「spy」の検索結果

Generates Qt Quick Test cases (TestCase, SignalSpy, tryCompare) for QML components. Use for "write QML tests", "qml test", "qt quick test".

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The compile-readiness gate for prompt auto-optimization. Decide whether you have earned the right to run an optimizer (DSPy MIPROv2 / GEPA / BootstrapFewShot) before spending compute. Two preconditions only — a real metric, and enough examples for the optimizer you picked. Garbage metric in, garbage prompt out. Pick the optimizer by data scale; GEPA inverts the scale assumption (~10 examples + textual feedback).

日本語の概要は準備中です。原文の説明を表示しています。

Lifecycle SOP for **per-model prompt artifacts** — the compiled prompts, instructions, few-shot demos, edit-format pins, and embedding-bound indices that change behavior when the underlying LM, dataset, or framework version changes. Activate when adopting compiled prompts (DSPy, GEPA, BootstrapFewShot output), when supporting multiple LMs in production, when a provider deprecates a model snapshot, or when a framework deprecates a config surface (LlamaIndex `ServiceContext` → `Settings`, Aider edit-format defaults). Do NOT activate for one-off raw prompt edits or for truly model-agnostic system prompts that have been swap-tested. Search keywords: prompt portability, model swap, recompile prompt, model deprecation, prompt per model, prompt breaks on new model, version compiled prompts.

日本語の概要は準備中です。原文の説明を表示しています。

Split a multi-call LM workflow by cognitive load, not by accuracy: let one strong model make the few reasoning decisions and a cheap model do the many mechanical executions (Aider architect+editor, DSPy optimizer-LM vs task-LM, vLLM speculative draft+target, LangGraph supervisor+worker are the same shape). Use when designing or cost-optimizing a pipeline that calls an LM many times, when deciding which steps need a strong reasoner vs a cheap executor, or when adding an escalation valve for when the cheap tier degrades. Search keywords: reduce LLM cost, cheaper model, lower token cost, model cascade, route to cheap model, strong model plus cheap model, LLM cost optimization.

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Tool skill — the *first move* in any LM-debugging session: dump the actual rendered prompt the framework sent to the model, before changing anything else. Activate when an LM call produced an unexpected output (wrong answer, schema violation, refusal, truncation, cost spike, latency spike, infinite loop, "model got dumber after upgrade"). The skill enforces a 30-second inspect step BEFORE any prompt edit, model swap, retry, or temperature tweak. Cross-framework cheat sheet: DSPy `inspect_history`, LangGraph `get_state_history`, CrewAI `step_callback`, LangChain `set_debug`/`set_verbose`, Aider `/diff`+`--verbose`, raw OpenAI/Anthropic via `OPENAI_LOG=debug`/`ANTHROPIC_LOG=debug` or HTTPX event hooks. Do NOT activate for first-time prompt authoring, exploratory prompt design, or non-LM bugs.

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Universal discipline for any LM-driven loop — agent retries, plan-act-observe, multi-agent handoffs, optimiser passes, test-fix cycles. Encodes the one rule every framework documents quietly and every team relearns expensively: the LM in the loop is NEVER a reliable terminator. Termination must be provided by an explicit counter + exit predicate + stagnation signal + escalation path that live OUTSIDE the LM's control. This is a tool- level, framework-agnostic skill. It maps onto LangGraph (recursion_limit + state counter + interrupt), CrewAI (max_iter + max_rpm + human_input), Claude / OpenAI SDKs (max_iterations + tool_use_budget), DSPy (declared evaluation budget), Aider (REPL + explicit retry cap), and AutoGen (max_consecutive_auto_reply). Search keywords: infinite loop, recursion limit, recursion_limit, GraphRecursionError, max iterations, max_iter, agent stuck, agent won't stop, runaway agent, ReAct loop not terminating, agent repeating itself.

日本語の概要は準備中です。原文の説明を表示しています。

Unified Python framework for extracellular electrophysiology. Load 20+ formats (SpikeGLX, OpenEphys, NWB, Intan, Maxwell, Blackrock), preprocess, run 10+ sorters (Kilosort4, SpykingCircus2, Tridesclous, MountainSort5) via one API, compute quality metrics (SNR, ISI, firing rate), compare sorters, export NWB/Phy. For format-agnostic multi-sorter workflows. For Neuropixels-specific PSTH/decoding use neuropixels.

日本語の概要は準備中です。原文の説明を表示しています。

Best practices, design patterns, and common pitfalls for ROS1 (Robot Operating System 1) development. Use this skill when building ROS1 nodes, packages, launch files, or debugging ROS1 systems. Trigger whenever the user mentions ROS1, catkin, rospy, roscpp, roslaunch, roscore, rostopic, tf, actionlib, message types, services, or any ROS1-era robotics middleware. Also trigger for migrating ROS1 code to ROS2, maintaining legacy ROS1 systems, or building ROS1-ROS2 bridges. Covers catkin workspaces, nodelets, dynamic reconfigure, pluginlib, and the full ROS1 ecosystem.

日本語の概要は準備中です。原文の説明を表示しています。

Canonical rules for writing tests in the Shift codebase. Use whenever you add, rewrite, or review a `.test.ts` or `.spec.ts` file — or any time you're about to mock, stub, or spy your way around a testing problem. Covers TestEditor-based tests and desktop E2E tests driven through EditorDriver.

日本語の概要は準備中です。原文の説明を表示しています。

Boot-to-root methodology for a full machine (THM/HTB/PG/CTF box, "get user.txt+root.txt", "root the box", "foothold to root"). Enforces basic-tool recon (nmap, nc, ffuf, nuclei, dig) before anything custom, wiki-first lookups, and ALWAYS pspy + linpeas/winpeas for privesc. Use when handed a box/IP to own end-to-end.

日本語の概要は準備中です。原文の説明を表示しています。

Use for mock functions in Bun tests, spyOn, mock.module, implementations, and test doubles.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Geophysicist. Use when a task calls for Geophysicist judgment. Reasons from wavelength and skin-depth limits through earthquake catalogs (USGS ComCat, ObsPy/FDSN), Bouguer/IGRF gravity-magnetics, MT distortion and dimensionality, and SEG-Y reflection processing (NMO, migration) while treating statics, galvanic distortion, velocity ambiguity, and header mis-mapping as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert FPGA Engineer. Use when a task calls for FPGA Engineer judgment. Reasons from metastability budgets, setup/hold margins, and tool-reported WNS/TNS through Vivado/Quartus timing and CDC reports, XDC/SDC constraints, Spyglass/Verilator lint, and ILA/IBERT lab bring-up while treating unsafe clock-domain crossings, reset domain crossings, sim-versus-silicon X mismatches, and unconstrained timing paths as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.

日本語の概要は準備中です。原文の説明を表示しています。

Research competitor paid-ad presence, messaging, creative, formats, landing pages, keyword and auction signals, transparent ad libraries, and strategic gaps across supported platforms. Use for competitor ads, ad libraries, ad spy, competitive PPC analysis, competitor creative, Google Ads Transparency, Meta Ad Library, or paid-media competitor research.

日本語の概要は準備中です。原文の説明を表示しています。

Write effective unit tests with Jest, Vitest, or pytest. Covers the test-doubles taxonomy (stub/spy/mock/fake), Arrange-Act-Assert, coverage threshold configuration and CI gating, snapshot testing, fake timers, and mutation testing with Stryker/mutmut. Use when: "unit test," "Jest," "Vitest," "pytest," "mock," "coverage threshold," "test doubles," "mutation testing," "fake timers," "snapshot test." Not for: interpreting coverage reports or finding coverage gaps — use coverage-analysis; AI generating the test code for you — use ai-test-generation; auditing existing tests for smells — use ai-qa-review; browser/component rendering assertions — use cypress-automation or visual-testing. Related: coverage-analysis, ci-cd-integration, ai-test-generation, shift-left-testing.

日本語の概要は準備中です。原文の説明を表示しています。

Understand implementation details of .NET code by decompiling assemblies. Use when you want to see how a .NET API works internally, inspect NuGet package source, view framework implementation, or understand compiled .NET binaries.

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Portfolio Analyzer — sector allocation analysis vs SPY benchmark, geographic exposure, correlation matrix, concentration risk (HHI), beta-weighted portfolio delta, dividend/income analysis, and prioritized rebalancing recommendations. Triggered by "trade portfolio" or "analyze my portfolio".

日本語の概要は準備中です。原文の説明を表示しています。

Technical Analysis skill — price action, indicators (RSI/MACD/Stochastic), chart patterns, support/resistance, volume, Bollinger Bands, and relative strength vs SPY. Returns Technical Score (0-100). Triggered by "trade technical <TICKER>".

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Use for code generation, feature work, bug fixes, refactoring, and code review when a change creates or reshapes a multi-step flow, state lifecycle, side-effect or decision ownership, cross-unit composition, or boundary contract. Also use it when an existing structural problem makes those elements hard to trace or verify. Structural change is optional. Mechanical edits, throwaway experiments, and coherent local changes stay with their current owner and focused verification.

日本語の概要は準備中です。原文の説明を表示しています。

Vitest test runner - configuration, assertions, mocking (vi.fn, vi.mock, vi.spyOn), fake timers, snapshot testing, coverage, workspace projects

日本語の概要は準備中です。原文の説明を表示しています。

Clones a STATIC IMAGE ad into finished ads for your own product, into a reusable prompt TEMPLATE, or both — the template is what makes the ads and the ads are what prove the template. Round-trips the frame through gpt-image-2, nano-banana-pro or reve-2.1 until it reproduces, replaces every brand-specific with a {placeholder}, then fills it with your product pinned to a real photo. Use for "clone this ad for my product", "rebuild this competitor's ad for us", "make me ads like this", "reverse engineer this ad", "make this ad reusable", "add this to my prompt library", or "clone my competitors' ads" with NO file attached — it sources one via spy-competitor-ads rather than asking which. **Among cloners the source's medium decides**: a still, or nothing attached, lands here; "clone this ad" plus a VIDEO is clone-video-ad. NOT for generating an ad from a brief (chatgpt-image-ad, nano-banana-image-ad), nor animating a still as-is (image-to-motion). Refuses to render your product without a real photo of it.

日本語の概要は準備中です。原文の説明を表示しています。

Research competitor paid-ad presence, messaging, creative, formats, landing pages, keyword and auction signals, transparent ad libraries, and strategic gaps across supported platforms. Use for competitor ads, ad libraries, ad spy, competitive PPC analysis, competitor creative, Google Ads Transparency, Meta Ad Library, or paid-media competitor research.

日本語の概要は準備中です。原文の説明を表示しています。

Detects chart patterns in OHLC data from natural-language hypotheses or visual chart selections.

日本語の概要は準備中です。原文の説明を表示しています。