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Found 2,772 Skills
Use this skill whenever the user is working with AdonisJS v7 backend framework code: controllers, routes, middleware, services, VineJS validators, Transformers, Bouncer policies, events, listeners, mail, cache, queue, exceptions, Ace commands, request/response/session handling, or backend architecture and review. Trigger for "create a controller", "add validation", "create a service", "add a policy", "wire routes", "handle an exception", or AdonisJS backend review/debugging. For Lucid ORM, migrations, schema generation, models, relationships, query builders, transactions, factories, or seeders, use the lucid skill alongside or instead of this one. For Japa tests, use the japa skill. For Inertia frontend patterns, use inertia-react or inertia-vue alongside this one.
Implement Syncfusion Blazor notification components (Toast, Message, Skeleton) for user feedback and loading states. ALWAYS use this when users need toast notifications, popup messages, alert boxes, success/error/warning/info messages, loading skeletons, shimmer effects, content placeholders, or any feedback UI. Trigger immediately when users mention notifications, toasts, alerts, messages, loading states, skeleton screens, shimmer loading, user feedback, status messages, SfToast, SfMessage, SfSkeleton, notification popups, or need to show temporary messages, form validation feedback, or loading placeholders.
Build and troubleshoot barcode generation in Blazor using SfBarcodeGenerator, SfQRCodeGenerator, and SfDataMatrixGenerator. Trigger for 1D barcodes (Code39, Code128, Codabar), QR codes with logo and error correction, Data Matrix, checksum validation, and exporting barcodes to images in Syncfusion Blazor apps.
A comprehensive guide to implementing Syncfusion Angular Input components, including Uploader, NumericTextBox, TextBox, Signature, CheckBox, OTP Input, RangeSlider, and TextArea. This guide is intended for building Angular applications with file upload UIs supporting async and chunked uploads, drag‑and‑drop functionality, numeric inputs with validation and formatting, text inputs with floating labels and custom adornments, digital signature capture with undo, redo, and export capabilities, checkbox multi‑select and indeterminate states, seamless form integration, accessibility compliance, one‑time password (OTP) inputs, programmatic row adjustments, and slider tick customization and styling.
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).
Use this skill whenever the user wants to integrate Loops from application code, backend services, webhook handlers, or server-side automation. This includes the Loops HTTP API and official SDKs for server-side contact, contact-property, mailing-list, event, API-key-validation, and transactional-email workflows. Trigger on phrases like "Loops API", "Loops SDK", "send a Loops event from my app", "add a contact to Loops in a webhook", "send a transactional email from backend code", or any time the user wants to integrate Loops into their app, backend, webhook, or automation. Do not trigger for CLI or shell-only requests.
Plans real-user QA deliverables: personas, journey maps, exploratory charters, persona/journey/tour/CFR test cases, regression suites, Figma validation checks, automation intent, and user-impact bug reports. Writes artifacts under <qa-output-path>/qa/ for qa-execution to consume. Use when planning QA before execution, documenting journey-driven test strategy, marking flows that need E2E follow-up, or filing structured bug reports. Do not use for live execution, AI implementation audits, CI gate ownership, or technical integration/security/performance suites; use qa-execution or agent-output-audit instead.
Guideline for designing, implementing, and verifying secure Python applications following OWASP Top 10 best practices. Use when the user wants to: (1) review Python code for security vulnerabilities, (2) design a secure Python application architecture, (3) implement security features (authentication, authorization, cryptography, input validation), (4) audit Python dependencies for known vulnerabilities, (5) create security checklists or verification plans, (6) fix security bugs or harden existing Python code, (7) set up security testing and static analysis (bandit, safety, semgrep), or (8) handle any Python security concern including injection prevention, secure deserialization, SSRF protection, secrets management, and secure deployment.
Pre-Production validation — build a production-quality end-to-end build to confirm the full game loop is achievable before committing to Production. Run after GDDs, architecture, and UX specs are complete. Produces a PROCEED/PIVOT/KILL verdict that gates the Pre-Production → Production transition.
Adaptive multi-agent framework for automated data science tasks with planning, execution, and validation