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This skill covers publication-quality tables and figures for academic research papers. Use when formatting regression results, summary statistics, Monte Carlo output, or research visualizations for LaTeX inclusion. Triggers on "table", "figure", "tabulate", "stargazer", "publication-ready", "LaTeX table", "event study plot", "coefficient plot", "RD plot", "power curve", "specification curve", "binscatter", "format results", "booktabs".

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Thoroughly verify all code, tables, figures, modeling decisions, and quantitative claims in an academic paper against its source R scripts and output files. Use this skill whenever you need to audit, replicate, or verify an academic research paper - including cross-checking LaTeX tables against R output, validating econometric modeling choices, ensuring sample sizes are consistent, building a verification manifest, and running automated replication tests. Trigger this skill for any mention of: paper verification, replication check, table audit, code-paper consistency, reproducing results, verifying estimates, checking coefficients, or any variant of "does the paper match the code."

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Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

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Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive statistics & Table 1 (gtsummary, modelsummary::datasummary, tableone), (4) classical diagnostic tests (shapiro/jarque.bera.test/bptest/dwtest/bgtest/vif/adf.test/kpss.test/Hausman), (5) baseline modeling (fixest::feols, ivreg, did::att_gt, eventstudyr, sun_ab, did_imputation, synthdid, rdrobust, MatchIt, WeightIt, grf::causal_forest, DoubleML, mediation), (6) robustness battery (modelsummary stack, clubSandwich CRSE, fwildclusterboot, ri2, robomit Oster, bacondecomp, HonestDiD), (7) further analysis (interactions + marginaleffects, mediation::mediate, gsem via lavaan, dose-response splines, grf CATE), (8) publication-ready tables & figures (modelsummary, kableExtra, gt, stargazer, texreg, flextable to LaTeX/Word/HTML; ggplot2 + ggpubr + cowplot + binsreg + iplot for figures). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `WeightIt` / `gfoRmula` / `tmle` / `ltmle`, Mendelian randomization via `MendelianRandomization` / `TwoSampleMR` / `MRPRESSO`, KM / Cox / AFT / RMST survival via `survival` / `survminer` / `flexsurv`, E-value sensitivity via `EValue`, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `DoubleML`, S/T/X/R/DR meta-learners via `causalweight` / `grf`, causal forest via `grf::causal_forest`, BART/BCF via `bartCause` / `bcf`, matrix completion via `MCPanel`, CATE distribution + policy tree via `policytree`, off-policy evaluation, conformal causal via `conformalInference` / `cfcausal`, fairness audit via `fairmodels`, DAG learning via `pcalg` / `bnlearn` / LLM-assisted). Use when the user asks for a complete R empirical analysis, wants a tidyverse-style reproducible R script / Quarto workflow, prefers fixest over reghdfe, needs the R counterpart to StatsPAI / 00.1 / 00.2, or names a specific R step in isolation ("feols with cluster", "MatchIt nearest neighbor", "bacondecomp in R", "gtsummary table 1", "modelsummary to Word"). Mode A triggers on "target trial emulation R", "tmle ltmle", "MendelianRandomization", "TwoSampleMR", "MRPRESSO", "survival cox AFT", "STROBE R", "EValue R", "公共健康 R", "流行病学 R". Mode B triggers on "DoubleML R", "grf causal forest", "policytree", "bartCause bcf", "conformal causal R", "fairmodels", "pcalg NOTEARS", "因果机器学习 R".

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Use when the user asks to "map the category narrative", "tear down how competitors tell their story", or "find the language conventions in our market"; produces a category narrative map — the dominant stories and points of view in the category, its language conventions and framing clichés, and a per-competitor narrative teardown (arc, claimed onlyness, proof pattern) plus how each rival's messaging has shifted over time (scraped copy vs archived copy). Not for the positioning canvas itself — use positioning-mapper; not for the beachhead's beliefs and objections — use audience-belief-mapper; not for SERP keyword targeting — use keyword-research; not for claim adjudication — use offer-claims-registry. 品类叙事/竞争叙事拆解/语言惯例/叙事演变

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Builds, runs, debugs, and operates event-driven applications using EventBridge Event Bus - a managed, centrally governed publish/subscribe event bus that an organization can share across many teams and accounts. Applicable when workloads need event-driven architectures, decoupling, choreography, asynchronous integration, pub/sub, fan-out, event ordering, delivered in sequence, event router, event broker, event bus, event store, event retention, event replay, deduplication, avro, protobuf, cloudevents, uses events or messages. Designed as a centrally managed event bus platform allowing governance and control - such as subscriber control, revocation, per-account throttling - while giving application owners flexibility across an organization spanning multiple accounts, with end-to-end open-standards observability and cost allocation. Also helps reduce costs in high fan-out scenarios where multi-account forwarding adds cost. A serverless, managed alternative to self-managed event streaming platforms.

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Resolve data lake and lakehouse asset references across Glue Data Catalog, S3, S3 Tables, and Redshift. Triggers on: find the table, where is our data, which table has, locate dataset, find data for, search catalog, what tables match, Redshift table, lakehouse table, data lake table, warehouse table, reverse lookup S3 path. Do NOT use for: full catalog audits (use exploring-data-catalog), running queries (use querying-data-lake), creating tables (use creating-data-lake-table).

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FAIR-aligned exposure quantification: turns a pile of recon findings into a defensible 0-100 + A-F org risk score (Likelihood x Impact, three ownership-aware factors: exposure/threat/impact), an ownership + proof demotion cap so unproven or weakly-owned findings can't inflate the number, a $-denominated FAIR loss-magnitude estimate (IBM/Ponemon per-record cost bands, cross-source record dedup, threat-factor annualization), attack-path amplification (curated red-team chain catalog + generic graph-walk engine, with a kill-chain vs shared-fate honesty gate), and a board-ready one-pager deliverable (hero $ + letter grade + top-3 findings + top attack path + the ask). Extends osint-methodology's severity rubric and client deliverable templates with quantification. Passive analysis only -- operates on findings already collected, no target traffic, no API keys. Use when asked to score risk, quantify exposure, estimate breach cost, build a board report, translate technical findings to dollars, or explain why a grade or dollar figure came out the way it did.

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Code obfuscation analysis and deobfuscation playbook. Use when reversing binaries protected by junk code, opaque predicates, self-modifying code, control flow flattening, VM protection, or string encryption.

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End-to-end Stata replication pipeline — scaffolds numbered `.do` files in `scripts/stata/`, executes them via the `stata-mcp` MCP server, captures logs and outputs to `output/`, and produces publication-ready tables (esttab) and figures (graph export). Mirrors `/data-analysis` for R-first projects. Use when user says "stata replication", "set up Stata pipeline", "scaffold the .do files", "run Stata analysis", "AEA replication package in Stata", or when a project's analysis language is Stata not R.

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Build or tailor an academic CV for faculty, postdoc, research or fellowship applications: publications, grants, teaching, supervision and service in the order the field expects, with consistent citations. Use when asked to write my academic CV, update my CV for a lectureship, tenure-track or postdoc application, format my publications list, or adapt my academic CV for an industry research role. Produces the full academic CV, a field-appropriate section order, a clean publications list in one citation style, and a two-page summary version when asked.

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Use when the identification argument is the bottleneck for an American Economic Review: Insights (AER: Insights) short-format manuscript — causal identification in an empirical design, parameter identification in a structural model, or treatment-effect identification in an experiment. Stress-tests the strategy so it is clean enough to defend in a few pages, before exhibits are finalized.

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When the user wants to plan, implement, or optimize student and education discount programs. Also use when the user mentions "student discount," "education discount," "student plan," "for students," ".edu discount," "academic pricing," "student verification," "SheerID," "UNiDAYS," or "education program." For startup pricing page, use startups-page-generator.

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Designing meeting schedulers and booking experiences that qualify leads, set up calls well, and convert at higher rates than a generic Calendly link. Availability logic, qualification gating, prep automation, follow-up sequencing. Honest about any-time-friction (no qualification, just a booking link), interrogation-gate (so much qualification it scares users off), and qualified-fast-path (just enough qualification to set up the call well) patterns. Triggers on scheduler design, meeting booking, demo scheduling, sales call scheduling, calendar tool, booking page, qualification flow. Also triggers when sales team complains about cold demos, when booking conversion is poor, or when scheduler is being scoped for the first time.

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Content distribution as a discipline. Owned channels (newsletter, blog, social), earned channels (PR, syndication, mentions), paid channels (boosted posts, syndication networks), and the channel-fit decisions that distinguish strategic distribution from spam-everywhere. Audience-channel matching, content-channel matching, distribution cadence. Triggers on content distribution, channel strategy for content, owned earned paid channels, content amplification, content promotion, audience-channel matching, content-channel matching, distribution cadence, syndication strategy, organic distribution. Also triggers when content is publishing but reach is low, when the team is distributing on every channel without strategy, or when the content program needs a distribution discipline rather than just publication.

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Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.

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Expert knowledge for Azure Web Application Firewall development including best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Front Door/App Gateway WAF rules, geo/bot filters, rate limits, Sentinel logging, or APIM/Azure OpenAI protection, and other Azure Web Application Firewall related development tasks. Not for Azure Application Gateway (use azure-application-gateway), Azure Front Door (use azure-front-door), Azure Firewall (use azure-firewall), Azure Firewall Manager (use azure-firewall-manager).

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Expert knowledge for Azure Communication Services development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building ACS calling, chat, SMS, email, Teams interop, or PSTN/Direct Routing solutions, and other Azure Communication Services related development tasks. Not for Azure Notification Hubs (use azure-notification-hubs), Azure SignalR Service (use azure-signalr-service), Azure Web PubSub (use azure-web-pubsub), Azure AI Bot Service (use azure-bot-service).

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Expert knowledge for Azure Application Gateway development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring App Gateway v2 listeners/routing, WAF/TLS with Key Vault, AKS/AGIC, Private Link, or Containers, and other Azure Application Gateway related development tasks. Not for Azure Front Door (use azure-front-door), Azure Load Balancer (use azure-load-balancer), Azure Virtual Network (use azure-virtual-network), Azure Web Application Firewall (use azure-web-application-firewall).

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Expert knowledge for Azure Application Network development including decision making, and configuration. Use when enabling App Network logs, Azure Monitor metrics, AKS/App Gateway versioning, or upgrade compatibility, and other Azure Application Network related development tasks. Not for Azure Virtual Network (use azure-virtual-network), Azure Virtual Network Manager (use azure-virtual-network-manager), Azure Networking (use azure-networking), Azure Application Gateway (use azure-application-gateway).

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Push notifications for iOS using UserNotifications framework and APNS. Use when integrating APNS push notifications in iOS applications.

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Integrate push and local notifications using FCM and flutter_local_notifications in Flutter. Use for notification delivery or presentation; defer badge-only UI and notification-service tests.

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Integrate Android app notifications using Firebase Cloud Messaging and NotificationCompat. Use when setting up FCM, notification channels, or client-side notification taps; defer Flutter, server sends, WorkManager orchestration, and generic Intent handling.

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Symbolicate .NET runtime frames in Apple platform .ips crash logs (iOS, tvOS, Mac Catalyst, macOS). Extracts UUIDs and addresses from the native backtrace, locates dSYM debug symbols, and runs atos to produce function names with source file and line numbers. Automatically downloads .dwarf symbols from the Microsoft symbol server using Mach-O UUIDs. USE FOR triaging a .NET MAUI or Mono app crash from an .ips file on any Apple platform, resolving native backtrace frames in libcoreclr or libmonosgen-2.0 to .NET runtime source code, retrieving .ips crash logs from a connected iOS device or iPhone, or investigating EXC_CRASH, EXC_BAD_ACCESS, SIGABRT, or SIGSEGV originating from the .NET runtime. DO NOT USE FOR pure Swift/Objective-C crashes with no .NET components, or Android tombstone files. INVOKES Symbolicate-Crash.ps1 script, atos, dwarfdump, idevicecrashreport.

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