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Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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 and simulate Data Poisoning attacks aimed at degrading or skewing an AI model's accuracy. This skill focuses on Adversarial Machine Learning concepts where attackers inject malicious or mislabelled data points into training or fine-tuning datasets (e.g., feedback loops) to bias the AI.

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Design annotation pipelines that produce reliable training labels — guidelines, agreement measurement, and quality control.

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。

Label smarter, not more — pick the most informative examples for annotation to maximize model gains per label.

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。

Product marketing and go-to-market strategy for B2B SaaS launches and positioning. Use this skill when planning a product launch, developing positioning and messaging, analyzing competitive landscape, building customer advocacy programs, designing a customer referral program (who may accept the reward, how the introduction is made), designing a win/loss interview program, working out why we lose deals, designing GTM strategy, creating category positioning, planning analyst briefings, or deciding which country to expand into and how far to localize. Also use it for an early-stage startup where the founder is still doing the selling — running founder-led sales, setting up a design-partner program, judging whether you have product-market fit, deciding what not to do yet, deciding when to hire the first AE or first marketer, and moving upmarket to larger buyers. Also use it when your company has just acquired another (or is about to), to plan the marketing integration: what you may and may not do between signing and close, the first letter to the acquired customers, whether to keep, endorse, fold in or retire the acquired brand, merging the two marketing databases without mailing people who opted out, when to start cross-selling, and announcing a product sunset. Also use it to design a demand-generation campaign and the channel plan it runs on: one campaign with one job, the offer for buyers who are and are not in market, what each channel is for, the kill and scale rules set before launch, the follow-up sales agrees to, the yearly calendar of always-on programs and tentpole campaigns, and which channels to keep, test or stop. Also use it for brand marketing and demand creation — how much to spend on brand versus performance, what to measure when a brand campaign produces no conversion event, and how to answer "we cannot attribute it". Also triggers on: founder-led sales, founder-led GTM, design partners, design partner agreement, first customers, first 10 customers, do we have product-market fit, PMF, Sean Ellis test, very disappointed survey, pre-seed, seed stage, when to hire our first AE, first sales hire, first marketing hire, moving upmarket, going upmarket, enterprise readiness, first enterprise deal, what do enterprise buyers need before they sign, positioning, product launch, beta program, early access program, design partners, private preview, public preview, general availability, GA launch, when can we call it GA, can sales sell a beta feature, selling the roadmap, launch tier, competitive intelligence, messaging, customer advocacy, win/loss analysis, win/loss interviews, why we lose deals, closed-lost analysis, loss reason, close-reason field, competitive displacement, displace an incumbent, rip and replace, competitive takeout, switch from a competitor, win deals from a competitor, incumbent contract, renewal window, auto-renewal notice period, switching costs, EU Data Act, cloud switching rights, why do we lose to no decision, GTM, category design, ideal customer profile, ICP, what is our real ICP, who actually buys from us, refresh our ICP from closed-won deals, ICP refresh before planning, analyst briefing, G2 review, international expansion, market entry, expand into Europe, which country should we launch in next, localization, translate our site, in-country go-to-market, regional GTM, is cold email legal in Germany, brand marketing, brand awareness, brand vs performance, brand versus demand gen, demand creation, demand capture, 95-5 rule, out of market buyers, category entry points, distinctive brand assets, mental availability, brand tracking, brand health, brand lift, share of search, how do we measure brand, justify brand spend, brand budget. Also triggers on public-sector marketing: sell to government, government marketing, public sector go-to-market, federal marketing, state and local government, SLED, FedRAMP, FedRAMP marketing claims, can we say FedRAMP ready, GovRAMP, StateRAMP, VPAT, accessibility confor

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代码提交与 MR 创建技能 - 自动生成语义化 commit message、创建符合规范的 GitLab Merge Request、验证飞书工作项关联。当用户提到 Git 提交、commit、push、推送代码、创建 MR、创建 PR、合并请求、或需要提交代码、推送代码、创建 MR/PR 时，必须使用此技能。支持交互式（对话）和非交互式（参数）两种模式。

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The measurement surface for ALL Pulp DSP and audio-pipeline work — read it BEFORE writing or gating DSP, not only when something already sounds wrong. Covers the C++ harness (signal generators, metrics, assertions, RenderScenario, contracts), the offline Audio Doctor (magnitude/frequency response, THD/THD+N, phase/group delay), and their Python sibling the Audio Quality Lab (tools/audio/quality-lab — null residual + alignment, LTAS log-spectral distance, spectral flux/centroid, HNR, Theil-Sen drift slope, Kaiser-sinc resampling, license-guarded corpus, regression-net ratchet). TRIGGER on AUTHORING work — "build/design an oscillator/filter/synth/effect", "add a DSP module", "what should the acceptance gate be", "how do I measure aliasing / anti-aliasing / alias floor", "null against a reference", "is this DSP correct", "choose a tolerance", "golden/regression corpus for audio", "measure drift or jitter", "A/B two renders" — AND on DEBUGGING work — "is there sound / no audio / I hear nothing", "does this filter/compressor/synth/delay produce the right signal", "prove the DSP / prove the contract", "measure the frequency response", "what's the THD / is it distorting", "what's the group delay / phase response / measured latency", "magnitude response curve", "render a test tone and assert", "audio regression", "64-frame works but 128 is silent", "sample-rate change pitch-shifted it", "describe what's in this buffer", "audio doctor", "compare before/after a DSP refactor". Reach for this BEFORE hand-rolling any FFT, null test, alias measurement, pitch tracker, or golden-render script — most of it already exists in one of the two lanes. Test/tool layer over HeadlessHost — deterministic, no audio device, no speakers. Off the realtime thread entirely.

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Diagnose whether an LLM classifier's validation-gate failure is GOLD-BOUND before spending on prompt revision or model changes. Use when: (1) a scoring pipeline over-predicts a label (precision low, recall high) and a prompt clarification is proposed to tighten it, (2) a pilot/validation gate fails and the fix candidates are prompt edits, (3) inter-rater agreement on the weak label was already low (κ < ~0.6). Core check: if gold POSITIVES share the exact feature the revision would exclude, no prompt can pass a gold-scored gate — recall craters while precision barely moves. Also documents the verified surgical-pilot design (single-section diff, tune/holdout split, pre-registered gate, perturbation check on untouched sections).

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Use when adding or reviewing Progressive Web App capabilities such as installability, manifest metadata, Service Worker registration, Workbox caching, offline fallback, app update prompts, maskable icons, or iOS PWA compatibility. Do not use for general API caching or non-installable performance tuning; Chinese triggers include PWA, offline, Service Worker.

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Generates guidance for reliability, resilience, availability, redundancy, fault-tolerance, and disaster recovery (DR) for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework. Use when the user asks to evaluate, design, or improve the reliability, resilience, availability, or disaster recovery capabilities of Google Cloud workloads.

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 task needs the judgment of a Dental Laboratory Technician — calculating wax pattern dimensions from a die measurement and alloy-specific casting shrinkage, rejecting a restoration built on a flawed impression or scan, verifying occlusal contacts against the articulator-mounted bite rather than sculpting by eye, or prioritizing value over hue when finalizing a shade match.

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Use when a task needs the judgment of a construction laborer — determining which OSHA-mandated protective system (sloping, benching, shoring, or shielding) a trench needs given its soil classification and depth, working as spotter/signal person on a crane or heavy-equipment lift, staging material and equipment on an active multi-trade site, or reading which hazard zone (excavation, crane swing, electrical rough-in) is live as work moves across the site.

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Use when a task needs the judgment of a cement mason / concrete finisher — timing float and trowel work against bleed water and set, laying out and cutting control joints on a slab, choosing air-entrainment and mix parameters for freeze-thaw exposure, adjusting a finishing schedule for hot- or cold-weather concreting, or diagnosing a slab defect (blistering, scaling, random cracking) after the fact.

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Use when a task needs the judgment of an allergist/immunologist — interpreting a skin-prick or specific-IgE result against pretest probability, working up a "penicillin allergy" label for delabeling before a needed antibiotic, managing anaphylaxis and biphasic-reaction risk, stepping asthma therapy up or down, or evaluating suspected primary immunodeficiency.

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Build comprehensive chromatin accessibility maps by aggregating ATAC-seq and DNase-seq narrowPeak data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "where is chromatin accessible in my tissue?" by combining peak calls into a union peak set. Handles cross-lab variation, ATAC vs DNase platform differences, and ENCODE blocklist filtering.

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Build comprehensive DNA methylation maps by aggregating WGBS (Whole Genome Bisulfite Sequencing) data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "where is DNA methylated/unmethylated in my tissue?" by combining per-CpG methylation data into tissue-level methylation profiles. Handles coverage filtering, identifies hypomethylated regions (HMRs) and partially methylated domains (PMDs), and manages cross-lab variation.

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Build comprehensive histone mark maps by aggregating narrowPeak data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "where is this histone mark present in my tissue?" by combining peak calls from multiple studies into a union peak set with confidence annotations. Handles cross-lab batch effects, broad vs narrow marks, and ENCODE blocklist filtering.

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Build comprehensive chromatin contact maps by aggregating Hi-C loop calls (BEDPE) across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "what regions are in 3D contact in my tissue?" by creating a union catalog of chromatin loops. Handles resolution-aware anchor matching, cross-lab variation, and Hi-C-specific quality metrics.

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。