AI product management skills: model evaluation, responsible AI, AI metrics, prompt engineering, AI feature specs, build-vs-buy decisions, AI user research, data strategy, incident response, and competitive analysis.
Plugin pm-ai-product-management from marketplace pm-skills in tarunccet/pm-skills. Commands read from the repo's manifest — 1 month ago.
ai-build-buy-partnerEvaluate AI capability sourcing options across build, buy, fine-tune, and partner archetypes using a structured decision matrix. Use when deciding whether to build a custom model, use an LLM API, fine-tune a base model, or partner with an AI vendor.
ai-data-strategyDefine a data strategy for AI products covering training data requirements, data quality, labeling strategy, feedback loops, and continuous retraining. Use when assessing data readiness for an AI feature, designing a data flywheel, or planning an ML data pipeline.
ai-feature-definitionWrite a complete AI feature specification that defines desired model behaviour, input/output examples, confidence thresholds, fallback logic, and non-functional requirements. Use when defining requirements for an AI-powered feature, writing an AI PRD, or bridging the gap between PM intent and ML tea
ai-incident-responseHandle AI model failures, quality regressions, bias incidents, and safety issues with a structured response runbook. Use when an AI feature produces harmful outputs, when model quality degrades in production, or when preparing an incident response plan for an AI system.
ai-model-evaluationEvaluate and compare LLMs, ML APIs, and fine-tuned models for product fit across quality, latency, cost, compliance, and vendor risk dimensions. Use when selecting an AI model or vendor, comparing foundation model options, or making build-vs-API decisions for a product use case.
ai-user-researchResearch user expectations, mental models, trust calibration, and interaction patterns for AI-powered features. Use when planning user research for an AI feature, evaluating user trust and satisfaction with AI outputs, or designing feedback collection for a machine learning system.
prompt-engineeringCraft, evaluate, and manage production-quality prompts including system prompts, few-shot examples, chain-of-thought instructions, and guardrails. Use when designing prompts for a product feature, improving output quality, reducing hallucinations, or building a prompt versioning strategy.
responsible-aiAssess an AI feature or product for ethical risks, bias, safety issues, fairness gaps, and regulatory compliance. Use when reviewing an AI feature before launch, conducting a responsible AI audit, or responding to a bias or safety concern.
/pm-ai-product-management:ai-feature-launchRun a full AI feature lifecycle — from concept through model evaluation, spec, data strategy, and responsible AI review to launch-ready
/pm-ai-product-management:ai-metricsDefine success metrics for an AI feature — model quality, operational, product, and business KPIs
/pm-ai-product-management:ai-model-evalEvaluate and compare AI models or vendors for a specific product use case
/pm-ai-product-management:ai-roadmapCreate an AI product roadmap that accounts for model uncertainty, data requirements, and iterative improvement cycles
/pm-ai-product-management:ai-specCreate an AI feature specification with behaviour requirements, evaluation criteria, and fallback design
/pm-ai-product-management:responsible-ai-reviewReview an AI feature or product for ethical risks, bias, safety issues, and regulatory compliance
Execution and product management skills: PRDs, solution briefs, ADRs, edge case analysis, OKRs, roadmaps, sprints, pre-mortems, stakeholder maps, user stories, prioritization frameworks, general-purpose writing, meeting preparation, and stakeholder updates.
20 skills · 11 commands
Interactive Socratic learning modules for PMs: guided exercises, simulations, and quiz checkpoints for discovery, strategy, metrics, prioritization, user research, stakeholder management, AI product management, and vibe coding.
12 skills · 14 commands
Product strategy skills for PMs: vision, strategy canvas, value propositions, lean canvas, business model canvas, SWOT, PESTLE, Ansoff Matrix, Porter's Five Forces, monetization, pivot decisions, and devil's advocate stress-testing.
14 skills · 6 commands
Product discovery skills for PMs: problem statements, testable hypotheses, ideation, experiments, assumption testing, lean validation, and customer interview synthesis.
12 skills · 6 commands
Data analytics skills for PMs: SQL query generation, cohort analysis, A/B test analysis, funnel analysis, event tracking planning, metric definition, and North Star metric definition.
7 skills · 7 commands
Vibe coding skills for PM-builders: write AI coding specs, plan prototypes, make technical architecture decisions, review AI-generated code, run deployment checklists, debug with AI assistance, and understand codebases in PM-friendly terms.
7 skills · 6 commands
AI-powered codebase understanding — analyze, visualize, and explain any project
9 skills · 10 agents · hooks
Open-source AI job search that runs locally in your AI coding CLI: scan job portals, evaluate listings into a structured A-H report with a 1-5 score, tailor your CV, track applications
5 skills · hooks
Rewrite AI-sounding text so it reads like the writer without changing what it says.
1 skill
AI memory system — mine projects and conversations into a searchable palace. 47 MCP tools, auto-save hooks, guided setup.
3 skills · 6 commands · 1 MCP server · hooks
Perform AI code review on Git diffs — supports workspace changes, branch ranges, and single commits with concurrent per-file analysis, codebase search, and deep context-aware review.
2 commands
Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.
10 skills · 2 commands · 3 agents
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