Browse reusable Agent Skills, each with a clear purpose and practical guidance.
spec を実装に渡す前に「本当に正しいか」を敵対的に検証したい時。特に correctness critical (データ整合性 / 並行 / 認証 / セキュリティ / 消失リスク) な spec、または「この spec で大丈夫? ちゃんとチェックして」と言われた時。根本原因が実コードと合っているか・修正案が十分か・ 抜けが無いかを、独立した複数観点で実コードに当てて確認する。 キーワード: spec review, spec 検証, 大丈夫か, ちゃんとチェック, red-team, 根本原因, 設計レビュー, データ消失, 裏取り, adversarial
Write unit and integration tests for Angular v21+ applications using Jest with @testing-library/angular, focusing on user-centric testing, AAA pattern, and modern Angular patterns (Standalone components, Signals). Use for testing components, services, and HTTP interactions with Jest globals and Testing Library DOM matchers.
**PROFILE TEST** - A skill for testing the profile command. USE FOR: testing profile analysis, verifying structural metrics. DO NOT USE FOR: production use.
Iteratively tune esp-ppq QuantizationSetting to recover post-quantization accuracy on ESP-DL targets. Drives a closed loop of "baseline -> calibration × TQT(default) cartesian product -> distribution-aware residual fixes -> agent-driven open exploration -> re-evaluate" in the current Python environment, using a minimal user contract (calib dataloader + evaluate function). Generic across architectures (ResNet / EfficientNet / ViT / DETR / YOLO / LSTM and any esp-ppq-supported graph) — the search procedure is distribution-driven and does not depend on a specific network family. Method ordering is accuracy-first with a soft penalty for passes that slow down on-device inference; once the prescribed Phase-1/2/3 sequence exhausts, the skill hands control to the agent (Phase 5) with a structured history of improving levers + the per-iteration error artifacts to read, so the agent can compose multi-knob iterations (lever stacking, calibration cross-pollination, ablation, cost-trim) without a rigid template. LSQ on POWER_OF_2 targets is auto-disabled (silent degenerate; use TQT instead) and esp32p4 layer-wise equalization is warn-only (esp-ppq officially "Not recommend" for per-channel weights but empirically can still help on some models). Use this skill whenever the user wants to improve a quantized esp-dl/.espdl model's accuracy, debug high quantization error, choose between calibration algorithms, decide on equalization / bias correction / weight split / mixed precision / TQT / blockwise reconstruction settings, or run an automated search over esp-ppq quantization options. Also triggers for "esp-ppq 量化调参", "降低量化误差", "Layerwise quantization error 分析", "QuantizationSettingFactory.espdl_setting 怎么调", "混合精度量化", "量化精度恢复", "espdl 量化优化".
A tiny test skill that runs a safe python script and echoes argv. Use to validate the skill script execution pipeline (allowed-tools gating, timeout, and output limits).
Version-pinned Telegram client connection behavior notes for proxy compatibility debugging.
Practical Zig/runtime pitfalls and invariants for this codebase after epoll rewrite.
Run, interpret, and iterate on the OpenInference GenAI conformance MVP at python/openinference-instrumentation/scripts/conformance/. Use when the user mentions GenAI conformance, OTel GenAI semantic conventions, Weaver registry live-check, the dual-write conversion (`_genai_conversion.py`, `enable_genai_semconv`), `gen_ai.*` attribute coverage, or asks to add new providers / scenarios to the conformance harness.
Review Java OpenInference instrumentation code for correctness and completeness. Use this skill when reviewing a Java instrumentor package — whether it's a new instrumentor, a PR that modifies one, or when the user asks to audit/review/check an existing instrumentor's code quality. Trigger on phrases like "review the instrumentor", "check the Java code", "audit the package", "is this instrumentor correct", or any request to validate an OpenInference Java instrumentation package against project standards.
Review Python OpenInference instrumentation code for correctness and completeness. Use this skill when reviewing a Python instrumentor package — whether it's a new instrumentor, a PR that modifies one, or when the user asks to audit/review/check an existing instrumentor's code quality. Trigger on phrases like "review the instrumentor", "check the code", "audit the package", "is this instrumentor correct", or any request to validate an OpenInference Python instrumentation package against project standards.