Processing
Data transformation and parsing
Uses and selection guide
Use these skills to collect, transform and examine datasets. Compare supported formats, data volume and handling of missing values; inspect a small sample before applying a transformation to the complete dataset.
analytics-pipeline
by dadbodgeoff
Real-time analytics with Redis counters, periodic PostgreSQL flush, and time-series aggregation. High-performance event tracking without database bottlenecks.
community-feed
by dadbodgeoff
Social feed with batch queries, cursor pagination, trending algorithms, and engagement tracking. Efficient database queries for infinite scroll feeds.
fuzzy-matching
by dadbodgeoff
Multi-stage fuzzy matching pipeline for entity reconciliation. PostgreSQL trigram pre-filter, salient overlap check, and multi-factor similarity scoring.
analyzing-outlook-pst-for-email-forensics
by mukul975
Analyze Microsoft Outlook PST and OST files for email forensic evidence including message content, headers, attachments,
adding-ingestion-warnings
by PostHog
How to add a new ingestion warning type to the event ingestion pipeline. Use when emitting a new warning from nodejs ingestion code (emitIngestionWarning, captureIngestionWarning, pipeline warnings arrays, drop() with warnings), when adding a warning type, category, or severity, or when a typecheck error says a string is not assignable to IngestionWarningType. Covers the INGESTION_WARNING_TYPES registry (the single source of truth for type, category, and severity), the details-key conventions that ClickHouse v2 materializes into columns, debouncing, and the downstream surfaces to keep in sync (v1 UI map, resolving-ingestion-warnings skill, docs, v2 API).
exploring-llm-clusters
by PostHog
'Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.'
downloading-batch-export-files
by PostHog
Export PostHog events, persons, sessions, or the results of a HogQL query on demand and download the resulting files. Use when the user asks to download/export raw PostHog data, export HogQL query results, create a one-off file export, fetch a Parquet or JSONLines export, or use the file_download_batch_exports API. Covers starting the export with MCP, polling completion, and downloading via the existing REST redirect endpoint.
modeling-dimension-tables
by PostHog
Build reusable dimension / lookup tables for a star schema — country/region, timezone, currency, date, plan/product, and other descriptive attributes — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model dimension tables, lookup tables, a star schema, conformed dimensions, or wants to enrich events/revenue/usage with country, region, timezone, plan, or currency attributes without repeating JOINs. Covers sourcing the dimension data (upload, warehouse source, or derive from events), shaping it into an aliased one-row-per-entity view (optionally materialized on a slow schedule since dimensions change rarely), and attaching it to facts via a saved or person join so its columns read as native fields. Key rule: for currency use the built-in convertCurrency() instead of a hand-rolled rate table. Read modeling-warehouse-foundations first; dimensions here are reused by the revenue, conversion, activation, and product-usage modeling skills.
cuml-machine-learning
by langchain-ai
Use for GPU-accelerated machine learning on tabular data using NVIDIA cuML. Triggers when tasks involve classification, regression, clustering, dimensionality reduction, or model training on datasets.
gpu-document-processing
by langchain-ai
Use when processing large PDFs, document collections, or bulk text extraction tasks that benefit from GPU-accelerated processing. Triggers when the user provides large documents or needs bulk document analysis.
cudf-analytics
by langchain-ai
Use for GPU-accelerated data analysis on datasets, CSVs, or tabular data using NVIDIA cuDF. Triggers when tasks involve groupby aggregations, statistical summaries, anomaly detection, or large-scale data profiling.
deepagents-thread-inspector
by langchain-ai
Inspect and explain conversations in the local Deep Agents Code SQLite session store. Use as a fallback when LangSmith trace tooling is unavailable, for offline or untraced sessions, or when asked to identify or summarize a local dcode thread, inspect checkpoint metadata, list recent local threads, or parse ~/.deepagents/.state/sessions.db and a thread UUID or prefix.
access
by anthropics
Manage iMessage channel access — approve pairings, edit allowlists, set DM/group policy. Use when the user asks to pair, approve someone, check who's allowed, or change policy for the iMessage channel.
example-command
by anthropics
An example user-invoked skill that demonstrates frontmatter options and the skills/<name>/SKILL.md layout
receipts
by anthropics
Generate a personal Claude Code usage & impact report ("receipts") from this machine's local session transcripts — for justifying Claude Code usage/spend to a manager, self-review, or "what have I been using this for" check-ins. Mines ~/.claude/projects locally (no extra API calls beyond one final write-up), cross-references local git history, and writes a markdown report plus a self-contained HTML receipt to your home directory. Use when the user asks for "receipts", an "impact report", "usage report", wants to "show my Claude Code activity", "prove the value of Claude Code", or runs /receipts.
access
by anthropics
Manage Telegram channel access — approve pairings, edit allowlists, set DM/group policy. Use when the user asks to pair, approve someone, check who's allowed, or change policy for the Telegram channel.
Evaluating Paper Relevance
by brycewang-stanford
Two-stage paper screening - abstract scoring then deep dive for specific data extraction
Traversing Citation Networks
by brycewang-stanford
Smart backward and forward citation following via Semantic Scholar, with relevance filtering and deduplication
api-data-fetcher
by brycewang-stanford
Fetch economic data from FRED, World Bank, and other APIs
reproducible-pipelines
by brycewang-stanford
This skill covers reproducible research pipelines and replication packages. Use when the user is setting up a research project directory structure, configuring workflow managers (Make, Snakemake, DVC), managing computational environments, preparing replication packages for journal submission, or debugging reproducibility failures. Triggers on "reproducible", "replication package", "Makefile", "Snakemake", "DVC", "pipeline", "workflow manager", "data versioning", "conda environment", "Docker", "seed management", "AEA data editor", "replication", "project structure", or "submission checklist".
Full-empirical-analysis-skill-Stata
by brycewang-stanford
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. 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 Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). 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 teffects ipw / teffects ipwra / teffects aipw / eltmle, Mendelian randomization via mrrobust (IVW / Egger / weighted median) and mregger / mrpresso, KM / Cox / AFT / RMST survival via sts / stcox / streg / strmst2, E-value sensitivity via evalue (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and Mode B — ML causal inference (DML via ddml / pdslasso, S/T/X/R/DR meta-learners via crforest and ddml interactive, causal forest via crforest / cforest, BART/BCF via bart / bartCause-style externals, CATE distribution + policy tree via crforest, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via pcalg / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".
python-panel-data
by brycewang-stanford
Panel data analysis with Python using linearmodels and pandas.
Full-empirical-analysis-skill-Stata
by brycewang-stanford
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. 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 Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). 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 teffects ipw / teffects ipwra / teffects aipw / eltmle, Mendelian randomization via mrrobust (IVW / Egger / weighted median) and mregger / mrpresso, KM / Cox / AFT / RMST survival via sts / stcox / streg / strmst2, E-value sensitivity via evalue (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and Mode B — ML causal inference (DML via ddml / pdslasso, S/T/X/R/DR meta-learners via crforest and ddml interactive, causal forest via crforest / cforest, BART/BCF via bart / bartCause-style externals, CATE distribution + policy tree via crforest, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via pcalg / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".
Answering Research Questions
by brycewang-stanford
Main orchestration workflow for systematic literature research - search, evaluate, traverse, synthesize