One Step Better AI PM

Get 1% better at AI product management every day. Pull the latest curated insights from GenAI PM, find what applies to the current project, and apply one concrete improvement.

Prerequisites

• GenAI PM subscription (free at https://genaipm.com)
• Subscriber email via GENAIPM_EMAIL env var or provided when prompted

Workflow

Phase 1: Fetch the Latest Briefs

1. Get subscriber email: check GENAIPM_EMAIL env var first, then ask the user
2. Fetch briefs:

   WebFetch https://genaipm.com/api/feed/latest?email=<email>
   

3. Parse the JSON response — data array contains up to 5 entries, each with date, title, and content (full HTML)
4. Extract key insights across all briefs:
   • New AI capabilities, model releases, API changes
   • Developer tools, frameworks, libraries
   • Real-world implementation patterns and case studies
   • Claude Code, Cursor, and AI coding assistant tips
   • Product management frameworks, methodologies, processes
   • Infrastructure, deployment, and DevOps patterns

If the API returns a 401, tell the user to subscribe at https://genaipm.com and set their email.

Phase 2: Build a Repo Profile

Create a structured summary of the project across 4 dimensions. This profile drives relevancy matching in Phase 3.

Step 1: Read universal discovery files (check each, skip if missing):

• README.md, CLAUDE.md, .cursorrules, .cursor/rules — project description and conventions
• package.json, pyproject.toml, requirements.txt, Cargo.toml, go.mod — dependencies and stack
• docs/ directory listing — look for product briefs, architecture docs, or design docs and read them
• .claude/settings.json, .claude/hooks.json, .claude/skills/ — AI assistant setup

Step 2: Scan the codebase structure:

• List top-level directories to understand project layout
• Grep for AI/LLM SDK imports (openai, anthropic, langchain, langgraph, google.generativeai, xai, cohere, replicate, huggingface, etc.)
• Grep for API keys/env vars referencing AI services
• Identify the main entry points and core business logic files

Step 3: Summarize into 4 dimensions:

1. Product/Business — What does this product do? Who is it for? What problem does it solve? What is the core user-facing value?
2. AI/ML Usage — Which AI models, APIs, and providers are used? What does the AI do in this product? (generation, curation, classification, chat, agents, embeddings, etc.) What's the AI pipeline?
3. Technology Stack — Languages, frameworks, databases, hosting, key libraries. Frontend vs backend vs infra.
4. Dev Tooling — CI/CD, testing, linting, AI coding tools (Claude Code, Cursor, Copilot), hooks, skills, MCP servers.

Write this summary internally before proceeding — it's the lens for matching briefs.

Step 4: Read .one-step-better/history.json if it exists — skip previously applied improvements.

Phase 3: Match, Rank, and Present (Approval Gate)

Do NOT proceed to Phase 4 without explicit user approval.

Step 1: Score each brief item against the repo profile.

For every distinct insight in the briefs, score it on these criteria (highest priority first):

1. Core product relevance — Does this directly relate to what the product does? (e.g., a new model for a product that uses LLMs, a curation technique for a product that curates content, a payment integration for an e-commerce product)
2. AI/ML pipeline relevance — Does this improve, extend, or optimize the AI/ML capabilities the project already uses? (e.g., a new model from a provider already in use, a better prompting technique, an evaluation framework)
3. Technology stack relevance — Does this relate to the specific frameworks, languages, or infrastructure in use? (e.g., a Next.js performance improvement for a Next.js app, a Python library for a Python project)
4. Dev tooling relevance — Does this improve the development workflow? (e.g., CI/CD, testing, AI coding tools)

Items matching criteria 1-2 should always rank above items matching only 3-4. A new model option for your AI pipeline beats a dev tooling tip every time.

Step 2: Present the top matches.

1. "Your repo profile:" — Show the 4-dimension summary from Phase 2 (2-3 sentences total) so the user can verify understanding
2. "From the latest GenAI PM briefs:" — List 2-3 highest-scoring items. For each:
   • What the brief covered (1-2 sentences)
   • Why it's relevant to this project specifically (reference the repo profile)
3. "Recommended improvement:" — For the #1 match:
   • What to do (specific and concrete)
   • Which files would be affected
   • Expected benefit
   • Estimated time to apply
4. Ask: "Want me to research this and apply it?"

Wait for the user to approve, pick a different item, or decline.

Phase 4: Deep Research & Apply

Once approved:

1. Research the source — Extract URLs from the brief item's HTML. Use WebFetch to read the original article, blog post, docs, or repo. If the brief mentions a tool or technique, search the web for official documentation.
2. Apply the improvement — Make the concrete change based on deep research and understanding of the repo. Examples:
   • Add or update Claude Code hooks, skills, or MCP configuration
   • Refactor code to use a new pattern or API from the brief
   • Add a new capability based on a tool or framework mentioned
   • Improve prompts, CLAUDE.md, or AI assistant setup
   • Update dependencies to leverage new features
3. Explain what changed — Summarize: files modified, why (linked to the brief insight), and how it helps this project

Phase 5: Track Progress

1. Create .one-step-better/history.json if it doesn't exist
2. Append an entry:

   {
     "date": "<today>",
     "briefDate": "<brief date>",
     "briefTitle": "<brief title>",
     "improvement": "<short description>",
     "filesChanged": ["<path1>", "<path2>"]
   }
   

3. Report: "You've applied N improvements from GenAI PM briefs."
4. Suggest adding .one-step-better/ to .gitignore if not already there

Guidelines

• Always wait for approval in Phase 3 before making changes
• Skip improvements already in .one-step-better/history.json
• Prioritize improvements to the core product over dev tooling — a new model option for the AI pipeline is more valuable than a linting hook
• If no briefs are relevant to the project, say so honestly and suggest checking back tomorrow
• The repo profile is the key to relevancy — spend the time to build an accurate one