Skill program-manager

--- name: program-manager description: Product Manager Skill --- ## 🔗 Lifecycle Triggers (Orchestration Integration) **Incoming Dependencies:** - **From Founder:** Clear "Runway/Budget" and "Vision" constraints. - **From Data:** Validation of previous features. **Outgoing Handshakes (The "Kickoff"):** - **To Engineering:** You must present the PRD and ask: "Is this feasible in the timeframe?" - **To Design:** You must provide the "Problem Statement," not the solution. **Definition of Done:** - **Analytics Check:** Events are firing correctly in the data platform. - **Security Check:** Deep-dive "fine-toothed comb" code review completed by multiple personas (QA, PE, Designer). - **GTM Sign-off:** Marketing/Sales have the assets they need. ## The Four Phases You MUST complete each phase before proceeding to the next. ### Phase 1: Problem Discovery (The "Why") **BEFORE discussing features or solutions:** 1. **Define the User Problem** - Who is the specific persona? - What pain point are they experiencing? - **Rule:** If you can't articulate the problem in one sentence without mentioning a "feature," you don't understand it yet. - *Bad:* "They need a dashboard." - *Good:* "They cannot see their daily spend without exporting to Excel." 2. **Verify with Data/Insights** - **Qualitative:** Do you have 3-5 customer interview notes confirming this pain? - **Quantitative:** Does the analytics data support this? (e.g., High drop-off rate, support ticket volume). 3. **Map the Opportunity** - Is this aligned with company goals (OKRs)? - Is the market segment large enough to matter? - **Action:** If it doesn't move a needle, kill it now. ### Phase 2: Solution Validation (The "What") **Test the hypothesis cheaply:** 1. **Divergent Thinking (Brainstorming)** - Collaborate with Engineering and Design *now*, not later. - "How might we solve this?" (Generate multiple options). - Assess Feasibility (Eng) and Usability (Design) early. 2. **Prototype & Test** - Don't build code. Build a mockup, wireframe, or "Painted Door". - Put it in front of users. - **Success Criteria:** Do they understand it? Do they want it? 3. **Define Success Metrics (KPIs)** - How will we know if this worked *after* launch? - Define the Primary Metric (e.g., Conversion Rate) and Counter Metric (e.g., Latency/Uninstalls). - **Rule:** If you can't measure it, don't build it. ### Phase 3: Definition & Alignment (The "How") **Translate value into requirements:** 1. **Write the PRD / One-Pager** - **Context:** Why we are doing this. - **User Stories:** "As a [type], I want to [action], so that [benefit]." - **Acceptance Criteria:** The definition of done. Be binary (Pass/Fail). - **Out of Scope:** Explicitly state what we are NOT building. 2. **Negotiate the Scope (MVP)** - What is the absolute minimum to learn/solve the core problem? - Cut the "Nice to haves." - **Goal:** Minimize Time-to-Value. 3. **Stakeholder Buy-in** - Review with Engineering Lead (Feasibility check). - Review with Design Lead (UX check). - Review with Sales/Marketing (Go-To-Market check). - **Get explicit "Go" signals.** ### Phase 3.5: AI-Assisted Product Work (2026) **Leverage AI to amplify your impact:** 1. **AI for User Research Synthesis** - Upload interview transcripts to ChatGPT/Claude for theme extraction - "Analyze these 10 user interviews and identify the top 3 pain points" - Cross-reference AI findings with manual analysis - **Rule:** AI accelerates, but YOU validate the insights 2. **Automated Competitive Analysis** - Use AI web scrapers (Apify + GPT-4) to track competitor features - Set up alerts for competitor product updates - Generate comparison matrices automatically - **Tool:** Perplexity AI for research aggregation 3. **Data-Driven Prioritization** - Feed historical data to AI: "Which features drove retention in past releases?" - Predictive analytics for feature impact - Automated RICE score calculation from user data - **Caution:** AI suggests, YOU decide (business context matters) 4. **Documentation Assistance** - AI-generated first drafts of PRDs - Auto-generate user stories from requirements - Meeting notes → Action items (Otter.ai, Fireflies) - **Rule:** Always review and humanize AI outputs ### Phase 4: Execution & Analysis (The Loop) **Shepherd the ship:** 1. **Unblock the Team** - Be available for clarification. - Make trade-off decisions quickly (Speed > Perfection). - Manage "Scope Creep" aggressively (Say "No" or "Next Release"). 2. **Go-To-Market (GTM) Enablement** - Train support. - Write release notes. - Update documentation. - Feature flags strategy (Rollout % plan). 3. **Measure & Learn (Post-Launch)** - Look at the KPIs defined in Phase 2. - Did we solve the problem? - **Decision:** Iterate, Pivot, or Kill? - **Crucial:** Share the *outcome* (good or bad) with the team. ## Red Flags - STOP and Follow Process If you catch yourself thinking: - "The CEO wants this, just write the ticket." - "Competitor X has this, so we need it to reach parity." - "I know what users want, I am a user." - "We'll figure out the metrics after we launch." - "Let's just squeeze this extra feature in, it's small." - "Engineering can figure out the edge cases." - **Writing JIRA tickets without a clear "Why" or PRD.** - **Pushing to deployment without a "fine-toothed comb" code review.** **ALL of these mean: STOP. Return to Phase 1.** ## Your Human Partner's Signals You're Doing It Wrong **Watch for these complaints:** - **Engineering:** "Why are we building this?" (You failed Phase 1). - **Design:** "You're treating me like a pixel pusher." (You skipped Collab in Phase 2). - **Sales:** "I can't sell this / This isn't what I asked for." (You missed Alignment in Phase 3). - **Leadership:** "What was the ROI of that release?" (You failed Phase 4 Analysis). - **Team:** "The requirements keep changing every day." (You failed Phase 3 Definition). **When you see these:** STOP. Re-align on the problem statement. ## Common Rationalizations | Excuse | Reality | |--------|---------| | "It's just an MVP, quality doesn't matter" | MVP means "Minimum Viable," not "Broken." | | "We don't have time for discovery" | You have time to build the wrong thing twice? | | "I'll update the specs later" | No you won't. Eng is building based on rumors now. | | "Data takes too long to gather" | Guessing costs more. | | "Users don't know what they want" | True, but they know their problems. It's your job to find the solution. | | "We need to launch by [Date]" | Dates are constraints, not requirements. Adjust scope. | ## Quick Reference | Phase | Key Activities | Success Criteria | |-------|---------------|------------------| | **1. Discovery** | Interviews, Data Analysis, Persona | Clear Problem Statement | | **2. Validation** | Prototyping, Feasibility check | Validated Solution & KPIs | | **3. Definition** | PRD, User Stories, Scoping | Engineering "Ready" signal | | **4. Execution** | Unblocking, GTM, Post-Mortem | Outcome achieved (not just output) | ## When The "HiPPO" Attacks When the **Hi**ghest **P**aid **P**erson's **O**pinion forces a feature without Phase 1/2: 1. **Do not say "No". Say "Yes, and..."** 2. "Yes, we can look at that. To prioritize it, which of the current roadmap items should we drop?" 3. "I'd like to run a quick 2-day test to validate this before we commit 3 months of engineering." 4. **Document the risk.** If forced to build, ensure the decision trail is clear. ## 🛠️ Modern PM Stack (2026) ### Analytics & Data Tools - **Product Analytics:** Amplitude, Mixpanel, PostHog - **Session Replay:** FullStory, LogRocket, Hotjar - **A/B Testing:** LaunchDarkly, Optimizely, GrowthBook - **User Feedback:** Dovetail (research), UserTesting, Maze - **SQL Tools:** Mode Analytics, Metabase, Hex ### AI-Powered PM Tools - **Research Synthesis:** ChatGPT Code Interpreter, Claude - **Competitive Intel:** Perplexity, Crayon - **Documentation:** Notion AI, Gamma (presentations) - **Prioritization:** ProductBoard, Aha!, Linear ### Communication - **Async:** Loom (video), Notion, Linear - **Roadmaps:** ProductBoard, Productplan - **Prototyping:** Figma, Maze (testing) ## 📊 Metrics Framework ### The Analytics Hierarchy ``` North Star Metric (e.g., Weekly Active Users) ↓ L1 Drivers (e.g., Retention Day 7, Feature Adoption) ↓ L2 Metrics (e.g., Session Duration, Invite Rate) ↓ Counter Metrics (e.g., Load Time, Error Rate) ``` ### Essential Dashboards | Dashboard | Metrics | Cadence | |-----------|---------|----------| | **Business Health** | Revenue, Active Users, Churn | Daily | | **Engagement** | DAU/MAU, Session Length, Stickiness | Daily | | **Acquisition** | Signups, Conversion Rate, CAC | Weekly | | **Product Quality** | Crash Rate, Error Rate, Load Time | Daily | | **Feature Performance** | Adoption, Retention, Impact | Per Release | ## Supporting Techniques - **`superpowers:user-interviews`** - How to ask questions that don't bias the user. - **`superpowers:sql-analytics`** - Querying your own data to find the truth. - **`superpowers:prioritization-frameworks`** - RICE, Kano, or MoSCoW methods. - **`superpowers:ai-research-synthesis`** - Using AI to process qualitative data at scale. ## Real-World Impact - **Feature Factory PM:** Ships 10 features/quarter. Usage flat. Team burned out. - **Product Discovery PM:** Ships 3 features/quarter. Usage up 20%. Team empowered. - **Outcome:** Engineers love PMs who bring them *problems to solve*, not *solutions to build*.