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Found 34 Skills
QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.
Make every number in the final PDF traceable to the exact code line that produced it. Uses \hypertarget/\hyperlink LaTeX commands and \num{formula} evaluated at compile time. Use for reproducibility and data integrity verification.
Use this tool when the user explicitly requests "update project guide", "sync guide", or "deposit insights into the guide". It saves newly generated reusable writing insights from conversations to the project guide file in real time, ensuring consistent terminology, stable structure, verifiability and reproducibility. The guide file path must be specified when invoking.
Audit a CS or AI research project for reproducibility across environment, data, code, configuration, logging, and documentation. Use this skill whenever the user wants to make experiments reproducible, prepare code for collaborators, debug environment drift, write a README, package a project for paper release, or ensure they can rerun results months later.
Make research code runnable and repeatable for artifact review by auditing README, dependencies, seeds, entrypoints, data instructions, result commands, and smoke tests with repro_check.py. Use for smoke tests, seed pinning, environment capture, reproducibility checks, or works-on-my-machine cleanup.
🇺🇸 Your dev team ships features nobody asked for while user-reported bugs pile up for months. Operations blames engineering for ignoring users; engineering blames operations for not understanding technical constraints. This gives you the complete Product × Engineering × Operations alignment SOP — from unified backlog to 10-day sprint cadence to veto power rules. What's inside: • Dual-layer Kanban system (master backlog + sprint board with unified tagging) • 10-day sprint standard process (Day 1 dev → Day 6 testable build → Day 10 ship) • Issue template with reproducibility requirements (3x reported = auto-severe) • Tri-party alignment meetings (daily standup / sprint planning / sprint review) • Operations veto power on releases (P0 bug = block shipping) • User feedback → product iteration closed loop (beta testing + interview SOP) • Core metrics framework (acquisition → activation → retention → monetization → referral) • Technical debt management (20-30% sprint capacity reserved) • Ready-to-use templates: Bug Report, Sprint Planning, Responsibility Matrix Built from: Real product strategy meetings + beta testing frameworks. References Supabase sprint model, Manus/DeepSeek commercialization alignment. By @WeiYipei. 🇨🇳 Your dev team is building features no one wants, while user-reported bugs have been piling up for three months without being addressed. Operations thinks R&D ignores users; R&D thinks Operations doesn't understand technical constraints. This SOP provides you with a complete Product × Engineering × Operations collaboration framework, from unified Kanban to 10-day iteration cadence to veto power rules. 🇯🇵 Your dev team is building features no one asks for, and user-reported bugs have been left unattended for months. This SOP provides a complete collaboration framework for Product × Engineering × Operations, from unified backlog to 10-day sprints to release veto power. 🇰🇷 Your dev team is building features no one requests, and user-reported bugs have been neglected for months. This SOP provides a complete collaboration framework for Product × Engineering × Operations, from unified backlog to 10-day sprints to release veto power. Triggers: "product ops" | "engineering operations" | "product development SOP" | "sprint planning" | "iteration management" | "cross-functional alignment" | "product engineering ops" | "dev ops collaboration" | "product-engineering-ops-alignment" | "iteration-management" | "product-development-operations" | "product-development-operations" | "product-development-operations"
Paper reviewer that evaluates machine learning research projects following official ICML reviewer guidelines. Provides comprehensive reviews with actionable feedback across all key dimensions: claims/evidence, relation to prior work, originality, significance, clarity, and reproducibility. Also provides formative feedback on incomplete drafts, proposals, and research code repositories. MANDATORY TRIGGERS: review paper, ICML review, paper review, evaluate paper, research paper feedback, ML paper review, conference review, academic review, paper critique, NeurIPS review, ICLR review, project proposal, research proposal, paper draft, early feedback, incomplete paper, work in progress, WIP review, review repo, review codebase, research project review
Build, edit, redesign, render, and verify polished editable PowerPoint `.pptx` decks from a prompt, structured JSON, local data, or a saved workspace. Use for scientific, lab, clinical, consulting, board, investor, editorial, policy, and operational presentations where narrative, visual hierarchy, readability, and reproducibility matter.
Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
Principal backend engineering intelligence for Python AI/ML systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale ML services and pipelines. Focus: data quality, reproducibility, reliability, performance, security, observability, model evaluation, MLOps.
Check whether reported paper metrics are consistent with outputs from the local code artifact. Use for result reproduction checks, artifact evaluation, reproducibility checklists, code-versus-paper audits, table or claim metric comparisons, and badge-readiness review.
Execute deterministic, reproducible historical backtests from a start date, end date, and strategy concept using the Alpaca CLI plus agent-written workspace code. Use when the user wants to backtest a strategy, simulate historical trades, or return trades, diagnostics, and reproducibility artifacts.