homeric_message_product_coach
Homeric - Message Product Coach

Message a human Homeric product coach: ask a question, or check for a reply to a previous one.

### discovery_opportunity_trees_create
Opportunity Solution Tree - Create

Builds a Homeric Opportunity Solution Tree from a Key Result plus customer evidence, per Teresa Torres: HMW-framed problems traced to evidence, one target problem, 2-3 concise ideas, and experiment stubs for the riskiest idea. Siblings: opportunity-tree-score / review.

### discovery_opportunity_trees_review
Opportunity Solution Tree - In-Depth Review

The deep coaching read of an Opportunity Solution Tree on the Homeric mapping: a markdown narrative walking outcome quality, problem framing (HMW), idea quality, and experiment linkage: with the why and framework references. Use opportunity-tree-score for the fast scorecard.

### discovery_opportunity_trees_score
Opportunity Solution Tree - Score

A fast OST scorecard: star ratings for Outcome, Problems, Ideas, and Experiments, the tree's four layers on the Homeric mapping (KR root, HMW problems, concise ideas, experiment stubs), with a couple of coaching nudges. Use opportunity-tree-review for the deep read.

### discovery_opportunity_trees_jira_sync
Opportunity Solution Tree - JIRA Sync

Pushes opportunity-tree work into Jira Product Discovery and Atlassian Goals via the user's Atlassian MCP connection, problems, ideas, experiments, the Objective's Goal, wiring the links and contributions that make JPD's tree view render. Composable actions.

### discovery_okrs_create
OKRs - Create

Proposes an improved OKR set on top of the user's input (or drafts one from a strategic priority brief) with per-dimension rationale per the SVPG/Wodtke framework. Use `product-okr-score` for a quick score; use `product-okr-review` for a thorough read without a rewrite.

### discovery_okrs_review
OKRs - In-Depth Review

Conversational coaching review of a product OKR set against the SVPG/Wodtke framework. Calls out what's working and what to address per dimension, in a warm teammate voice. Use `product-okr-score` for a quick verdict; use `product-okr-create` for a rewrite.

### discovery_okrs_score
OKRs - Score

A fast OKR scorecard: a 3-star rating across Context, Objective, Key Results, and Ops Metrics, scored underneath against the full SVPG/Wodtke framework, plus a few friendly ideas to improve. Use product-okr-review for the deep per-dimension read.

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Manage your skills, connections, and activity at https://mcp.homeric.ai