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Use when rigging a character, creature or prop in Maya: "rig this character", placing or orienting joints, Orient Joint doing nothing, IK/FK switch and matching, pole vector flips or elbow drift, space switching, foot roll, spline IK spine, offsetParentMatrix, blendMatrix or matrix rigging, controls and lockdown for animators, Quick Rig, HumanIK, AdvancedSkeleton or mGear, a game skeleton for Unreal or Unity, or a slow rig (parallel evaluation, profiling, GPU Override not kicking in).

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Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualiz

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Seed `outline/claim_evidence_matrix.md` from outline+notes; claim-matrix-rewriter rewrites it later from evidence packs.

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Provides a structured risk scoring methodology for Data Protection Impact Assessments aligned with ENISA threat taxonomy and ISO 29134. Covers likelihood and severity assessment, risk matrix construction, inherent vs residual risk calculation, and risk appetite thresholds per EDPB WP248rev.01 guidance. Keywords: risk scoring, DPIA risk matrix, likelihood, severity, ENISA, ISO 29134, residual risk, risk appetite.

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Synthesize multiple per-competitor research files (produced by /competitor-research) into a single canonical threat matrix. Writes to marketing/competitors/aggregate.md as the locked canonical version every downstream skill reads. Triggers - "competitor aggregate", "synthesize competitors", "competitor matrix", "threat matrix", "lock competitor canonical"

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Agent skill for matrix-optimizer - invoke with $agent-matrix-optimizer

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Advanced matrix theory expert covering spectral theory, matrix factorizations, and numerical linear algebra

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Use this skill when building or maintaining a Requirements Traceability Matrix (RTM) on a Salesforce project: one row per requirement, columns for source, user-story id(s), test-case id(s), defect id(s), sprint, release, and status. Covers forward traceability (req → story → code → test) and backward traceability (test → req). Trigger keywords: RTM, requirements traceability matrix, audit trail for salesforce delivery, traceability for steerco, deferred requirement tracking, regulatory traceability. NOT for eliciting the requirements in the first place — use admin/requirements-gathering-for-sf. NOT for user-story authoring — use admin/user-story-writing-for-salesforce. NOT for UAT test design — use admin/uat-test-case-design. NOT for Apex test design — use apex/test-class-standards. NOT for backlog prioritization — use admin/moscow-prioritization-for-sf-backlog. More triggers: traceability.md, build-layer traceability, REQ-XXX and FG-XXX id mapping, artefact-to-requirement orphan report, coverage-gap report, check_rtm.py --manifest-dir, owning run-time agent column, design decision ref column, metadata API name in a traceability row.

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Plan, run, and measure an adversary engagement operation using the MITRE Engage framework so that deployed deception is driven by strategy instead of deployed ad hoc. Covers the Engage Matrix (Prepare, Expose, Affect, Elicit, Understand), the 10-Step Operational Process, mapping engagement Activities to the ATT&CK techniques they expose, and defining measurable Goals and Operational Objectives. Use when a team has honeypots, honeytokens, or canary tokens but no coordinating strategy, when leadership asks "should we engage attackers and how", when building a deception/denial program, when writing an adversary engagement operation plan, or when deciding which deception Activities to deploy against a specific threat actor. Keywords: MITRE Engage, adversary engagement, cyber deception strategy, denial and deception, Engage Matrix, EAC, EGO, Expose Affect Elicit, deception program, honeypot strategy, engagement operation.

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Builds a DRAFT weighted supplier evaluation matrix from supplier responses and the panel's agreed criteria, weights and scale: a provisional score per cell with the quoted evidence and anchor behind it, a register of every missing or partial answer, and calibration and sensitivity flags. Provisional for panel moderation; never recommends, awards or disqualifies. Use when the user asks to score, weight or rank supplier, bid or tender responses against agreed criteria, for example "score these bids", "build the evaluation matrix", "weight the tender responses" or "rank the suppliers against our criteria". Do not use for a side-by-side comparison without scores, use rfp-comparison-pack instead; for writing the criteria, use rfp-requirements-pack instead. Drafts for human review; never approves, authorises or signs off.

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Reads two or more supplier, bid or tender responses against a requirements list and returns a DRAFT comparison pack: a requirement-by-supplier matrix, gaps per supplier, clarification questions and neutral observations, for an evaluation panel. Never scores, weights, ranks or recommends a winner; the award stays with the panel. Use when the user asks to "compare these bids", "matrix the tender responses against our requirements", "collate the RFQ replies", "show me where each supplier falls short" or "draft clarification questions for the bidders". Do not use for scoring or weighting against agreed criteria, use supplier-evaluation-matrix instead, or for writing the requirements, use rfp-requirements-pack instead. Drafts for human review; never approves, authorises or signs off.

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Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.

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Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.

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Complete guide for TUnit advanced applications. Use when using TUnit for data-driven testing, dependency injection, or integration testing. Covers MethodDataSource, ClassDataSource, Matrix Tests, Properties filtering. Includes Retry/Timeout control, WebApplicationFactory integration, Testcontainers multi-service orchestration. Keywords: TUnit advanced, TUnit advanced, MethodDataSource, ClassDataSource, Matrix Tests, MatrixDataSource, MicrosoftDependencyInjectionDataSource, Property, Retry, Timeout, data-driven testing, test filtering, WebApplicationFactory TUnit, multi-container orchestration

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Build a single HTML dashboard for comparing metrics across social platforms. Include interactive charts, point details, selected-period comparisons, and light or dark themes, while adapting colors, typography, and components to an existing design guide.

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• Reviewing metrics across multiple social platforms
• Comparing selected periods on charts
• Exploring chart points and related insights

Plan video production and combine tools for footage search, capture, generation, audio, analysis, and captions. Select tools for each task based on quality, budget, available credentials, and offline needs, with guidance for source rights and review.

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• Turning a video concept into a script and storyboard
• Finding footage and music while recording usage rights
• Planning narration, music, and sound effects

Plan game development workflows by matching engines, art and audio tools, automated playtests, packaging, publishing, and analytics to available tools and credentials. Compare options by cost, quality requirements, and offline availability.

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• Plan a small Godot game for a game jam
• Combine AI-generated art with a simple game loop
• Choose tools for automated agent playtesting

Plan image and graphic design workflows by matching tasks to available tools, from AI generation and photo editing to logos, interface mockups, diagrams, and publishing. Compare requirements, costs, and offline options before combining providers.

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• Generate and compose social sharing images
• Create logos and export size variants
• Draft interface wireframes and user flows

Choose tools and workflows for 3D modeling, dimensioned CAD parts, scan processing, rendering, and fabrication. Capability tables and recipes help connect tasks from photographs or initial designs to printable parts, CNC toolpaths, or game assets.

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• Plan a workflow from dimensioned CAD to 3D printing
• Model and export a game asset
• Reconstruct a 3D model from photographs

Find and install command-line tools that let AI agents operate professional software. Search by task or category, check which workflow capabilities are available locally, and install the specific tools needed for a multi-tool project.

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• Find command-line tools for image editing or 3D modeling
• Check available tools for a video production workflow
• Install only the capabilities needed for a task

Integrate or upgrade cache-dit in SGLang diffusion, the image generation system. Follow guidance for resolving code conflicts, adding cache settings, testing feature combinations, and comparing generated images using quality measurements and logs.

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• Upgrading cache-dit in SGLang diffusion
• Resolving conflicts when porting upstream changes
• Adding per-request cache settings

Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.

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• Preparing an LLM for CPU-only use
• Running a local model on Apple Silicon
• Comparing model size and quality across quantization levels

Prioritize product or business assumptions using an impact and risk matrix. Sort them into implementation, deferral, rejection, or testing, then propose experiments for assumptions that need validation, using actual behavior and clear success metrics and thresholds.

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• Deciding which assumptions to test first
• Comparing assumptions by impact and risk
• Defining experiments and success thresholds

Review whether game design requirements are covered by documented architecture decisions. Identify missing coverage, conflicting decisions, and engine compatibility concerns, with an optional matrix linking requirements to development stories and tests.

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• Find game requirements without architecture decisions
• Check for conflicts between architecture decisions
• Review compatibility with the pinned engine version