strategy-synthesizer
The Pattern Recognizer of the PM Brain. Analyzes company and product strategy, aligns tactical work with strategic objectives, and produces executive-level memos. Use for
আপনার এজেন্টের জন্য উপযুক্ত সক্ষমতা খুঁজে নিন।
The Pattern Recognizer of the PM Brain. Analyzes company and product strategy, aligns tactical work with strategic objectives, and produces executive-level memos. Use for
Identify skill coverage gaps and improvement opportunities. Use when analyzing missing skills for a task, creating skill gap issues, evaluating skill effectiveness, or refining skill progressive disclosure.
The Archivist and Storyteller of the PM Brain. Generates weekly and monthly summaries with trajectory analysis, metrics, and executive-ready rollups. Use for
Analyzes protective collar strategies combining long stock, long put protection, and short call income. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0. Use when protecting stock positions with reduced cost, hedging downside while generating income, or implementing portfolio insurance with capped upside on mid to large-cap holdings.
Guide for debugging X-Fidelity analysis issues. Use when troubleshooting analysis failures, rule evaluation problems, VSCode extension issues, or unexpected results.
Expert-level insurance systems, underwriting, claims processing, actuarial analysis, risk assessment, and insurtech solutions
Tail risk, EVT, regularization, validation guardrails, and common pitfalls.
Analyzes iron condor credit spreads with OTM put and call spreads for range-bound trading. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting sideways price action, want to collect premium in high IV, analyzing range-bound opportunities, or implementing neutral income strategies on stocks with defined trading ranges.
Frank's personalized financial mastery and wealth coaching
Review educational content against the Four Learning Design Pillars framework. Use when users want to evaluate course materials, lessons, tutorials, e-learning modules, or any instructional content for alignment with evidence-based learning design principles. Provides structured feedback with specific principle references (e.g., 1.1.1, 2.3.4) and actionable recommendations.
Analyze RFP JSON exports to score suppliers, calculate weighted rankings, generate syntheses, compare responses, and identify risks. Use when working with RFP evaluation data in JSON format (requirements with weights, supplier responses with scores). Supports executive summaries, detailed category analysis, pairwise comparisons, and risk identification using weighted scoring methodology.
Plan development sprints for Stapledons Voyage game features. Analyzes design docs, estimates effort considering AILANG constraints, and creates realistic sprint plans. Use when user asks to "plan sprint" or estimate game feature timelines.
Generate clear, measurable, and curriculum-aligned learning objectives for any topic. Use when user asks to define goals, outcomes, or objectives for a lesson or course.
Analyzes bear-put-spread debit spreads for bearish directional plays with defined risk. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting moderate price decline, comparing put spread configurations, analyzing debit spread opportunities, or evaluating defined-risk bearish positions on mid to large-cap stocks.
Converts app ideas into shippable MVPs by providing structured product planning, technical guidance, and decision-making support focused on rapid validation. Use when the user shares an app idea, needs help scoping an MVP, wants to generate PRDs or starter prompts for Claude Code, needs product decisions during a build, or when they need to be kept focused on shipping rather than over-engineering. Also use for questions about tech stack choices (Next.js/Supabase/Vercel), feature prioritization, or avoiding common early-stage mistakes.
Expert data science guidance for analytics, data processing, visualization, statistical analysis, machine learning, and AI integration. Use when analyzing data, building ML models, creating visualizations, processing datasets, conducting A/B tests, optimizing metrics, or integrating AI features. Includes Python (pandas, scikit-learn), data pipelines, and model deployment.
Expert-level automotive systems, connected vehicles, fleet management, telematics, ADAS, and automotive software
QA workflow for software projects. Use for tasks like writing test plans, deriving test cases from PRDs, risk-based testing, API testing, UI testing, test data design, defect reporting, release checklists, and setting up automation strategy (unit/integration/e2e) with clear entry/exit criteria.
This skill should be used when implementing order management logic for Zipline strategies. It provides patterns for order placement, tracking, cancellation, fill handling, and execution analytics.