Results for “great-expectations”

Run machine learning and generative AI tasks directly in BigQuery SQL using built-in functions for forecasting, anomaly detection, key driver analysis, and text generation.

Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness, returning a structured report with exact patch locations.

Profiles datasets automatically to assess data quality, structure, and completeness, generating reports with ydata-profiling, pandera, or manual pandas methods.

Advanced Big Data Design Skill

Creates a dramatic 'giant product' visual by compositing a product image into a scene where it appears building-sized next to a person, with an optional animation step.

CEO/founder-mode plan review. Rethink the problem, find the 10-star product, challenge premises, expand scope when it creates a better product. Four modes: SCOPE EXPANSION (dream big), SELECTIVE EXPANSION (hold scope + cherry-pick expansions), HOLD SCOPE (maximum rigor), SCOPE REDUCTION (strip to essentials). Use when asked to "think bigger", "expand scope", "strategy review", "rethink this", or "is this ambitious enough". Proactively suggest when the user is questioning scope or ambition of a plan, or when the plan feels like it could be thinking bigger. (gstack)

Designs and implements big data architectures, processes large-scale datasets with distributed systems, and optimizes data pipelines for throughput using Hadoop, Spark, and cloud platforms.

Create irresistible offers using the Value Equation, bonus stacking, risk-reversing guarantees, and ethical scarcity.

Grill the user relentlessly about a plan or design. Use when the user wants to stress-test a plan before building, or uses any 'grill' trigger phrases.

Use for writing, reviewing, debugging, or testing publication-quality display tables in Python with Great Tables, including GT construction, stub and row groups, headers and spanners, labels, numeric/date formatting, targeted styles, footnotes/source notes, HTML/LaTeX/image export, and render verification. Do not use for dataframe computation, interactive data grids, charts, or plain console tables.

Multi-phase dialectical stress-test for HIGH-STAKES decisions only (architecture choices, irreversible product calls, strategic bets). Heavy-cost skill — do NOT use for routine questions, brainstorming, or simple tradeoffs. User must explicitly invoke or describe a decision they call "high-stakes", "irreversible", or "needs stress-testing". Triggers: "stress test this decision", "dialectic on", "challenge this thesis", "should I really".

A relentless interview to sharpen a plan or design.

Design the deeper patterns, structures, artefacts, and mental models needed to grow a desired event. Use when a class imagines a better future.

Guides multi-step tasks by decomposing them into checkpoints, verifying results with concrete observations, and re-planning after surprises or failures.

CEO/founder-mode plan review. (gstack)

Generates award-level UI with GSAP motion, bento grids, and Python-driven randomization for layout variety.

Big Data Based Modeling Skill

Socratic deep interview with mathematical ambiguity gating before execution

Manage an executive's schedule, inbox, and communications using GWS workflows for email triage, calendar management, and meeting preparation.

Provides Gemini CLI delegation workflows for large-context analysis tasks, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Gemini for a specific task such as broad codebase analysis or long-document processing. Triggers on "use gemini", "delegate to gemini", "run gemini cli", "ask gemini", "use gemini for this task".

A/B test design and experiment planning for paid advertising. Structured hypothesis framework, statistical significance calculator, test duration estimator, sample size calculator, and platform-specific experiment setup guides (Meta Experiments, Google Experiments, LinkedIn A/B). Use when user says A/B test, split test, experiment design, test hypothesis, statistical significance, sample size, or test duration.

Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads

Plan a chunk of work too big for one agent session by putting it on a shared whiteboard — a map of investigation tickets on GitHub Issues — working them until nothing is left to decide, then snapshotting the board into a handoff artifact. Tickets needing nobody are worked back-to-back; the session stops when the human is the blocker. Use only when the user explicitly invokes whiteboard or asks to draw, work, run, or snapshot a whiteboard/map — not for ordinary planning requests.

Socratic deep interview with mathematical ambiguity gating before explicit execution approval

For analysis/task/review agents using a Professional Skill on batch, stream, warehouse, lineage, replay, or data quality; not for transactions without distributed-data impact.

Big Data Testing Expert Skill

Big Data Design Expert Skill

Deliberative consultation with Gemini CLI. Ask, evaluate, critique, iterate until workable agreement.

Design and evaluate paid-ad experiments with hypotheses, randomization, sample-size calculations, guardrails, and decision rules for A/B and split tests.

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

Use to design or audit the executive escalation path for a major account — intake, ownership, communication cadence and follow-through — so escalations are resolved rather than merely attended. Trigger for "design our escalation process for key accounts", "an executive escalation went badly", "customer went to our CEO", red account war rooms, or escalations that get attention and then quietly stall.

Generate unique personalized first lines for hundreds of prospects using company news, LinkedIn activity, and mutual connections. Saves 10+ hours of manual research per campaign. Use when you need personalized outreach at volume.

Use when the user asks to run Gemini CLI for code review, plan review, or big context (>200k) processing. Ideal for comprehensive analysis requiring large context windows. Uses Gemini 3 Pro by default for state-of-the-art reasoning and coding.

Codex agent definition.