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Found 3,116 Skills
Enforces complete execution, mode-aware delivery, compact sub-agent communication, independent agent-review gating, validation, and reporting for implementation, bugfix, hardening, documentation, specification, architecture, design, review, and post-mortem tasks. Use whenever work must be completed, reviewed, validated, or documented through an explicit execution mode instead of handled ad hoc.
Shopify Functions allow developers to customize the backend logic that powers parts of Shopify. Available APIs: Discount, Cart and Checkout Validation, Cart Transform, Pickup Point Delivery Option Generator, Delivery Customization, Fulfillment Constraints, Local Pickup Delivery Option Generator, Order Routing Location Rule, Payment Customization
Builds production-ready REST API endpoints with validation, error handling, authentication, and documentation. Follows best practices for security and scalability.
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.
Evaluates ML models for performance, fairness, and reliability. Use for metric selection, cross-validation strategies, overfitting/underfitting diagnosis, hyperparameter tuning, LLM evaluation, A/B testing, and production monitoring for model drift.
Goose-native software delivery command suite for product validation, scope challenge, planning, TDD implementation, debugging, review, QA, and release handoff
Node.js/Bun backend reference skill: TypeScript-first, structured error handling, pino logging, Zod validation, async patterns, HTTP server conventions, database access, auth, queues, caching, testing, security, CLI tooling, and observability. Covers both Node.js and Bun runtimes. Use when the task touches server-side TypeScript/JavaScript code and should follow the project's backend conventions.
Guides edge and tactical autonomous systems—perception-planning-control under latency and safety constraints; behavior trees/state machines vs learned policies; human-on-the-loop; geofencing, no-strike rules, mission abort; sim and field testing; ROS2/middleware patterns; sensor fusion; degraded modes; autonomy audit logging. Use for UAS/autonomous stacks, safety rules, HITL, sim-to-field validation, fail-safe—not LLM products (ai-engineer), LLM red team (ai-redteam), safeguard serving (ml-infrastructure-engineer-safeguards), governance only (ai-risk-governance), MCU firmware without autonomy (embedded-real-time-software-engineer), plant PLC/DCS (control-software-developer), HIL security bench (hardware-in-the-loop-security-tester).
Guides cleaning and standardizing tabular datasets before analysis, modeling, or reporting—profiling, quality rules, missing values, duplicates, outliers, type coercion, encoding fixes, record linkage, deduplication, high-level PII handling (not legal advice), actuarial/insurance field scrubbing, reproducible scrub pipelines, validation checks, and sign-off. Distinct from warehouse ETL or statistical modeling. Use when the user asks for "data scrubbing", "clean this dataset", "scrub the data", "data cleaning", "dedupe records", "handle missing values", "outlier treatment", "standardize columns", "data quality rules", "profile this table", or "prepare data for modeling". Not warehouse pipelines (data-warehouse-engineer), ML modeling (data-scientist, actuary), privacy programs (compliance-engineer), FinOps only (finops-analyst), or assumption governance (assumption-setting).
Guides cybersecurity isolation controls using MITRE D3FEND—access mediation, content filtering, execution isolation, and network segmentation. Covers access policies, permissions, content validation, process isolation, allowlisting, and traffic filtering. Use when segmenting networks, restricting access, filtering content, or isolating execution—not for detection (d3fend-detect), hardening (d3fend-harden), or deception (d3fend-deceive).
This skill should be activated when the user requests to "deepen a topic", "analyze a topic", "help me write an outline", "will this topic go viral", "help me diagnose a topic", "is this topic worth pursuing", or "how to improve this topic". Even if the user only shares a topic and asks for opinions, you should proactively initiate the diagnosis process instead of providing a simple response. Driven by the cognitive hijacking theory, it features four modules: Perspective Collision (challenging the topic's premise), Topic Diagnosis (graded using 🛵🚗✈️), Outline Design (emotional peak planning), and Style Validation (alignment with li-writer style). It generates a comprehensive deepening report and saves it as a file. Do NOT trigger this skill for: merely recording topics (use li-recorder), directly writing scripts (use li-writer). Use when the user wants to "develop a topic", "analyze topic potential", "write an outline", "will this topic go viral", or needs full topic diagnosis + outline design. Trigger even if the user just shares a topic and asks for opinions.