Results for “analysis”

Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.

Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.

Creates detailed, sectionized implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.

Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.

`analysis-agent`/`task-agent`/`review-agent`: use for input authority, parsing, canonicalization, bounds, state checks, writable fields, rejection, or external-response changes.

`analysis-agent`/`task-agent`: use for persisted identity, relationship, ownership, lifecycle, cardinality, or invariants; skip repository, migration, and mapping-only work.

`analysis-agent`/`review-agent`: use when module ownership, public surface, dependency direction, cycles, or shared state changes; skip owner-internal placement.

`analysis-agent`: use when a request needs normal, failure, edge, abuse, recovery, or operational scenarios; skip when no scenario-decomposition decision exists.

Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.

Creates detailed, sectionized implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.

Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.

Use when writing Python code for DSGE models, HANK models, numerical economic computation, causal inference, or quantitative economic data analysis

Patterns for Bayesian inference in R using brms, including multilevel models, DAG validation, and marginal effects. Use when performing Bayesian analysis.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"

PPC financial calculator and modeling tool. CPA, ROAS, CPL calculations, break-even analysis, impression share opportunity sizing, budget forecasting, LTV:CAC ratio analysis, and MER (Marketing Efficiency Ratio) assessment. Requires zero API access. Works with pasted data from exports. Use when user says PPC math, ad calculator, break-even, budget forecast, ROAS calculator, CPA calculator, impression share, LTV CAC, or MER.

Build and analyze genome-scale constraint-based metabolic models with COBRApy — flux balance analysis (FBA), flux variability analysis (FVA), gene and reaction knockouts, flux sampling, and SBML model I/O. Use when simulating metabolic networks, predicting growth or knockout phenotypes, or running systems-biology and metabolic-engineering analyses on SBML genome-scale models. Part of the AlterLab Academic Skills suite.

Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"

Optimize post-signup onboarding and activation to turn signups into active users.

Converts narrative technical documents into structured mathematical problem specifications with variables, constraints, objectives, and uncertainty.

Generates a comprehensive .editorconfig file based on project analysis and user preferences, ensuring consistent coding styles across editors.

Fuzz Rust projects with libFuzzer using cargo-fuzz, including harness writing, sanitizer integration, and coverage analysis.

Analyzes an app from video, screenshots, or description, then generates implementation stories in dependency order.

Explora el código y el dominio del problema antes de proponer un cambio, documentando hallazgos y recomendaciones.

Creates GitHub pull requests, issues, and discussions with semantic analysis, template selection, and label application.

This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions

Beta coefficient reference — CAPM, systematic risk, portfolio sensitivity, regression analysis. Use when measuring stock volatility relative to the market or constructing risk-adjusted portfolios.

Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.

Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.

`analysis-agent`/`task-agent`/`review-agent`: use for provider-consumer compatibility of APIs, events, schemas, or behavior; skip without independent consumer/version risk.

`analysis-agent`/`task-agent`/`review-agent`: use for release identity, compatibility, exposure, stop, rollback, or recovery; skip pipeline-only and no-release work.

`analysis-agent`/`task-agent`: use when actor journeys change entries, branches, interruption, recovery, authority, or side effects; skip route/state-only work.

`analysis-agent`/`review-agent`: use when proven variation needs extension points, plugins, policy hooks, compatibility, or deprecation; skip when no extension decision exists.

`analysis-agent`/`task-agent`/`review-agent`: use for structure, workspace or package bootstrap, config, scripts, or quality gates; skip without an initialization decision.