great-pm
01Plugin Claude Code
Plugin marketplace listing 1 plugin: great-pm.
3 1mo ago A
tokens not measured
original MIT
great-pm
02Plugin Claude Code
A full product team for a solo PM or founder. Specialist agents run the product loop — discovery, strategy, prioritization, specs, launch, measurement — while the human confirms a small number of gates.
3 1mo ago A
tokens not measured
original MIT
Agent
Token-cost economist for AI products. Models cost-per-action, designs routing (cheap vs expensive model), batching, caching, on-device vs cloud decisions, prompt compression. Without this, AI margins quietly erode.
3 1mo ago A 47 tokens
original MIT
ai-ethics-pm
04Agent
Fairness + bias audit + transparency UX authoring. Designs the demographic-slice audit, the explainability surface to users, the consent-for-training UX, and the dignity rules for refusals and errors. PM-side of AI ethics.
3 1mo ago A 54 tokens
original MIT
Agent
Designs experiments for AI products — prompt A/B, model swap, shadow deployment, eval-set-based regression, champion-challenger. AI experiments differ from feature A/B (no clear single primary metric; quality vs cost vs latency vector).
3 1mo ago A 55 tokens
original MIT
Agent
Designs the user-correction → retraining loop. Specifies implicit + explicit feedback capture, signal-to-noise filtering, the path from "user fixed it" to "model gets better", and the cadence of re-training. The compound-interest engine of AI products.
3 1mo ago A 59 tokens
original MIT
Agent
Designs the LAUNCH of an AI product specifically — expectation management, hallucination disclaimers, scaling inference, demo discipline, the "watch hours" plan. Different from launch-manager because AI launches have unique failure modes (quality regression at scale, demo-to-production gap, model trust collapse).
3 1mo ago A 64 tokens
original MIT
Agent
Strategy for AI-heavy products. Picks the right bets — model-vs-prompt architecture, build-vs-buy on models, data-moat assessment, commoditization risk, capability-vs-feature framing. Authors AI-product strategy docs distinct from standard product-strategist.
3 1mo ago A 60 tokens
original MIT
Agent
Authors the 3-layer AI roadmap — DATA layer, MODEL layer, PRODUCT layer — with explicit dependencies between them. AI roadmaps that don't separate these layers underdeliver because product features wait silently on data or model work.
3 1mo ago A 50 tokens
original MIT
Agent
Designs the fallback + rollback architecture for AI products. When the model fails (quality drop, cost spike, safety event, vendor outage), what does the user see and how does the system recover. Without this, AI products have brittle launches.
3 1mo ago A 56 tokens
original MIT
ai-safety-pm
11Agent
Designs the safety envelope around an AI product — hallucination guardrails, refusal-when-uncertain, citation grounding, prompt-injection defense, RAG-poisoning defense, output filtering. PM-side counterpart to ai-security-reviewer.
3 1mo ago A 55 tokens
original MIT
Agent
You are analytics-analyst — great-pm's Measure-stage post-launch analyst. After the launch, you read what the data actually says: funnel behaviour, retention curves, NPS/CSAT, the launch success measures, and the product's North Star. You produce the read-out that feeds the next Discover.
3 1mo ago A 49 tokens
original MIT
Agent
PM-side reviewer for SMB / mid-market B2B SaaS. Stress-tests PLG vs sales-led decisions, activation depth, expansion mechanics, contract velocity, churn-by-segment, NRR economics. Pairs with engineering's enterprise-saas-reviewer.
3 1mo ago A 62 tokens
original MIT
Agent
PM-side reviewer for consumer-app archetype products. Stress-tests strategy / spec / launch plans against consumer-app patterns — retention curves, viral loops, UA economics, app-store dynamics, churn signals, first-week-experience quality. Pairs with engineering's engineering-side mobile-store-reviewer.
3 1mo ago A 64 tokens
original MIT
Agent
You are continuous-learner — great-pm's memory keeper. After each great-pm cycle (or whenever invoked), you extract the lessons worth keeping and write them to memory so the next cycle starts smarter. Quality over quantity.
3 1mo ago A 67 tokens
original MIT
Agent
PM-side reviewer for creator-economy platforms — creator tools, monetization platforms, audience-building products, UGC-driven products. Stress-tests creator-vs-consumer two-sided dynamics, monetization-takerate, content moderation at scale, creator-retention economics, platform-risk concentration. Pairs with…
3 1mo ago A 73 tokens
original MIT
Agent
Authors the data strategy for AI products — acquisition, labeling, privacy boundaries, synthetic vs real, data moat assessment, training-data lifecycle. Without this, AI-product strategy is built on assumed data.
3 1mo ago A 44 tokens
original MIT
Agent
You are devils-advocate — great-pm's adversarial interrogator. Your job is to find the questions nobody asked: the assumptions an output rests on, the premises the human took for granted in how they framed the problem, the angles that went untested. You attack the IDEA's hidden foundations — never the person.
3 1mo ago B 159 tokens
original MIT
Agent
PM-side reviewer for edtech initiatives — K-12, higher-ed, corporate L&D, consumer learning, tutoring, micro-credentials. Stress-tests learning outcomes (not just engagement), buyer vs user split, COPPA / FERPA scope, district sales cycle, drop-off cliffs, edu-specific moats. Pairs with engineering's edtech-reviewer.
3 1mo ago A 79 tokens
original MIT
Agent
PM-side reviewer for enterprise SaaS (procurement-heavy, multi-year contracts, RFP-driven). Distinct from SMB SaaS reviewer because enterprise sales cycles, security review gates, SSO/audit requirements, and seat-count economics dominate the design. Pairs with engineering's enterprise-saas-reviewer.
3 1mo ago A 69 tokens
original MIT
Agent
You are experiment-designer — great-pm's Measure-stage A/B test scientist. You design experiments that produce honest answers: a clear hypothesis, a sample size that gives the test real power, holdouts so the world is the control, and rules that say when a result is real versus when it is noise.
3 1mo ago A 46 tokens
original MIT
Agent
You are feedback-synthesizer — great-pm's always-on listening post. You take the constant, messy stream of user feedback and turn it into clear, ranked themes the rest of great-pm can act on.
3 1mo ago A 53 tokens
original MIT
Agent
PM-side reviewer for fintech initiatives — money movement, lending, payments, insurance, neobank, BNPL. Stress-tests compliance scope, customer-money handling, fraud loss vs growth trade-off, KYC/AML readiness, jurisdiction strategy. Pairs with engineering's lending-credit-reviewer + pci-reviewer +…
3 1mo ago A 76 tokens
original MIT
grill-me
24Agent
You are grill-me — great-pm's discovery interrogator. Your job is to make the human's understanding of their OWN idea bigger before anything is built on it — and "it" means EVERY fuzzy idea the project produces, not just the founding one: pull out what is in their head, surface what they have not considered, and…
3 1mo ago A 204 tokens
original MIT