product-manager-skills
claudeAll PM skills in one install — discovery, strategy, delivery, finance, career, lifecycle, and more. Choose this OR individual skills, not both.
6 commands
Diagnose context stuffing vs. context engineering in AI workflows.
Plugin context-engineering-advisor from marketplace pm-skills in deanpeters/Product-Manager-Skills. Commands read from the repo's manifest — today.
context-engineering-advisorDiagnose context stuffing vs. context engineering. Use when an AI workflow feels bloated, brittle, or hard to steer reliably.
All PM skills in one install — discovery, strategy, delivery, finance, career, lifecycle, and more. Choose this OR individual skills, not both.
6 commands
Evaluate acquisition channels using unit economics, customer quality, and scalability.
1 skill
Design multi-agent AI workflows with clear boundaries, handoffs, and monitoring.
1 skill
Assess whether your product work is AI-first or AI-shaped across 5 competencies.
1 skill
Understand the PM-to-Director transition through altitude and horizon thinking.
1 skill
Map evidence-backed growth options across the Ansoff Matrix with risk-rated sequencing.
1 skill
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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