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Found 1,432 Skills
Scientific method expert for systematic bug investigation and root cause analysis. Use when users report bugs, crashes, unexpected behavior, or debugging requests. Applies hypothesis-driven investigation, controlled experiments, and rigorous validation across any programming language or platform.
Advanced debugging specialist for diagnosing and resolving code issues. Use when user encounters bugs, errors, unexpected behavior, or mentions debugging.
Expert guidance for Django REST Framework class-based views using Classy DRF (https://www.cdrf.co). Use when selecting or debugging APIView, GenericAPIView, concrete generic views, mixin combinations, or ViewSet/GenericViewSet/ModelViewSet behavior; tracing method resolution order (MRO); understanding which method to override (`create` vs `perform_create`, `update` vs `perform_update`, `destroy` vs `perform_destroy`, `get_queryset`, `get_serializer_class`); and comparing behavior across DRF versions. Do not use for function-based views, GraphQL, FastAPI/Flask, frontend work, or non-DRF backend frameworks.
Systematic debugging with root cause investigation. Four phases: investigate, analyze, hypothesize, implement. Iron Law: no fixes without root cause. Use when asked to "debug this", "fix this bug", "why is this broken", "investigate this error", or "root cause analysis". Proactively suggest when the user reports errors, unexpected behavior, or is troubleshooting why something stopped working.
Netra MCP trace-debugging workflow focused on query_traces and get_trace_by_id, including exact input parameters, filter schema, operators, sorting, and pagination patterns.
Saleor backend internals and behavior reference. Covers discount precedence, order-level vs line-level discount stacking, manual/voucher/promotion interactions, and denormalized field semantics. Use when working with Saleor discounts, building Dashboard discount UI, or debugging discount application order.
Hypothesis → Prediction → Test → Revise with explicit falsification. Use for debugging, feature experimentation, performance investigation, and A/B testing design.
Create and debug Cloudinary transformation URLs from natural language instructions. Use when building Cloudinary delivery URLs, applying image/video transformations, optimizing media, or debugging transformation syntax errors.
Systematic evidence-based debugging using runtime logs. Generates hypotheses, instruments code with NDJSON logs, guides reproduction, analyzes log evidence, and iterates until root cause is proven with cited log lines. Use when the user reports a bug, unexpected behavior, or asks to debug an issue.
LLDB debugger skill for C/C++/Swift/Objective-C programs. Use when debugging with LLDB on macOS, FreeBSD, or Linux-clang environments, mapping GDB mental models to LLDB commands, using LLDB in Xcode or VS Code, or debugging Swift/Objective-C. Activates on queries about LLDB commands, GDB to LLDB migration, Apple platform debugging, LLDB Python scripting, or IDE-integrated debugging with clang-built binaries.
Use when building, fixing, or improving ANY SwiftUI UI — views, navigation, layout, animations, performance, architecture, gestures, debugging, iOS 26 features.
Embedded CAN/CAN-FD debugging tool for interface scanning, message monitoring, test frame transmission, log recording, database file decoding, and bus statistics. Automatically triggered when users mention CAN, CAN-FD, DBC decoding, bus packet capture, USB-CAN joint debugging, message transmission, bus statistics, PCAN, Vector, slcan, CAN interface scanning, CAN ID filtering, ASC logs, BLF files. Also compatible with explicit invocation via /can. Even if users only say "check CAN messages", "send a test frame" or "decode DBC", this skill should be triggered as long as the context involves CAN bus communication.