Results for “black”

Lint and format Python with Ruff. Use when a user asks to set up Python linting, replace flake8/black/isort, configure code quality rules, or speed up Python code formatting.

Executes a structured ransomware incident response from initial detection through containment, forensic analysis, decryption assessment, recovery, and post-incident hardening.

Validates observable behavior of apps, CLIs, APIs, and generated artifacts against a prewritten contract without inspecting source code.

Visualizes outlines and markdown as bold, color-block manifesto-style decks without rewriting or reordering content. Supports black, red, and yellow themes.

Enforces principal-engineer-level Python code quality: formatting, type safety, idiomatic patterns, class and function design, and anti-pattern prevention.

Analyze and replicate xAI's design language: near-black canvas, white pill outlines, Universal Sans display type, and Geist Mono technical labels.

Generates a white magazine-style deck with rainbow bar, gradient text, macaron cards, and black pill for dual-use on Xiaohongshu and horizontal PPT.

Creates a tech sharing deck for internal engineering talks with GitHub-dark theme and JetBrains Mono code blocks.

Analyzes and recreates Uber's design language: a black-and-white palette, custom geometric display and text fonts, pill-shaped interactive elements, and editorial 4:3 illustrations for transportation and delivery interfaces.

Analyzes Perfetto traces to find the root cause of latency, memory, or jank issues in Android apps.

Creates a dark-themed presentation deck with gradient backgrounds, floating blur orbs, SVG force-directed graphs, and JetBrains Mono code highlighting.

Turn "which leads should I work?" into a lead-by-lead work plan. Per-lead status (never-touched, engaged-not-replied, gone-cold, hot), days-dark per lead, last-touch quality, a re-engagement ranker, and a recommended next action for every lead. Built for SDRs and full-cycle reps, customizable to your CRM and your outreach tool. Trigger on "which leads should I work?", "who haven't I touched?", "show me cold leads", "hot leads to follow up on", "who's gone dark?", "re-engagement candidates", or any lead-level work-assignment question.

Analyzes and documents SpaceX's design language, including color palettes, typography, layout, and elevation, for use in design systems and brand guidelines.

BALD EAGLE v3.0 — XYZ Alpha Hunter (Hardened). Focused on 6 high-liquidity XYZ assets: CL, BRENTOIL, GOLD, SILVER, SP500, XYZ100. Conviction-scaled leverage (5-10x based on score). Wider DSL for macro assets. Maker-only execution. Scanner calls create_position internally. v3.0: focused assets, conviction-scaled leverage, XYZ-tuned DSL, no thesis exit.

Diagnose your own behavior by reading the structured logs you leave behind — turns.jsonl, events.jsonl, chat_history.jsonl, scheduler.yaml. Use when something has gone wrong (a message didn't land, a scheduled job isn't firing, a communication pattern feels off), when you need to understand a pattern over many turns, or when cost / token usage needs auditing. Covers jq query recipes and points at the debugging-jobs / debugging-communication / debugging-drift companion guides for specific failure modes.

Baseline the EFI System Partition and hunt malicious EFI binaries (ESPecter, BlackLotus, Bootkitty, Glupteba) by mounting the ESP, hashing and verifying boot loaders, scanning with YARA, and detecting anomalous non-EFI files.

Prediction market arbitrage & alpha scout. Searches Polymarket and Kalshi for the same event, runs cross-platform arb math (including fees), and ranks opportunities by profitability. Use when asked to "find arb", "scout markets", "find edge", or scan a specific topic across prediction markets.

Adds strategic color to monochromatic or dull interfaces using oklch for perceptually uniform palettes, generating harmonious color systems with automatic light/dark variants and accessible contrast ratios.

极简编辑风预设 — 基于 taste-anti-slop 的低 VARIANCE 配置。克制、留白、排版驱动。适用于作品集、文档站、编辑类产品。

Flip a problem, goal, or decision 180 degrees to find what forward thinking misses. Asks what would guarantee failure, then works backward to what must be avoided or changed. Load when the user says "invert this", "flip this problem", "what would guarantee failure", "think backward", "reverse engineer the goal", "think about it backwards", or when deep-thinking diagnoses an inversion frame. Also triggers on "what's the opposite of success here", "how would we sabotage this". Two methods: Failure Inversion and Opposite Goal. Max 2 clarifying questions before inverting. Always returns forward actions. For broader analysis, deep-thinking calls this.

Use to scan support conversations for a harm nobody has categorised yet — a new failure mode, a scam pattern, or a product change hurting a group of customers before it shows in any metric. Trigger for "is anything new going wrong", "scan for emerging issues", "early warning from support", horizon scanning, a new scam pattern, or preparing for a question about what you might be missing.

Runs a full retrospective and dev cycle analysis, chaining /recall and /new-features to reconstruct what went wrong, extract lessons learned, identify rework patterns, and synthesize feature ideas from the findings.

扫描全 wiki 发现健康问题，生成修复建议报告（覆盖全 9 种 entity + graph 一致性）

Monitors a watchlist of Polymarket markets and surfaces meaningful shifts in price, volume, comments, or resolution proximity, with position context and optional Bankr integration.

Classifies a patient's dizziness into one of four categories — vestibular/vertigo, presyncope, disequilibrium, or nonspecific — using history, timing, triggers, and associated symptoms. Use when a patient presents with dizziness, giddiness, lightheadedness, feeling off-balance, spinning, or nearly blacking out and you need to determine the type before further workup.

Use when designing a near-black premium UI with subtle ordered-dither texture and a thin accent-colored laser atmosphere. Prefer this over mengto-dither-background when dither pairs with laser accents; prefer over mengto-blue-laser-clean-glass-layout when ordered dither texture is the surface cue, not frosted glass dashboards.

Interpretabilidade e explicabilidade de modelos usando SHAP (SHapley Additive exPlanations). Use essa skill ao explicar predições de modelos de machine learning, computar importância de features, gerar plots SHAP (waterfall, beeswarm, bar, scatter, force, heatmap), depurar modelos, analisar vieses ou justiça de modelos, comparar modelos ou implementar IA explicável. Funciona com modelos baseados em árvores (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), modelos lineares e qualquer modelo black-box.

Use to plan and run peak-season support — what to freeze, pre-stage and measure daily so volume spikes do not become quality and attrition crises. Trigger for "peak season readiness", "holiday staffing plan", "Black Friday prep", freeze policy or KB changes before peak, pre-hire and ramp for surge, daily war room metrics, peak post-mortem, or when last year's peak broke SLA and attrition.

Detect and humanize AI-generated Chinese text. 20+ rule detection categories plus statistical features (sentence-length CV, short-sentence fraction, comma density, perplexity, GLTR, DivEye) plus scene-aware LR fusion (rule × 0.2 + LR × 0.8) trained on three scenes: general / academic / longform 长文本 (≥1500 字)。Unified CLI: ./humanize {detect,rewrite,academic,style,compare}. 8 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo/novel)。 Multi-paragraph rewriting (paragraph length CV、跨段 trigram 重复) plus best-of-N humanize (默认 N=10 取最低 LR)。165 replacement patterns + CiLin 同义词词林 38873 with collision blacklist。 Academic paper AIGC reduction for CNKI/VIP/Wanfang (知网/维普/万方 AIGC 检测降重)。 Pure Python, no dependencies, offline。v5.0.0 — HC3 fused 准确率 95%、学术 hero 100→35 (-65)、 工作汇报 96→13 (-83)、长篇博客 96→41 (-55)。 Use when user says: "去AI味", "降AIGC", "人性化文本", "humanize chinese", "AI检测", "AIGC降重", "去除AI痕迹", "文本改写", "论文降重", "知网检测", "维普检测", "AI写作检测", "让文字更自然", "detect AI text", "humanize text", "reduce AIGC sc