AI Analyst Plus

Plugin ai-analyst-plus from marketplace ai-analyst-plugin in ai-analyst-lab/ai-analyst-plugin. Commands read from the repo's manifest — 1 month ago.

Skills (47)

• always-compare

  Never present a metric or number in isolation; anchor every number to a comparison (prior period, benchmark, or another segment) or state that none is available. Use every time you show a number: rates, counts, revenue, averages, query results, chart labels, summary stats. Trigger on output containi

• analysis-design

  Turn a vague hunch or stakeholder request into a rigorous analysis plan via a staged pipeline (hypothesis sharpening, confound scan, V1, feedback, V2 redesign). Trigger on "I think X caused Y", "design an analysis for", "what's driving", "V2 after feedback", "stakeholder comments", "redesign the ana

• analyst-core

  Operating rules for every data analysis. Apply for ANY data-analysis intent: "analyze", "investigate", "why did X change", "compare", "report on", "dashboard", "metrics", "funnel", "retention", "revenue", "conversion", "trend", "segment", "forecast", "how are we doing", "dig into", "break down", or

• archaeology

  Retrieve proven SQL patterns, table cheatsheets, and join patterns from .knowledge/query-archaeology/ so past work gets reused. Fire as a pre-flight step before writing ANY SQL. Also trigger on "do we have a known query for X", "how do we usually join these tables", "have we computed this metric bef

• business

  Browse and search the organization's business context in .knowledge/organizations/: glossary terms, product catalog, metric definitions, OKRs, team structure. Trigger on "/business", "show me our glossary", "what metrics are defined?", "what products do we have?", "show me our OKRs", "who's on which

• causal

  Causal inference toolkit for when experiments are not possible: estimate treatment effects from observational data with assumption checks and mandatory caveats. Invoke as /causal. Trigger on "causal", "caused", "impact of", "effect of", "attribution", "counterfactual", "difference-in-differences", "

• close-the-loop

  End every analysis that makes a recommendation with an actionable follow-up plan. Use ONLY AFTER a specific recommendation ("we should...", "I recommend..."); add the checklist automatically even if not asked. DO NOT use for pre-recommendation exploratory questions like "should we investigate this?"

• color-commentary

  Every chart ships with 1-2 sentences of commentary, baked into the chart subtitle AND a sidecar .md, telling the reader what to NOTICE, not what the chart depicts. Trigger every time you save, render, embed, or caption any visual, and when presenting or re-sharing a chart that ALREADY EXISTS: regene

• compare-datasets

  Compare metrics, findings, and patterns across two or more connected datasets. Trigger on "compare datasets", "is this pattern the same across", "how does X differ between datasets", "cross-dataset comparison", "compare metrics between", or asking whether a finding is unique to one dataset. Also off

• connect-data

  Guided wizard to connect a new dataset for analysis in Cowork: find CSV/Excel files, open local DuckDB, or point at a cloud warehouse via a connector, then profile the schema and register the dataset in .knowledge. Trigger on "/connect-data", "connect my data", "add a new dataset", "set up my data",

• context-compare

  Advanced: runs the same question under two configurations and diffs the results. Ask one analytics question with a piece of context and without it, and measure what changed. Trigger on "/context-compare", "run it with and without <the definition/context>", "does adding <X> change the answer", "is th

• data-inspect

  Show the active dataset's schema: tables, columns, row counts, relationships. Trigger on "/data" or "/data {table}", "what tables do I have?", "show me the schema", "what columns are in the users table?", "list tables", "describe the data", "data dictionary". DISAMBIGUATION: schema inspector only; f

• data-map

  Produce a cross-table data health map for the active dataset: row counts, date alignment, join rates, relationships, opening hypothesis. Trigger on dataset-wide open questions: "tell me about this data", "what's in here", "give me an overview", "map out the data", "what do I have", "what does this d

• data-profiling

  Deep-profile the active dataset: distributions, temporal patterns, correlations, completeness gaps, anomalies. Use after connecting a new dataset. Trigger on "profile this data", "deep-profile the dataset", "run a data profile", "check distributions", "find anomalies in the data", "how complete is t

• data-quality-check

  Validate data completeness, consistency, and coverage before any analysis, flagging issues with severity ratings. Run at the start of every new analysis. Trigger on "check data quality", "is the data clean", "validate the data", "run a quality check", "what's the coverage", and on named-table questi

• datasets

  List all connected datasets with status, table counts, and last analysis date, and switch the active dataset. Trigger on "/datasets", "/switch-dataset", "what datasets do I have?", "show me my data sources", "list datasets", "which datasets are connected?", "what data is available?", "switch dataset

• deck-critique

  Score a presentation slide-by-slide against the Data Story Checklist (SO-WHAT, STAKES, EVIDENCE, ASK): per-slide scorecards, anti-pattern flags, an A-F grade, and prioritized fixes. Trigger on "review my deck", "critique this presentation", "score my slides", "evaluate this deck", "deck feedback", "

• deck-rescue

  Take a failing deck end-to-end and produce a complete rewrite with proper narrative structure (Context-Tension-Resolution arc, action headlines, a specific ask). Use when a deck scores D or F on /deck-critique, or the user says "rescue", "rewrite", "redo", or "fix" an entire deck, "this deck is a me

• distribution-profiler

  Single-column distribution deep-dive. Profile the statistical distribution of a data column and produce an analytical playbook: distribution identification, valid summary stats, recommended tests, A/B guidance, traps. Trigger on "profile this column", "what distribution is this", "check the distribu

• drop-off-format

  Every step-to-step drop-off must show three numbers: raw users entering the step, raw users exiting, and the drop-off percentage. Trigger whenever output contains funnel steps, conversion rates between stages, or step-to-step comparisons, in tables, narrative, chart annotations, or Slack posts. A dr

• eval

  Advanced: needs a gold-case YAML you supply and a live data connection. Run the held-out gold suite live against the analyst and score it. Trigger on "/eval", "run the eval suite", "score the system", "run the train split", "check the test split", "what's our accuracy on the gold cases". Drives the

• experiment-brief

  The intake step when someone proposes a test. Auto-generate a structured experiment brief when a user expresses intent to test something: hypothesis, ONE north star metric, guardrails, pre-registered success criteria, feasibility. Trigger on "I want to test...", "Let's experiment with...", "Should w

• experiment

  The analysis and lifecycle owner for experiments. Full experiment lifecycle: design, power analysis, statistical analysis, interpretation, reporting, and monitoring of A/B tests. Invoke as /experiment. Trigger on "A/B test", "experiment", "treatment vs control", "sample size", "MDE", "statistical si

• explore

  Quick, interactive data exploration without the full pipeline. Trigger on "/explore", "let me explore the data", "browse the data", "show me a sample of the data", "what does the distribution look like?", "explore this table". DISAMBIGUATION: interactive poke-around within a known dataset; for a fir

• export

  Export analysis results in different formats for different audiences: email summaries, Slack updates, decision briefs, Google Docs with charts, Word docs, slide decks, or CSVs. Trigger on "/export", "export this as...", "send this to...", "share this analysis", "create a Google Doc", "make a Word do

• forecast

  Generate time-series forecasts and projections for metrics. Trigger on "/forecast", "what will X look like next month?", "forecast DAU for Q2", "project revenue", "estimate future growth", "predict next quarter", "where are we headed?", "extrapolate this trend", "what will happen if this continues?"

• google-doc-export

  Create properly formatted Google Docs through the Google Docs/Drive connector, preventing text/image overlap, broken heading hierarchy, and inconsistent formatting. Use automatically whenever building a Google Doc or calling any Google Docs connector tool (create/append/format document, insert image

• google-slides-export

  Create properly formatted Google Slides through the Google Drive connector's Slides capabilities, preventing common errors (zero-weight outlines, short object IDs, oversized batches, text overflow) with a design system and pre-validated slide recipes. Apply on any Google Slides API call, when conver

• guardrails

  Pair every success metric with guardrail metrics and check for trade-offs before presenting improvements as wins. Trigger when defining a metric or spec, and on any positive metric movement: "X improved", "X is up", "X increased", "better performance on X", "we're seeing gains in X". Never present a

• knowledge-bootstrap

  Initialize all knowledge subsystems at session start: active dataset context, user profile, corrections, learnings, query archaeology, analysis history. Run automatically at the start of EVERY new session before responding to any analytical request, and after "/connect-data" or "/switch-dataset" to

• log-correction

  Record analyst mistakes, fixes, and reusable learnings so future analyses never repeat an error. Fires automatically when the user corrects work ("actually it's Y", "that's wrong") or teaches a rule ("always use X", "never include test users", "remember that our fiscal year starts in February"), and

• metric-spec

  Define any metric completely with a standardized template: calculation, denominator, time window, filters, interpretation. Trigger on "define this metric", "how should we measure X?", "what's the right way to calculate Y?", "document our metrics", "create a metric definition", "different teams are m

• metrics

  Browse, search, and display metric definitions from the active dataset's metric dictionary. Trigger on "/metrics", "show me the metrics", "what metrics do we track?", "how is [metric name] calculated?", "what's the definition of [metric]?", "list all metrics", "define conversion rate", "how do we me

• notion-export

  Export analysis results to a Notion page with structure, embedded charts, data stamps, and provenance toggles. Trigger on "/export notion", "export to Notion", "create a Notion page", "share this in Notion", "put this in Notion", "send to Notion", or mentions of the Analysis Gallery database. Handle

• notion-ingest

  Requires the Notion connector. Crawl a Notion workspace to extract business terms, metrics, product docs, and team structure into the organization knowledge system. Trigger on "/notion-ingest", "import our team's Notion docs", "sync our product roadmap from Notion", "pull in our metric definitions f

• patterns

  Browse, search, and leverage recurring analytical patterns discovered across past analyses. Trigger on "what patterns have we seen?", "have we seen this behavior before?", "show me recurring patterns", "what's consistent in our data?", "any known patterns about [metric/segment]?", "search patterns f

• question-framing

  Structure analytical questions with the Question Ladder (Goal, Decision, Metric, Hypothesis), then fill the 7-field Analysis Design Spec, before touching data. Trigger on "analyze", "investigate", "look into", "why did", "what's happening with", "what happened with", "figure out", "explore", "deep d

• reliability

  Check whether an AI analysis answer is STABLE by running the same question several independent times and measuring what holds versus what drifts. Trigger on "/reliability", "run this a few times", "is this answer stable / reliable", "check reliability of <question>", "does it give the same answer ag

• setup-notion

  Guided Notion connection setup wizard. Verifies the Notion connector is enabled, walks the user through enabling and authorizing it, and checks for an Analysis Gallery database. Use when the user says "/setup-notion", "connect to notion", "set up notion", "I want to export to Notion", or when a Noti

• slide-transform

  Take one bad slide and produce 2-3 redesigned variants with before/after scoring against the Data Story Checklist (SO-WHAT, STAKES, EVIDENCE, ASK). Trigger on "fix slide 3", "make this slide better", "transform this slide", "improve the headline", "this slide is confusing", "redesign this", "show me

• srm-check

  The randomization gate before reading any experiment result. Detect Sample Ratio Mismatch (SRM) in experiment data before any analysis proceeds. Auto-fire when data has columns like "variant", "group", "treatment", "control", "arm", "experiment_group", "bucket", or when the user says "A/B test", "ex

• stakeholder-communication

  Adapt analytical findings to the audience: same insight, different framing, detail level, and format. Trigger whenever an audience is mentioned (executive, PM, engineer, leadership, stakeholders): "prepare this for leadership", "write this for the product team", "create a deck for the VP", "make thi

• stress-test

  Pressure-test any analysis plan or approach for hidden methodological flaws before execution: wrong baselines, survivorship bias, missing segments, uncontrolled confounds, absent kill criteria. Trigger on "/stress-test", "stress test this plan", "review my analysis design", "check my approach", "is

• trace

  Show the provenance of every reported number: which query or computation produced it, from which file or table. Use after an analysis when someone asks "where did that number come from?", "show your work", "prove it", or "/trace". Also use proactively before a deliverable ships, as part of the check

• tracking-gaps

  Assess whether the data needed for an analysis exists, identify what's missing, and produce prioritized instrumentation requests for engineering. Trigger on "do we track...", "is there data for...", "can we measure...", writing an instrumentation request, queries returning unexpected nulls, or verif

• triangulation

  Cross-reference and validate findings before presenting them: mandatory segment-first Simpson's Paradox check, denominator changes, survivorship bias, plausibility vs benchmarks. Deep mode adds a scored 4-layer battery with an A-F confidence grade for high-stakes deliverables. Use after EVERY analys

• visualization-patterns

  Apply whenever generating ANY chart, graph, or data visualization: bar, line, scatter, heatmap, funnel, distribution, dashboard. Trigger on "make a chart", "show me a graph", "visualize the funnel", "plot revenue over time", "create a distribution", "build a dashboard", or "visualize". Enforces Stor

Slash commands (1)

• /ai-analyst-plus:analyst

  Start an analysis the AI Analyst Plus way, framing the decision first and running the method end to end

Subagents (13)

• causal-analyzer

  Execute the selected causal inference method on data using the causal skill's bundled causal_stats package. Produces point estimates, confidence intervals, and diagnostic charts.

• causal-assumption-checker

  Per-method diagnostic battery that checks whether the assumptions required for the selected causal method hold in the data.

• causal-method-selector

  Interactive decision tree for selecting the right causal inference method based on data availability, study design, and assumptions.

• causal-report-generator

  Generate a complete causal inference report with mandatory sections including method choice, results, assumption checks, sensitivity analysis, confidence assessment, and non-negotiable caveats.

• causal-sensitivity

  Sensitivity analysis for observational causal estimates — Rosenbaum bounds, E-values, and placebo tests to assess robustness to unmeasured confounding.

• confound-scanner

  Adversarial agent that finds threats to analytical validity — concurrent changes, data quality issues, selection biases, and measurement artifacts that could invalidate an analysis.

• experiment-analyzer

  Full experiment analysis workflow — from data loading through SRM validation, treatment effects, segment analysis, novelty checks, guardrail evaluation, and nuanced ship/kill/iterate recommendation.

• experiment-designer

  Design experiments or quasi-experimental analyses to test causal hypotheses, including power estimation, guardrail selection, and pre-registered decision rules.

• experiment-interpreter

  Walk the Result Interpretation Tree to classify experiment outcomes as Ship/Abort/Learn/Invalid using Spotify's EwL framework. References pre-registered decision rules from experiment.yaml.

• experiment-monitor

  Daily monitoring for running experiments — SRM trending, guardrail status, sample accumulation tracking, and power projection. Uses coded helpers from experiment_stats.

• experiment-readout

  Transform experiment analysis results into a stakeholder-ready readout with executive summary, visualizations, per-segment decisions, ramp plan, and follow-up experiment proposals.

• feedback-synthesizer

  Takes V1 analysis findings and messy stakeholder feedback (comments, meeting transcripts, Slack threads), categorizes the feedback, identifies what V1 got right vs. wrong, and produces a structured V2 investigation plan with a stakeholder answer map.

• hypothesis-sharpener

  Takes a vague analytical hunch and transforms it into a testable hypothesis with precise metrics, comparison groups, key segments, natural experiments, and accept/reject criteria.