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Review SAML single sign-on, where services exchange signed identity information, for signature and identity handling flaws. The skill guides testing and validation to determine whether altered assertions can change accounts, roles, or access decisions.
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Fine-tune vision models on custom images using Hugging Face Jobs cloud GPUs. Covers object detection, image classification, and segmentation—identifying image regions—with dataset validation, hardware selection, cost estimates, monitoring, and saving trained models to the Hub.
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Guides privacy audit sampling methodology including statistical and non-statistical sampling, sample size determination, stratification techniques, attribute sampling for compliance testing, confidence level selection, tolerable deviation rates, and extrapolation of results to the population. Keywords: audit sampling, statistical sampling, attribute testing, sample size, confidence level, stratified sampling, privacy audit.
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Configure SAML identity federation, a way to share authentication between systems, linking on-premises Active Directory with Microsoft Entra ID. Cover single sign-on for cloud applications, user attribute mapping, authentication model choices, and signing certificate management.
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Assess authorized single sign-on systems using SAML, OIDC, and OAuth2. Map the login flow, check signatures, redirects, and token validation, then document reproducible findings and their impact, including identity or tenant confusion.
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Investigate authentication gaps across single sign-on, APIs, and legacy login endpoints. The skill helps map entry points, compare their protections, test token trust between portals, and document whether a flaw grants access to protected accounts or functions.
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Provides AWS SAM bootstrap patterns: generates `template.yaml` and `samconfig.toml` for new projects via `sam init`, creates SAM templates for existing Lambda/CloudFormation code migration, validates build/package/deploy workflows, and configures local testing with `sam local invoke`. Use when the user asks about SAM projects, `sam init`, `sam deploy`, serverless deployments, or needs to bootstrap/migrate Lambda functions with SAM templates.
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使用SAM通过点或矩形框指定图像中的对象，生成用于抠图的区域掩码。指导自动生成、边界修正和质量筛选，也涵盖训练数据准备及模型部署步骤。
Plan study sample sizes, estimate statistical power—the chance of detecting an assumed effect—and find the smallest detectable effect. Supports standard tests and simulations for complex designs, with sensitivity analyses and sample size justifications.
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Use this skill when an external audit, attestation examination or assessment is coming and the team wants populations and evidence ready for the samples the auditor will pull, or asks "prepare for audit sampling", "what samples will the auditor ask for", "get our populations ready", "the auditor wants a list of all changes in the period", "evidence for the leavers sample", "Type II sample prep" or "how do we prove completeness of the population". Lists the populations an auditor is likely to sample, builds each from the system of record with the query that produced it, checks completeness and the evidence behind a dry-run sample, and writes a readiness file per population plus a list of gaps to fix before fieldwork.
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Build market research evidence by comparing two market-size estimates, calculating survey sample sizes for each customer group, and scoring potential segments. Outputs show methods and assumptions so teams can assess the basis for business decisions.
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Converts and transcodes audio file formats and encoding parameters using Volcengine LAS. Audio format conversion between wav, mp3, flac, m4a, ogg, aac and other audio formats. Adjusts sample rate (resample, downsample, upsample), bit rate (bitrate), channels (mono, stereo, channel mixing), audio compression, and audio quality settings via ffmpeg parameters. Supports TOS cloud storage paths and local file upload. Use this skill when the user wants to convert audio format (wav/mp3/flac/m4a/ogg/aac), transcode or re-encode audio files, adjust audio sample rate, bitrate, channels, compress audio files, resample or downsample audio, prepare audio for downstream tasks, or do any audio preprocessing.
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Verdichtet gebündelte offene Entscheidungen zu EINEM zusammenhängenden, prosa-basierten Gesamtkonzept auf Basis aller empfohlenen Varianten. Zeigt, wie die Teilsysteme nach der Umsetzung konkret ineinandergreifen, statt den Nutzer durch Dutzende isolierte Einzelfragen zu zwingen. Der Nutzer nimmt das Bild als Ganzes ab oder gibt zielgerichtete Korrekturen, die automatisch in Einzelentscheidungen übersetzt und über decision-briefing Phase 4 ins Register zurückgeschrieben werden. Nutze diesen Skill bei "wie sieht das Ganze aus wenn wir allem zustimmen", "zeichne mir das Gesamtbild", "Gesamtkonzept der Entscheidungen", "decision draw", /decision-draw, "wie greifen die Systeme zusammen". NICHT nutzen, wenn Alternativen pro Einzelpunkt diskutiert werden sollen -- das macht decision-briefing; NICHT für den Abhängigkeitsgraphen -- das macht decision-path; NICHT für strukturierte Einzelblöcke mit Optionen und Pro/Contra (auch bei eng zusammengehörigen Gruppen) -- das macht decision-shot; NICHT zur Herleitung einer einzelnen Entscheidung -- das macht decide.
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Store genomic variant records in TileDB-VCF, add samples over time, and retrieve selected samples or genomic regions. Supports batch reading, allele statistics, quality checks, and VCF/BCF export, with workflows for local storage and TileDB Cloud.
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Use SAM to identify object regions in images from points, boxes, or existing masks. The skill provides implementation examples for transparent cutouts, automatic mask generation, annotation tools, and model selection based on available resources.
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Estimate the size of a market or business opportunity using traceable sources, explicit assumptions, and inspectable calculations. Compare scenarios, show how changing assumptions affects the estimate, and identify the evidence needed to improve confidence.
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Assess externally exposed enterprise SSL VPN gateways by identifying products, checking known vulnerabilities, and reviewing authentication backends and SAML settings. The skill provides vendor-specific checks and a staged workflow that starts with less disruptive identification before deeper testing.
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Plan quantitative research by comparing experiments and surveys against research goals and resource limits. Get design recommendations, sample size calculations, participant recruitment strategies, and experimental materials, with an assessment of factors that could distort results.
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Functional programming helpers for Golang using samber/lo — 500+ type-safe generic functions for slices, maps, channels, strings, math, tuples, and concurrency (Map, Filter, Reduce, GroupBy, Chunk, Flatten, Find, Uniq, etc.). Core immutable package (lo), concurrent variants (lo/parallel aka lop), in-place mutations (lo/mutable aka lom), lazy iterators (lo/it aka loi for Go 1.23+), and experimental SIMD (lo/exp/simd). Apply when using or adopting samber/lo, when the codebase imports github.com/samber/lo, or when implementing functional-style data transformations in Go. Not for streaming pipelines (→ See `samber/cc-skills-golang@golang-samber-ro` skill).
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Monadic types for Golang using samber/mo — Option, Result, Either, Future, IO, Task, and State types for type-safe nullable values, error handling, and functional composition with pipeline sub-packages. Apply when using or adopting samber/mo, when the codebase imports `github.com/samber/mo`, or when considering functional programming patterns as a safety design for Golang. Not for nil-safety and zero-value design without this library (→ See `samber/cc-skills-golang@golang-safety` skill), nor for native error wrapping with fmt.Errorf, errors.Is and errors.As (→ See `samber/cc-skills-golang@golang-error-handling` skill).
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Reactive streams and event-driven programming in Golang using samber/ro — ReactiveX implementation with 150+ type-safe operators, cold/hot observables, 5 subject types (Publish, Behavior, Replay, Async, Unicast), declarative pipelines via Pipe, 40+ plugins (HTTP, cron, fsnotify, JSON, logging), automatic backpressure, error propagation, and Go context integration. Apply when using or adopting samber/ro, when the codebase imports github.com/samber/ro, or when building asynchronous event-driven pipelines, real-time data processing, streams, or reactive architectures in Go. Not for finite slice transforms (→ See `samber/cc-skills-golang@golang-samber-lo` skill).
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Comprehensive guide for dependency injection (DI) in Golang. Covers why DI matters (testability, loose coupling, separation of concerns, lifecycle management), manual constructor injection, and DI library comparison (google/wire, uber-go/dig, uber-go/fx, samber/do). Use this skill when designing service architecture, setting up dependency injection, refactoring tightly coupled code, managing singletons or service factories, or when the user asks about inversion of control, service containers, or wiring dependencies in Go. For a specific DI library, → See `samber/cc-skills-golang@golang-google-wire`, `samber/cc-skills-golang@golang-uber-dig`, `samber/cc-skills-golang@golang-uber-fx`, or `samber/cc-skills-golang@golang-samber-do` skills.
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Idiomatic Golang error handling — creation, wrapping with %w, errors.Is/As, errors.Join, custom error types, sentinel errors, panic/recover, the single handling rule, structured logging with slog, HTTP request logging middleware, and samber/oops for production errors. Built to make logs usable at scale with log aggregation 3rd-party tools. Apply when creating, wrapping, inspecting, or logging errors in Go code. For samber/oops specifics → See `samber/cc-skills-golang@golang-samber-oops` skill; for slog handler ecosystem → See `samber/cc-skills-golang@golang-samber-slog` skill.
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Building or editing Simulink (.slx) models that stream signals in frames using DSP System Toolbox — audio, vibration, radar, comms. Use when the prompt names a Simulink block (Discrete FIR Filter, Buffer, Unbuffer, Rate Transition, Downsample, Upsample, Sample-Rate Converter, FIR Decimation, Time Scope, Spectrum Analyzer, From Multimedia File) or an action (frame-based processing, InputProcessing, buffering, overlap, windowing, STFT, short-time Fourier, decimate, downsample, upsample, resample, multirate, anti-aliasing, tunable filter). Prevents silent numerical errors from sample-vs-frame mismatch, wrong block choice, or missing anti-aliasing.
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