710 件 ・ 関連度順
概要と使いどころ
Enforce universal standards for high-performance development. Use when profiling bottlenecks, reducing latency, fixing memory leaks, improving throughput, or optimizing algorithm complexity in any language.
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。
Audit structural debt, logic leakage, and monolithic components across Web, Mobile, and Backend codebases. Use when reviewing architecture, assessing tech debt, detecting logic in wrong layers, or identifying God classes.
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。
Identify dangerous API footguns, surprising default behaviors, and sharp edges in codebases and dependencies. Adapted from Trail of Bits. Use during code review to catch APIs that are easy to misuse, configurations that surprise, and abstractions that leak.
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。
Load testing with k6/Artillery, response time thresholds, memory leak detection, N+1 query detection, and CI integration.
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。
Managing secrets (API keys, database credentials, certificates) with Vault, cloud providers, and Kubernetes. Use when storing sensitive data, rotating credentials, syncing secrets to Kubernetes, implementing dynamic secrets, or scanning code for leaked secrets.
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。
Audit/regression checks for the evidence-first survey pipeline: citation health, per-section coverage, placeholder leakage, and template repetition.
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。
Correctly call native (C/C++) libraries from .NET using P/Invoke and LibraryImport. Covers function signatures, string marshalling, memory lifetime, SafeHandle, and cross-platform patterns. USE FOR: writing new P/Invoke or LibraryImport declarations, reviewing or debugging existing native interop code, wrapping a C or C++ library for use in .NET, diagnosing crashes, memory leaks, or corruption at the managed/native boundary. DO NOT USE FOR: COM interop, C++/CLI mixed-mode assemblies, or pure managed code with no native dependencies.
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 when a native app, service, or user-mode driver host allocation fails; distinguish VA exhaustion, fragmentation, and commit pressure. Not for managed .NET heap growth, proving a leak from one snapshot, or corruption.
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。
Diagnose where your growth is actually leaking (acquisition, activation, retention, or loops) and produce a growth plan with a named loop, an activation definition, a 90-day experiment portfolio and channel guardrails. Use it when someone says growth stalled, signups don't activate, retention is flat, how do we build a growth loop, should we go PLG, onboarding redesign, churn, referral program, or which channel should we bet on. Draws on 672 insights from 143 Lenny's Podcast guests, led by Elena Verna, Casey Winters and Lauryn Isford.
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。
Comprehensive performance specialist covering analysis, optimization, load testing, and framework-specific performance. Use when identifying bottlenecks, optimizing code, conducting load tests, analyzing Core Web Vitals, fixing memory leaks, or improving application performance across all layers (application, database, frontend). Includes React-specific optimization patterns.
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。
Audit applications for AI prompt injection, agent security, and LLM permission boundary vulnerabilities. Use when the user mentions 'prompt injection,' 'LLM security,' 'AI security,' 'jailbreak,' 'indirect prompt injection,' 'prompt leaking,' 'AI red team,' 'LLM vulnerabilities,' 'AI input validation,' 'system prompt extraction,' 'agent security,' 'MCP security,' 'AI permissions,' 'AI privilege escalation,' or needs to secure any application with AI features, AI agents, or LLM integrations.
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。
Local Gitleaks scans for staged changes, push ranges, and full history in private repos, with redacted reports. Use to block committed secrets without GitHub Secret Protection.
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。
Venue- and market-type-agnostic strategy, sizing, and backtesting layer for binary prediction markets (Kalshi, Polymarket, ForecastEx). Covers the durable edge thesis, fee-aware selection, fractional-Kelly sizing, and leak-free validation methodology.
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。
Build or adapt a local harness to drive, inspect, and profile an interactive CLI or TUI without external services. Use for CLI UX checks, startup regressions, memory leaks, hangs, prompt flows, or terminal demos.
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。
Build correct cached/dynamic boundaries in the Next.js App Router when Cache Components or PPR are enabled. Use when working with cacheComponents, Partial Prerendering (PPR), 'use cache', cacheLife, cacheTag, updateTag, or revalidateTag, to avoid request-context leaks and enforce proper cache invalidation. Not for the Pages Router or when Cache Components/PPR are off.
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 AI-generated writing patterns in prose. Use when reviewing docs for slop, vague language, or identity leaks before publishing.
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。
Profile and optimize application performance including load times, memory usage, and rendering. Use when debugging slow performance, memory leaks, or optimizing app speed.
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。
Analyze pipeline health, identify stalled deals, predict close probability, and suggest actions to move deals forward. Improves forecast accuracy and prevents revenue leakage. Use when deals get stuck or forecast accuracy is poor.
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 when checking your own manuscript before submission from a reviewer's perspective. Returns anticipated Major/Minor comments with fixes, including numerical, citation and leakage checks, with an optional multi-reviewer panel. Someone else's paper is /peer-review.
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 when preparing a medical-imaging dataset (DICOM/NIfTI) for modelling. Profiles spacing, orientation, intensity, label integrity, foreground fraction and target volume, gates them against the plan, then plans and audits preprocessing and augmentation for leakage.
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 when validating or evaluating a trained medical-imaging model. Audits split leakage and validation design, computes task-correct held-out metrics (Dice + HD95, AUROC + AUPRC, FROC, calibration), and covers uncertainty/OOD and Grad-CAM explainability, each with a gate.
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 when checking a radiology or medical AI study design before drafting or submission. Reviews the analysis unit, cohort logic, leakage risks, comparator, validation strategy and reporting-guideline fit. AI-vs-expert benchmarks are /design-ai-benchmarking.
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。
Exposed-secrets hunting - .git/ dir + history mining, exposed .env/config files, hardcoded keys in JS bundles + source maps, S3/blob exposure, public-repo secret search, CI/CD leakage. Live-validation mandatory. Wiki-first, FIND schema output.
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。
Post-engagement knowledge harvest AND harness retrospective - after a box/bugbounty/pentest/CTF is completed, first diff how the engagement was EXECUTED against the skills/hooks that governed it (what discipline was skipped) and improve the harness, then sweep the whole engagement for GENERIC reusable knowledge NOT already in wiki/ and land it via the leak-gated stage->promote pipeline. Use at close-out or when asked to "extract learnings", "what did we learn", "harvest lessons into wiki", "distill this engagement", "post-mortem into the wiki", "what did we do wrong", "improve the harness from this box".
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。