pm-ai

Plugin pm-ai from marketplace pm-claude-skills in mohitagw15856/pm-claude-skills. Commands read from the repo's manifest — yesterday.

Skills (13)

• agent-design-review

  Review an LLM agent design and find where it will be unreliable, expensive, or unsafe. Use when asked to review an agent architecture, critique a multi-step/tool-using agent, debug an agent that loops or goes off-task, or harden an agent before launch. Produces a structured review — task fit, contro

• agent-spec

  Specify an autonomous or tool-using AI agent before building it. Use when asked to design an AI agent, define an agent's tools and guardrails, scope what an agent is allowed to do, or write an agent spec/PRD. Produces an agent spec — goal & scope, tools with permissions, the control loop, guardrails

• ai-eval-plan

  Design an evaluation plan for an LLM or AI feature before shipping it. Use when asked how to evaluate a prompt/model/agent, set up an eval harness, define quality metrics for an AI feature, or build a regression gate. Produces an eval plan — task definition, datasets, metrics & rubrics, baselines, a

• ai-feature-prd

  Write a PRD for an AI-powered feature, covering the things normal PRDs miss. Use when asked to spec an AI/LLM feature, write a PRD for a feature that uses a model, or plan an AI capability (assistant, summarizer, generator, classifier). Produces an AI feature PRD — problem & UX of uncertainty, model

• dataset-datasheet

  Document a dataset so others know what it is, how it was made, and when not to use it. Use when asked to write a datasheet for a dataset, document training/eval data, or assess whether a dataset is fit for a use. Produces a datasheet — motivation, composition, collection process, preprocessing, reco

• eval-rubric-designer

  Design a scoring rubric and LLM-as-judge prompt to evaluate the quality of an AI feature's output. Use when asked to create an eval rubric, define quality dimensions, build an LLM judge, or decide how to measure whether AI output is good. Produces a rubric with weighted dimensions and concrete 1–5 a

• llm-cost-latency-budget

  Model the cost and latency of an LLM feature before it ships and surprises the bill. Use when asked to estimate LLM API costs, set a latency/token budget, decide which model tier to use, or bring down the cost of an AI feature. Produces a cost & latency budget — token math per request, monthly cost

• llm-guardrails-spec

  Specify the safety and reliability guardrails for an LLM feature before it ships. Use when asked to define LLM guardrails, add safety controls to an AI feature, prevent prompt injection or jailbreaks, or harden a chatbot/agent against misuse. Produces a guardrails spec — threats, input/output contro

• model-card

  Document a deployed ML/AI model so others can use it responsibly. Use when asked to write a model card, document a model's intended use and limitations, or prepare an AI model for review/launch. Produces a complete model card — intended use, training data, evaluation metrics across slices, limitatio

• model-selection-advisor

  Choose the right LLM for a task by trading off quality, cost, latency, and constraints. Use when asked which model to use, whether to upgrade/downgrade a model, how to cut LLM costs without hurting quality, or to justify a model choice. Produces a recommendation with the decision criteria, a per-opt

• prompt-optimizer

  Diagnose and rewrite an underperforming LLM prompt so it produces reliable, well-structured output. Use when asked to improve a prompt, fix a prompt that gives inconsistent or wrong results, reduce hallucination/refusals, or make output follow a format. Produces a rewritten prompt with a diagnosis o

• rag-architecture-review

  Review an existing Retrieval-Augmented Generation system and find why it underperforms. Use when asked to review or audit a RAG pipeline, diagnose wrong/ungrounded answers from a 'chat with your docs' feature, or improve an already-built knowledge assistant. Produces a staged review — ingestion, chu

• rag-design-doc

  Design a Retrieval-Augmented Generation system end to end. Use when asked to design a RAG pipeline, a 'chat with your docs' feature, a knowledge assistant, or to debug why a RAG system gives wrong/ungrounded answers. Produces a RAG design doc — ingestion & chunking, embeddings & index, retrieval & r

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