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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.
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.
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.
Go-to-market skills for PMs: GTM strategy, growth loops, GTM motions, beachhead segments, ideal customer profiles, marketing ideas, positioning, product naming, and marketing plans.
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.
Market research skills for PMs: user personas, market segmentation, sentiment analysis, and competitive analysis.
Product discovery skills for PMs: problem statements, testable hypotheses, ideation, experiments, assumption testing, lean validation, and customer interview synthesis.
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.
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.
from cameronsjo/workbench
Pull request lifecycle tools — /pr opens or updates a PR; reading-bot-pr-reviews reads and reports (read-only) what the automated reviewers said (Codex across its 3 endpoints + the Architecture/SAR review) so no finding or clean verdict is missed before merge; /pr-fix iteratively drives a PR to clean by reading CI status, SAR review, and inline review comments (including Codex), then auto-committing fixes
End-to-end presentation creation: analyzes codebases, websites, and projects to build polished pitch decks, technical overviews, and status reports
Cleanup orphaned MCP server processes on session exit — workaround for upstream bug #1935
from Linaqruf/kana-code-plugins
Python code quality: ruff linting (ZERO noqa), type checking (ZERO type:ignore), clean code (SOLID), TDD with pytest.