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Turn code from the current session into a focused blog walkthrough with a generalized code sample and usage example. Learn the author's style from writing samples, adapt to the destination, and save only after approval.
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Examine Windows and Active Directory logs for attempts to extract passwords and other credentials. Identify suspicious memory access and database exports, correlate events with user and host context, and produce findings with severity ratings and detection queries.
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Validate Illumina BCL run folders and sample sheets, plan demultiplexing—the separation of reads by sample—and convert data to FASTQ using installed tools. Review conversion metrics, index settings, and reads that could not be assigned.
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Retrieve concise information about microbiome studies, samples, and habitat metadata from MGnify. Narrow requests by study or sample identifier or biome, a type of biological environment, and receive readable summaries or JSON for further processing.
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Troubleshoot Golang programs systematically - find and fix the root cause. Use when encountering bugs, crashes, deadlocks, races, or unexpected behavior in Go code. Covers debugging methodology, common Go pitfalls, test-driven debugging, pprof setup and capture, Delve, race detection, GODEBUG tracing, and production debugging. Start here for any 'something is wrong' situation. Not for interpreting profiles or benchmarking (→ See `samber/cc-skills-golang@golang-benchmark` skill), applying optimization patterns (→ See `samber/cc-skills-golang@golang-performance` skill), or designing new code (→ See `samber/cc-skills-golang@golang-safety` skill for defensive coding, `samber/cc-skills-golang@golang-concurrency` skill for concurrency design).
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Structured error handling in Golang with samber/oops — error builders, stack traces, error codes, error context, error wrapping, error attributes, user-facing vs developer messages, panic recovery, and logger integration. Apply when using or adopting samber/oops, or when the codebase already imports github.com/samber/oops.
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Train and run models on Tinker (Thinking Machines) — LoRA and full fine-tuning, SFT with correct loss masking, RL/RFT with group-relative advantages and importance sampling, agentic RL over multi-turn tool-using agents, inference via the SamplingClient or the OpenAI-compatible endpoint, checkpoint management and export, every hyperparameter, and cost estimation from the live price table. Use this skill whenever the user mentions Tinker, tinker://, TINKER_API_KEY, console.tinker.ai, or the tinker CLI; wants to fine-tune, SFT, RFT, or RL a model on Tinker; asks about Tinker models, context windows, LoRA rank, sampling params, loss functions (cross_entropy, importance_sampling, ppo, cispo, dro), Datum construction, forward_backward or optim_step; wants to sample from, download, resume, merge, or export a Tinker checkpoint; or asks what a Tinker run will cost. Whenever Tinker work is initialized in a project, this skill also creates a TINKER_PRICING.md there so prices sit next to the training code. Reach for it even on vague asks like "train a model on this data" or "get inference working" when Tinker is the platform in play.
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Use when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample similarity assessment. Trigger keywords: hierarchical clustering, dendrogram, sample QC, batch inspection, sample similarity. NOT for: differential expression testing, gene clustering heatmaps, single-cell clustering workflows.
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Tracks ctDNA across serial liquid-biopsy timepoints for molecular residual disease (MRD) and treatment-response monitoring, treating MRD as a binary integrated detection call across the patient's full variant set (with a defined LoD95 and per-sample specificity) rather than a per-timepoint VAF threshold, and handling undetectable samples as left-censored at the per-sample limit of detection rather than true zeros. Covers tumor-informed bespoke vs tumor-naive design, landmark vs surveillance sampling, molecular-response definitions and their non-standardization, censoring-aware clearance kinetics, and the multiple-testing structure of repeated surveillance. Use when monitoring ctDNA during therapy, calling molecular relapse before imaging, or estimating clearance half-life from serial samples.
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Sets up BMad Samples module in a project. Use when the user requests to 'install samples module', 'configure BMad Samples', or 'setup BMad Samples'.
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Find Amazon same-niche competitors by ASIN, with multi-dimensional filtering by click conversion rate, composite conversion rate, click volume, sales volume, reviews, ratings, price, and gross margin to identify potential competitors. Triggered when users mention same-niche competitors, ASIN competitor mining, niche competitor analysis, similar product benchmarking, ASIN benchmarking, niche market competitor list, high-conversion competitor screening, Nexscope product mining, niche competitor by ASIN, ASIN competitor analysis, same niche products, similar products discovery, conversion rate comparison, potential competitor screening, Nexscope ASIN mining. Even if users do not explicitly mention "niche" or "same niche", this skill should be triggered whenever the task involves discovering competitor lists or screening potential competitors based on a specific ASIN within the same market segment.
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Conduct structured insect behavior observations using sampling protocols, ethogram categories, event recording, interaction logging, environmental context, and summary analysis. Covers focal animal sampling, scan sampling, all-occurrences sampling, and instantaneous sampling methods. Defines a standard insect ethogram with locomotion, feeding, grooming, mating, defense, communication, and rest categories. Includes timestamped event recording, intraspecific and interspecific interaction logging, environmental covariate documentation, and time budget analysis. Use when studying insect behavior for ecological research, documenting behavioral repertoires for a species, observing pollinator activity or predator-prey dynamics, or supporting conservation assessments with behavioral data.
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Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
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Start and manage a Shopify store, from exploring business names and sample catalogs to adding products, updating inventory, checking orders, creating discounts, and reviewing sales. Store connection also supports people creating their first Shopify account and store.
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Build quantum circuits with Qiskit, test them locally, and prepare them for IBM quantum hardware. The skill covers hardware-specific circuit conversion, sampling, expectation values, and analysis of measurement results, uncertainty, and resource usage.
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Investigate Windows credential theft by correlating endpoint monitoring data, Sysmon events, and security logs. The skill provides detection queries and a reporting format to identify suspicious access and assess which credentials may be compromised.
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Identify and unpack malware compressed with UPX to prepare executable files for static analysis, which examines code without running it. The workflow covers altered header repair, debugger-based recovery, and validation before analysis in Ghidra or IDA.
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Build AWS applications with managed services that reduce server administration. Follow patterns for Lambda functions, HTTP APIs, database access, and queued background processing, with guidance on deployment and delays when functions first start.
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Prepare SOX 404 testing materials for internal controls over financial reporting. Organize controls, select samples based on frequency and risk, and create workpaper templates with test procedures, evidence requirements, and a framework for assessing deficiencies.
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Prepare SOX 404 internal control testing, including sample selection, evidence requirements, and audit workpapers. Organize findings about controls over financial reporting, assess deficiencies, and outline remediation plans for review by qualified financial professionals.
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Create realistic sample data for testing, development, and demos. Specify fields, record counts, value ranges, and business rules, then receive CSV, JSON, SQL INSERT statements, or a Python generator script with validation and usage instructions.
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Estimate a product’s total, serviceable, and realistically obtainable markets using industry data and customer-level calculations. Produce market estimates, growth projections, source references, and explicit assumptions to support investment pitches or market entry decisions.
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Build Handsontable demo pages for visual regression tests, which compare screenshots for appearance changes. It guides feature routes, URL-driven display states, sample data, and decisions about testing the same feature across framework wrappers.
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Validate CARLA pedestrian navigation by sampling walkable locations and reporting their coverage. Draw the samples as debug points to inspect the walkable area after a map import or investigate pedestrians that will not move.
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