Synthesize Learnings

Creates experiment readouts, codifies learnings, and routes follow-up actions.

Published by @gtmagents·0 agent reads / 30d·0 saves·Updated June 14, 2026

Command: synthesize-learnings

Inputs

source – experiment tracker, warehouse table, or analytics workspace.
scope – filters (timeframe, product area, funnel stage, persona).
audience – exec | pod | growth-guild | async memo; controls fidelity/tone.
format – deck | memo | dashboard | loom.
follow-ups – optional CSV/JSON for linking Jira/Asana action items.

Workflow

Data Consolidation – assemble final metrics, guardrail outcomes, and qualitative notes.
Insight Extraction – group learnings by hypothesis theme, persona, or funnel stage.
Decision Encoding – record win/ship, iterative, or archive outcomes plus rationale.
Action Routing – create follow-up stories, backlog items, or automation triggers.
Knowledge Base Update – tag learnings in centralized library with attribution + status.

Outputs

Executive-ready readout with insights, decisions, and KPIs.
Learning cards mapped to hypothesis taxonomy + next bets.
Action log synced to backlog/project tools.

Agent/Skill Invocations

insight-analyst – leads analysis and storytelling.
experimentation-strategist – ensures learnings feed roadmap + governance.
hypothesis-library skill – indexes learnings against taxonomy.
experiment-design-kit skill – suggests iteration ideas based on patterns.

More on the bench

Monitor Performance

Aggregates channel analytics, benchmarks, and insights to optimize social programs.

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Product Research

Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.

Alireza Rezvani

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Product Manager Toolkit

Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market strategies. Use when prioritizing features, synthesizing user research, writing requirement documentation, or developing product strategy.

Alireza Rezvani

product-management+2

Sharebench · Public bench

Connect via MCP.

Add the public bench to any AI tool. Your agents read every artifact you see — no signup needed.

1Pick your tool

# Add the public bench as an MCP server
claude mcp add --transport http sharebench-public https://mcp-public.sharebench.ai

# Settings → MCP → Add new MCP server
{
  "mcpServers": {
    "sharebench-public": {
      "url": "https://mcp-public.sharebench.ai"
    }
  }
}

# Custom GPT or Developer Mode
# Add a remote MCP connector with this URL:
https://mcp-public.sharebench.ai

# Append to ~/.codex/config.toml
[mcp_servers.sharebench_public]
url = "https://mcp-public.sharebench.ai"

2Ask your agent for an artifact

Once connected, just ask in plain language — your agent searches the bench and pulls in what it needs.“Pull the brand voice from sharebench and rewrite this draft.”