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Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool profiling. Covers tracer choice, isotopologue vs isotopomer, mass-isotopomer distributions (MID), fractional enrichment, the mandatory natural-abundance + tracer-purity correction (IsoCor, AccuCor), and the metabolic/isotopic steady-state vs non-stationary (INST-MFA) distinction. Use when feeding a labeled tracer and interpreting labeling patterns, correcting raw isotopologue intensities, computing or plotting an MID, or deciding tracing vs abundance profiling. For absolute pool concentration and MRM mechanics see metabolomics/targeted-analysis; for constraint-based genome-scale flux (FBA, not empirical tracing) see systems-biology/flux-balance-analysis; for feature detection see metabolomics/xcms-preprocessing; for pathway enrichment that ignores the pool-vs-flux caveat see metabolomics/pathway-mapping.

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Assigns honest lipid annotation levels, designs class-based internal-standard quantification, and runs lipid-aware differential and enrichment analysis with lipidr, guarding against in-source-fragment phantoms, sn-position over-claims, and invalid cross-class quantification. Use when naming or canonicalizing lipid species (shorthand separators, Goslin), deciding shotgun vs RP vs HILIC LC-MS, picking internal standards (SPLASH/EquiSPLASH), interpreting MS-DIAL/LipidSearch output, or comparing lipid classes. For general feature detection see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for non-lipid annotation confidence see metabolomics/metabolite-annotation; for normalization/QC see metabolomics/normalization-qc; for multivariate stats see metabolomics/statistical-analysis.

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Work on lyrics — the web fallback (LRCLIB, Musixmatch, NetEase, Genius), the in-memory cache, verification, and embedding lyrics into audio file tags. Use this whenever a task touches `services/lyrics.py`, `services/tags.py`, the `/lyrics.json` route, or `scripts/embed_lyrics.py` / `embed_lyrics_cron.sh` — adding or fixing a provider, changing caching/verification, or the batch tagger. Covers the provider contract, the synced-vs-plain rule, the read-only cache, and why writes only ever store plain text.

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Plan product analytics with event tracking, conversion funnels that reveal drop-offs, and retention analysis by user group. Includes PostHog examples and guidance for evaluating A/B tests, defining a core product metric, and designing dashboards.

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• Designing events to track product usage
• Investigating drop-offs before signup or purchase
• Comparing weekly retention across user groups

Analyze customer profitability and revenue durability for SaaS, subscription, and other recurring businesses. Examine acquisition costs, retention, and revenue trends by customer group, then produce an Excel workbook, summary slide, and issues for further investigation.

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• Assessing recurring revenue quality in an investment target
• Tracking revenue by customer acquisition year
• Comparing acquisition costs with customer lifetime value

Plan technology strategy and engineering team growth with frameworks for technical debt, hiring, architecture decisions, and vendor evaluation. Use Python tools and decision templates to prioritize improvements, structure teams, and track engineering performance.

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• Prioritizing technical debt reduction
• Planning hiring and engineering team structure
• Comparing technologies and vendors

Plan, implement, and audit website or app analytics with GA4 and Google Tag Manager. Define meaningful events, standardize campaign tags, and validate data collection so marketing and product decisions use consistent measurements.

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• Plan tracking for signups and purchases
• Review existing GA4 and GTM tracking
• Standardize UTM campaign naming

Review product performance by comparing usage, retention, revenue, and other metrics with previous periods and targets. Turn the numbers into a scorecard, explain meaningful trends and uncertainties, and recommend investigations or improvement experiments.

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• Prepare weekly, monthly, or quarterly metrics reviews
• Investigate sudden spikes or drops
• Compare product performance with targets

Monitor TPU workloads on GKE using system metrics and PromQL, a metrics query language. Check utilization, memory, node availability, and interruptions to investigate infrastructure issues and calculate average recovery times and intervals between interruptions.

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• Checking TPU utilization and memory usage
• Investigating node and node pool availability
• Tracing maintenance and preemption interruptions

Investigate crashes, out-of-memory errors, and device initialization failures on GKE TPU v6e nodes. The skill checks logs and custom metrics collection for conflicting operations, then guides temporary mitigation by disabling an identified collector.

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• Investigate vbar_control_agent crashes and memory exhaustion
• Check tpu-device-plugin metrics checksum errors
• Inspect conflicts with custom TPU metrics collectors

Configure GKE logging, metrics collection, dashboards, and alerts with Cloud Logging, Cloud Monitoring, and managed Prometheus. Diagnose missing measurements, collection failures, permission errors, and alert rule problems to identify appropriate configuration changes.

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• Setting up GKE logging and metrics collection
• Investigating missing Prometheus metrics
• Planning dashboards and alerts for cluster health

Search YouTube by keyword and upload date, then collect video metrics and channel details through BrowserAct. Results include views, likes, comments, duration, and subscriber counts for comparing videos and researching activity around a topic.

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• Collecting metrics for videos published this week
• Researching recent videos from competing channels
• Comparing reactions to campaign videos

Simulate quantum systems in Python with QuTiP, including open systems that interact with their surroundings. Calculate time evolution, energy loss, and entanglement, then visualize states and results for physics research or educational experiments.

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• Simulate the decay of a harmonic oscillator
• Track changes in two-qubit entanglement
• Study interactions between an atom and a cavity

Create summary tables, treatment and control comparisons, and correlation matrices using Python, R, or Stata. Summarize averages, variation, sample sizes, and missing data to understand a dataset before regression or prepare tables for a research paper.

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• Explore a dataset before regression
• Prepare a paper's summary statistics table
• Compare treatment and control groups

Draft or revise introductions for economics papers using a clear sequence: motivation, research question, empirical approach, findings, and contribution to existing literature. A checklist helps identify vague claims, missing results, misplaced literature discussion, and excessive length.

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• Draft an introduction for an economics paper
• Improve the opening motivation and research question
• Reorder findings and discussion of related literature

Evaluate scientific manuscripts and grant proposals for sound methods, statistics, reproducibility, ethics, and clear reporting. Produce a structured review that separates major concerns from minor issues and gives authors specific, constructive suggestions for revision.

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• Preparing a structured review of a scientific manuscript
• Evaluating methods and statistics in a grant proposal
• Checking reproducibility and reporting guidelines

Turn written instructions into infographics for statistics, comparisons, timelines, and more. Choose layouts and color palettes, optionally research supporting facts, and receive versioned images with automated quality reviews and refinement when scores fall below the chosen threshold.

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• Turning survey results into a statistical infographic
• Creating a side-by-side product comparison
• Visualizing company history or project milestones

When the user wants to set up, interpret, or improve their app analytics and tracking. Also use when the user mentions "analytics", "tracking", "metrics", "KPIs", "App Store Connect analytics", "install tracking", "funnel", "attribution", or "how is my app performing". For A/B testing, see ab-test-store-listing. For retention metrics, see retention-optimization.

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Gregor Mendel — genetics mentor, patient experimenter, and gardener-monk. Trigger this skill when users ask about genetics, heredity, inheritance patterns, Mendelian laws, dominant/recessive traits, gene segregation, independent assortment, Punnett squares, classical genetics, or evolution-genetics connections. Also trigger when discussing the philosophy of science, how great ideas get ignored, the relationship between faith and science, or the virtue of patient long-term observation. Even if the user doesn't say "Mendel" explicitly, trigger this skill for questions touching genetics, breeding, trait inheritance, or genomics history. Part of the AIPOCH Science Mentor Skill Hub.

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This skill should be used when the user asks about "key startup metrics", "SaaS metrics", "CAC and LTV", "unit economics", "burn multiple", "rule of 40", "marketplace metrics", or requests guidance on tracking and optimizing business performance metrics.

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Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

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TikTok Shop analytics and performance tracking. Video metrics, live selling data, affiliate performance, advertising analytics, and business intelligence. Use when the user asks about TikTok Shop analytics, performance tracking, metrics analysis, or data-driven optimization.

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Product analytics for instrumenting products, defining metrics, and building retention funnels. Use when designing a metric tree, instrumenting a feature, auditing instrumentation, defining a North Star, or building an analytics roadmap.

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Expert patterns for Godot 2D physics including collision layers/masks, Area2D triggers, raycasting, and PhysicsDirectSpaceState2D queries. Use when implementing collision detection, trigger zones, line-of-sight systems, or manual physics queries. Trigger keywords: CollisionShape2D, CollisionPolygon2D, collision_layer, collision_mask, set_collision_layer_value, set_collision_mask_value, Area2D, body_entered, body_exited, RayCast2D, force_raycast_update, PhysicsPointQueryParameters2D, PhysicsShapeQueryParameters2D, direct_space_state, move_and_collide, move_and_slide.

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