ai-analyst-plus

Run the held-out gold suite live against the analyst and score it. Use when the user types "/eval", or asks to "run the eval suite", "score the system", "run the train split", "check the test split", "what's our accuracy on the gold cases", or wants the system-level eval number (accuracy + query-similarity + cost). This DRIVES the analyst on each question (it does not grade hand-supplied answers), keeps the gold blind, and writes a self-describing run record. Pairs with /reliability (stability, no key) and /compare (two configs or two models).

Initialize all knowledge subsystems at session start to load active dataset context, user profile, corrections, learnings, query archaeology, and analysis history into working memory. Use this skill automatically at the start of EVERY new session, immediately after the conversation begins. Also trigger after `/connect-data` or `/switch-dataset` to reload dataset context. Apply when the system detects missing or stale knowledge files, when returning to the tool after a break, during session initialization, at the beginning of any conversation, when Claude Code first starts, when a new Claude conversation opens, or at any session boundary. This skill is critical for contextual awareness — it ensures you understand the user's data environment, preferences, and past work before beginning any analysis. Without running this skill at session start, you lack essential context about what dataset is active, what the user prefers, and what corrections have been logged. Always run this skill first, before responding to a

Check whether an AI analysis answer is STABLE by running the same question several independent times and measuring what holds versus what drifts. Use when the user types "/reliability", or asks to "run this a few times", "is this answer stable / reliable", "check reliability of <question>", "does it give the same answer again", "run it N times and compare", or wants to know if a number can be trusted enough to act on. This is the cheapest eval and the only one that needs no answer key: it tells you about stability, not correctness.

Show the provenance trace — every reported number linked to the SQL that produced it, with a confidence badge. Use after an analysis when someone asks "where did that number come from?"

Validate data completeness, consistency, and coverage before any analysis begins, flagging issues with severity ratings so the analyst knows what blocks analysis vs. what to note as a caveat. Use this skill at the start of EVERY new analysis — whenever you're about to query data, explore a dataset, design an investigation, or answer an analytical question. Also apply when connecting to a new data source, when results look suspicious or unexpected, when a user mentions data quality concerns, when switching datasets, when profiling a new table, or any time before drawing conclusions from data. Trigger on phrases like "analyze this data", "explore the dataset", "what's in this data", "investigate why", "check data quality", "is the data clean", "profile this table", "validate the data", "run a quality check", "before we analyze", "what's the coverage", or any request that involves querying or analyzing data. **This skill also fires whenever the user asks about a specific named table** — e.g., "tell me about the

Run a comparison: ask one analytics question two ways, with a piece of context and without it, and measure what changed. Use when the user says "/compare", "run it with and without <the definition/context>", "does adding <X> change the answer", "is this context worth it", or wants to see whether a metric definition (or any context) moves the answer and collapses the drift. This is the context-side companion to /reliability: reliability says stable or drifts under one setup; compare says what changed between two setups.

Independently validate the current analysis with a second model (OpenAI Codex). Codex re-derives the same answer from the same data — blind to Claude's SQL and numbers — and the skill reports AGREE / DISAGREE / PARTIAL per finding. Use when the user types "/codex-review", or says "validate with codex", "codex review", "second opinion from codex", "have the other model check this", "independently verify this analysis", "does codex agree", "cross-check this with gpt/codex", or wants a different model to confirm a result before acting on it. This is multi-model validation: a real independent re-analysis, not a critique of Claude's work. If the Codex plugin or CLI isn't installed, this skill detects that and walks the user through setup first.

Show the active dataset's schema — tables, columns, row counts, and relationships. Optionally drill into a specific table. Use this skill whenever the user invokes `/data` or `/data {table}`, or asks questions like "what tables do I have?", "show me the schema", "what's in this dataset?", "what columns are in the users table?", "show me table structure", "what data is available?", "list tables", "describe the data", "what's in my database?", or any request to inspect, browse, or understand the structure of the active dataset. Also trigger when users mention "schema", "columns", "tables", "data dictionary", "data structure", or when they need to understand what data they're working with before starting an analysis. This is a foundational command that should be offered proactively when users seem unsure about available data or table structure. DISAMBIGUATION: this is the `/data` SCHEMA inspector (structure — tables, columns, types, row counts). For a dataset-wide health + relationships overview ("tell me about

Produce a comprehensive cross-table data health map for the active dataset — the full payoff answer to open-ended "tell me about this data" questions. Run all tables, not just one: table inventory with row counts, PK uniqueness, date range per table, date alignment across tables, column completeness, foreign-key join-rate matrix, relationship diagram, and a thread-to-pull opening hypothesis. Use this skill whenever the user asks a **dataset-wide open question** — "tell me about this data", "tell me about the data", "tell me about this dataset", "what's in here", "what's in this data", "give me an overview", "give me the map", "map out the data", "what do I have", "what does this data look like", "show me what we've got", or any broad first-contact question about the active dataset as a whole (not scoped to one table). This skill is the curriculum payoff moment — students who just connected data get cross-table health, relationship mapping, and date alignment on the first broad question, not a schema dump and

Quick, interactive data exploration without the full pipeline. Use this skill whenever a user wants to explore data, understand what's in a dataset, browse tables or columns, check data distributions, spot patterns, or get familiar with data before analysis. Trigger on phrases like "/explore", "let me explore the data", "what's in this dataset?", "show me what data we have", "browse the data", "what tables are available?", "what columns does X have?", "show me a sample of the data", "what does the distribution look like?", "explore this table", or any request to understand data structure, contents, or patterns before diving into formal analysis. This is especially important after connecting a new dataset or when the user wants to poke around without committing to a specific analytical question yet. DISAMBIGUATION: this is interactive poke-around within an already-known dataset. For a first-contact dataset-wide overview ("tell me about this data"), use `data-map`; for a plain schema/structure listing, use `/da

Takes a vague analytical hunch, stakeholder request, or business question and produces a rigorous, stakeholder-ready analysis plan through a multi-stage pipeline. Use this skill whenever someone says "I think X caused Y", "help me investigate...", "design an analysis for...", "can you look into why...", "we need to figure out what's driving...", or presents any analytical question that needs methodological rigor before execution. This skill is essential when a PM has a hunch but no plan, when an analysis needs redesign after stakeholder feedback, or before starting any major investigation to prevent wasted work. The skill orchestrates hypothesis sharpening → confound scanning → investigation planning → V1 execution → feedback synthesis → V2 redesign. Apply this whenever the user is moving from vague intuition to structured investigation, when they're about to dive into data without a clear plan, when they need to validate an analytical approach before running queries, or when they're iterating on findings aft

Produce an Analysis Design Spec before running any data query or analysis. Use this skill at the start of EVERY analytical request — whether it's "analyze why conversion dropped", "investigate this metric", "compare segments", "what was X last week", or any question involving data. This skill prevents wasted work by defining the question, decision, data needs, dimensions, time range, output format, and success criteria upfront. Apply even when users say "quick question" or "just pull this number" — the spec ensures you understand the decision context before querying. Skip ONLY if the user's request already explicitly states all seven fields (Question, Decision, Data Needed, Dimensions, Time Range, Output Format, Success Criteria) — which virtually never happens. The spec takes 2-5 minutes but prevents hours of rework from scope creep, misaligned analysis, or discovering data gaps mid-stream. When in doubt, use this skill. Trigger on phrases like: analyze, investigate, look into, what happened with, compare, s

Export analysis results in different formats for different audiences — email summaries, Slack updates, decision briefs, Google Docs with embedded charts, Word documents, slide decks, or raw data CSVs. Use this skill whenever someone says `/export`, "export this as...", "send this to...", "share this analysis", "create a Google Doc", "make a Word document", "I need this as a deck", "export the data", "write an email summary", "draft a Slack update", "create a brief", or mentions needing analysis outputs in a specific format for stakeholders. Also trigger after completing an analysis when the user wants to share results, when they mention audience needs (VP, team channel, product team), or when they want multiple output formats at once. This skill handles the full export workflow — finding source material, generating the requested format with proper structure, managing Google Workspace auth and uploads, version tracking for re-exports, and fallback handling when external services fail.

Orchestrate the full 4-layer validation stack plus confidence scoring to produce a comprehensive data quality assessment for any analysis output. This skill applies automatically after analysis agents produce findings and before the Storytelling agent runs. It runs structural validation (schema, primary keys, referential integrity, completeness), logical validation (aggregation consistency, trend continuity, segment exhaustiveness, temporal consistency), business rules validation (range checks, rate validation, YoY change plausibility), and Simpson's paradox detection. The skill produces a confidence score (A-F grade) that appears in executive summaries and validation reports. Use this skill whenever you need to validate analysis findings, check data quality, assess confidence in results, verify calculations, or answer questions like "how confident should I be in these results?", "are these findings valid?", "can we trust this analysis?", "what's the quality of this data?", "validate my analysis", "check my w

North Star Metric lifecycle coach. Helps product managers design, audit, defend, diagnose, and evolve their team's strategic anchor metric across the full 6-month NSM journey. Cited, calibrated, stateful — remembers the product across sessions and grounds every claim in the Amplitude playbook + curated case-book. Use this skill when a PM says "we need an NSM", "is X a good north star", "audit our metric", "our NSM hasn't moved", "how do I push back on the CEO's NSM choice", "should we change our north star". Trigger on phrases like "north star metric", "NSM", "anchor metric", "primary success metric", "guiding metric", "team's main metric". Composes with /metric-spec (NSM is a metric), /metrics (NSM written to the same dictionary), /guardrails (countervailing metrics are guardrails), /tracking-gaps (measurability check), /question-router (NL intent routing). Honors /pace, /knowledge-bootstrap, /session-handoff.

Classify incoming analytical questions into complexity levels (L1-L5) and route them to the appropriate response path. This skill ensures that simple questions get quick answers without unnecessary overhead, while complex investigations get the full analytical treatment they deserve. Use this skill at the start of EVERY user interaction that involves data analysis, metrics, business questions, or investigative requests. Trigger on phrases like "analyze", "why did", "what's happening with", "how many", "compare", "show me", "breakdown", "investigate", "root cause", "size the opportunity", "design an experiment", "create a deck", "run the pipeline", or ANY question that asks about data, metrics, trends, segments, funnels, user behavior, revenue, conversion, retention, or any other analytical topic. Also apply when users ask follow-up questions mid-analysis, when they request charts or visualizations, when they mention datasets or tables, when they ask about business performance, when they want to understand why

Presents a menu of available visual themes when the user asks for a chart, plot, graph, or any visualization interactively and has not already chosen a look. Trigger on requests like "make a bar chart", "plot this", "chart revenue by category", "visualize this", "show me a graph". Skip the menu when the user named a theme, a session default theme is set, or the chart is being generated inside a pipeline run.

List all connected datasets with their status, table counts, and last analysis date. Use this skill whenever the user invokes `/datasets`, or asks questions like "what datasets do I have?", "show me my data sources", "list datasets", "which datasets are connected?", "what data is available?", "show all datasets", "what datasets can I analyze?", "view my datasets", "what data sources are set up?", or any request to see what datasets exist in the system. Also trigger when users mention "switch dataset", "change dataset", "use a different dataset", or "what's my active dataset?" since seeing the list helps them choose. This is a foundational command that should be offered proactively whenever users seem unsure about what data they're working with or when they need to understand what datasets are available before starting analysis.

Generate teaching visuals for analytics and statistics concepts on demand. Each topic produces a small, opinionated set of charts that illustrate one intuition clearly — not a generic explainer, but the specific picture that makes the idea click. Use this skill whenever the user invokes `/teach <topic>`, says "teach me X", "show me the intuition for X", "make the chart that explains X", or asks for visuals to walk someone else through a concept. Trigger on phrases like "/teach", "teach signal vs noise", "explain variance with a chart", "show the intuition", "make a teaching chart", "I want to walk through X with a picture". Topics are extensible — each lives under `topics/` as a single Python script that renders to `outputs/charts/teach/<topic>/`.

Change how visibly Claude surfaces analytical work during L3+ analyses. Three modes — guided (announce each phase and pause for /continue), narrated (announce each phase and run end-to-end), autopilot (silent end-to-end, final output only). Use this skill whenever the user invokes `/pace`, `/pace guided`, `/pace narrated`, `/pace autopilot`, or says things like "slow down and walk me through", "pause between steps", "just run it silently", "narrate each step", "don't stop to ask me", "stop narrating". Pace is orthogonal to complexity level — it changes surfacing, not which agents run. Full spec and phase-banner format live in the question-router skill. This skill is the write-side: it persists the user's explicit pace choice to `working/session_state.yaml` so it survives across phases, across `/resume-pipeline`, and across sessions.

Save completed analyses to the knowledge system's analysis archive for future reference. Use this skill after completing any L3+ analysis, when `/run-pipeline` completes, when the user explicitly says "save this analysis" or "archive this", or automatically at the end of Step 18 (Close the Loop) in the analysis workflow. This skill captures key findings, metrics used, agents invoked, and output file paths so past work can be referenced in future sessions. Trigger whenever you finish validation on a multi-step analysis, complete an analytical deck, wrap up a root cause investigation, finish an opportunity sizing exercise, or close any analysis that produced deliverables worth preserving. Also apply when the user mentions saving work, archiving results, preserving findings, or wants to ensure an analysis can be recalled later. This is your analytical memory system — use it proactively to build institutional knowledge.

Compare metrics, findings, and patterns across two or more connected datasets. Helps identify cross-dataset patterns (e.g., "conversion funnel behavior is similar across both product lines") and dataset-specific anomalies. Use this skill whenever the user wants to compare datasets, mentions phrases like "compare datasets", "is this pattern the same across", "how does X differ between datasets", "cross-dataset comparison", "analyze across product lines", "compare metrics between", or asks whether a finding is unique to one dataset or appears universally. Also trigger when the user has analyzed multiple datasets sequentially and might benefit from seeing commonalities and divergences side-by-side. If you've just finished analyzing dataset A and the user switches to dataset B to run similar queries, proactively offer to compare the two. This skill is valuable for identifying where business patterns are universal vs. dataset-specific, spotting metric definition inconsistencies, and finding opportunities to apply

Auto-generate a structured experiment brief when a user expresses intent to test something. Use this skill whenever users mention testing, experiments, A/B tests, or want to validate a product change with data. Trigger on phrases like "I want to test...", "Let's experiment with...", "Should we A/B test...", "Can we test whether...", "I think X will improve Y", "We should validate...", "Let's try...", or any request involving running an experiment or measuring the impact of a change. This skill is your "think before you design" safety net — it forces clarity on hypothesis, metrics, guardrails, success criteria, and feasibility BEFORE any statistical design work begins. Apply this skill automatically when experiment intent is detected, even if the user hasn't explicitly asked for a brief. The skill ensures every experiment starts with a clear hypothesis (what change, what metric, what mechanism), exactly ONE north star metric (force a choice if multiple metrics are mentioned), guardrail metrics (what must not b

Browse and search past analyses from the knowledge system's analysis archive. Use this skill whenever the user invokes `/history` or variants like `/history search=X`, `/history {id}`, `/history --all`, `/history dataset=X`, or asks questions about their analytical work history such as "what have I analyzed before?", "what analyses have I run?", "show me past work", "show my history", "what have we looked at?", "what questions have I answered?", "can I see my analysis history?", "list my analyses", "what did I work on last week?", "have I analyzed X before?", "find analyses about Y", or mentions reviewing prior analyses, checking if similar work was already done, needing context on previous findings, or wants to build on past work. This skill also applies automatically at session start when you need context on the user's analytical history to inform current work. When displaying history, ALWAYS filter to the active dataset unless the user explicitly requests --all or dataset={id}.

Browse, inspect, compare, and clean up past pipeline runs. This skill is your window into analysis history — every `/run-pipeline` execution creates a tracked run with its own working directory, outputs, and state. Use this skill whenever the user wants to see what analyses have been executed, review past work, compare different approaches to the same problem, or clean up old runs. Trigger on phrases like "/runs", "show me my past analyses", "what analyses have I run?", "list my pipeline runs", "show run history", "what did we analyze last week?", "compare these two analyses", "clean up old runs", "show me the latest run", "what was that analysis we did on [dataset]?", "review past work", "show me completed analyses", or any mention of viewing, listing, comparing, or managing previous pipeline executions. Also trigger when users reference a specific analysis by date or dataset name and want to see its details. This skill provides critical context for iterative analytical work — users often need to revisit, co

Assess whether the data needed for an analysis actually exists, identify what's missing, and produce prioritized instrumentation requests for engineering when gaps are found. Use this skill whenever you're about to start an analysis, when a user asks about data availability, when you need to check if certain events or properties are tracked, when the Data Explorer agent finds incomplete data, when you're designing an analysis and need to verify data exists, when initial queries return nulls or missing values, when a user mentions "do we track...", "is there data for...", "can we measure...", when you're writing an instrumentation request, when you need to assess data completeness, when planning metrics or experiments that require specific tracking, when a user wants to know what data gaps exist, or any time you need to map analytical requirements to available data sources. This skill should be applied proactively before committing to an analytical approach to avoid wasted work on analyses that can't be comple

Automatically detect Sample Ratio Mismatch (SRM) in experiment or A/B test data before any analysis proceeds. SRM is a critical randomization integrity check — if the treatment/control split deviates significantly from the expected ratio, the experiment is compromised and results cannot be trusted. This skill acts as a safety gate that blocks analysis when randomization is broken. Use this skill whenever you detect experiment or A/B test data — look for columns like "variant", "group", "treatment", "control", "arm", "experiment_group", "test_group", "bucket", "condition", or any column with binary/small-cardinality values that suggest treatment assignment. Auto-fire on experiment data detection without waiting to be asked. Apply this skill when loading any experiment dataset, before calculating treatment effects, when starting any experiment analysis workflow, when users mention "A/B test", "experiment", "treatment vs control", "randomization", "test group", or when you see data that looks like it came from

Profile the statistical distribution of any data column and produce a complete analytical playbook — distribution identification, valid summary statistics, recommended tests, A/B testing guidance, and common traps to avoid. Use this skill whenever the user wants to understand a metric's distribution, check statistical assumptions, profile data before analysis, or figure out the right test for a metric. Trigger on phrases like "profile this column", "what distribution is this", "check the distribution", "is this normal", "what test should I use", "understand this metric", "distribution check", "data profiling", "statistical assumptions", "check assumptions before A/B test", "how is this data distributed", "profile the data", or any request to understand the shape/distribution of a numeric column before or during analysis. Also trigger proactively at the start of any analysis involving numeric metrics — profiling the distribution first prevents the #1 mistake in product analytics: using the wrong test because

Guided wizard to connect a new dataset to the AI Analyst system. Use this skill whenever the user wants to add a new data source, connect a database, set up data access, or configure a new dataset for analysis. This skill handles the full connection workflow: choosing connection type (CSV, DuckDB, MotherDuck, PostgreSQL, BigQuery, Snowflake), collecting credentials, validating connectivity, profiling schema, and setting up the knowledge brain. Trigger this skill when users say things like "/connect-data", "connect my database", "add a new dataset", "set up my data", "I have a database I want to analyze", "can you connect to my Postgres/BigQuery/Snowflake", "I need to add CSV files", "how do I get my data into this system", or "connect to my warehouse". Also trigger after first-run welcome when users need to set up their first dataset, or after /switch-dataset when the target dataset doesn't exist yet. This is the primary entry point for all new data connections — always offer this when users mention having da

Create properly formatted Google Docs via the MCP API. This skill prevents common issues like text/image overlap, broken heading hierarchy, excessive whitespace, and inconsistent formatting. Use this skill automatically whenever you're building a Google Doc, calling any google-docs MCP tool (create_document, append_text, write_formatted_content, insert_image, upload_file_to_drive, etc.), designing a document structure, or when the google-doc-creator or google-doc-reviewer agent is running. This skill is essential for ANY workflow involving Google Docs creation, document formatting, analysis writeups in Google Docs, report generation to Docs, chart embedding in documents, or exporting analysis results to shareable Docs. Make sure to use this skill whenever the user wants to create a Doc, export to Google Docs, share analysis as a Doc, build a formatted document, or mentions Google Docs in any capacity.

Create properly formatted Google Slides presentations via the MCP API. Use this skill whenever you're building Google Slides decks, calling batch_update_presentation, designing slide layouts, or the google-slides-creator or google-slides-reviewer agents are running. This skill prevents common API errors (zero-weight outlines, short object IDs, oversized batches, text overflow) and ensures professional deck design with consistent brand styling, proper spacing, and no overlapping elements. Apply this automatically when any MCP Google Slides API calls are being made, when converting Marp decks to Google Slides, when creating presentation decks for analysis results, when building slide decks for stakeholders, or any time the user mentions "Google Slides", "export to slides", "create a presentation", or "share as slides". The skill provides a complete design system (color palette, layout zones, font hierarchy) and pre-validated slide type recipes (title slide, section divider, header + bullets, KPI cards, two-colu

Introduce yourself to the AI Analyst Lab community on Slack. Posts a natural, excited intro message to #introductions from the user's own Slack account. Use this skill whenever the user mentions introducing themselves, joining the community, posting to Slack, saying hello to the group, or getting started. Trigger on phrases like "/kickoff", "introduce myself", "kick it off", "say hi to everyone", "post to the community", "join the Slack", "meet the group", "introduce me", "first post", "onboarding", "getting started", or any mention of introducing themselves to others in the AI Analyst Lab community. This is typically the FIRST interactive step after setup — if the user has just connected their data and is ready to engage with the community, this is the natural next step. Don't wait for them to explicitly say "/kickoff" — if they mention wanting to connect with others, get involved, or participate in the community, this is the right skill to use.

Export analysis results to a Notion page with proper structure, embedded charts, data stamps, and provenance toggle blocks. Use this skill whenever the user says "/export notion", "export to Notion", "create a Notion page", "share this in Notion", "put this in Notion", "send to Notion", or mentions wanting analysis outputs in their Notion workspace. Also trigger when the user mentions their Analysis Gallery or wants to add analysis results to a Notion database. This skill handles Notion MCP authentication, page structure decisions (toggle blocks vs H3 fallback), chart image hosting, provenance embedding, and Analysis Gallery database integration. The skill auto-detects whether toggle blocks are supported and falls back gracefully.

Resume an interrupted or paused analysis pipeline and pick up where you left off. Use this skill whenever the user invokes `/resume-pipeline`, or when they say things like "continue my analysis", "pick up where we left off", "resume the pipeline", "finish the analysis from yesterday", "I need to continue that analysis", "the pipeline stopped, let's continue", "keep going with the analysis", "I had to stop mid-analysis", "let's resume from where we were", or any indication they want to restart work on a partially completed pipeline run. This skill is CRITICAL when a pipeline was interrupted due to context limits, user breaks, connection issues, or agent failures that have since been fixed. Make sure to use this skill proactively when you detect existing pipeline state files in `working/latest/`, `working/runs/`, or `working/pipeline_state.json` — even if the user doesn't explicitly say "resume", if they're asking about a previous analysis or want to continue work and there's pipeline state present, trigger thi

Execute the complete end-to-end analysis pipeline — from raw data and business question to validated slide deck with charts. This is your power tool for delivering full analyses fast. Use this skill whenever the user wants to run a comprehensive analysis workflow, needs a complete deck with insights and recommendations, says things like "/run-pipeline", "run the full pipeline", "analyze this end-to-end", "take this through the full workflow", "I need a complete analysis", "build me a deck", "investigate this data thoroughly", "do the full analytical treatment", or when you detect a complex analytical request (L4-L5 from question-router) that requires multiple phases. This skill manages the entire DAG-based execution engine: pre-flight validation, per-run directory isolation, dynamic dependency resolution, parallel agent execution, checkpoint gates, chart fan-out, automated validation loops, Marp linting, PDF/HTML export, metric capture, and analysis archival. It handles crash recovery, timeout management, cir

Share what you've built with the AI Analyst Lab community on Slack. Use this skill whenever the user mentions sharing their work, showing what they built, posting to the community, celebrating progress, or demonstrating their project. Trigger on phrases like "/show-off", "share what I built", "show the community", "post my work", "show off my project", "share this with the team", "let people see what I made", "I want to share", "can we post this", "show this to others", or "tell people about this". Also trigger proactively after the user completes a significant milestone like finishing an analysis pipeline, building custom agents or skills, or creating visualization helpers — suggest they share it with the community even if they don't explicitly ask. This skill handles the full workflow: detects what the user actually built (vs what was in the template), creates beautiful ASCII architecture diagrams, writes first-person narratives, confirms before posting, and publishes to #show-and-tell on Slack. The skill a

Run the multi-persona planning methodology to produce a master plan for a new project or feature. This skill orchestrates 3-5 expert personas through a rigorous 5-phase planning workflow: scope definition → independent plans → debate & critique → revised plans → synthesis into a master plan with wave structure, task specs, dependencies, and build tracker. Use this skill whenever the user wants to plan a new project, design a feature implementation, architect a system, create a build roadmap, structure a complex initiative, or needs a multi-perspective planning approach. Trigger on phrases like "architect", "plan this project", "design the implementation", "how should we build", "create a master plan", "multi-persona planning", "planning methodology", or when the user invokes `/architect` explicitly. Also trigger when users mention needing different expert perspectives on a project, when they want a structured approach to planning something complex, when they reference waves or phases of work, when discussin

Verify Google Workspace MCP authentication at the start of any session that needs Google APIs (Docs, Slides, Drive). This skill prevents auth failures mid-workflow by testing credentials upfront. Use this skill automatically at session start when the task involves Google Docs, Google Slides, Drive uploads, or any MCP Google Workspace tool. Also trigger when users mention "Google Doc", "Google Slides", "upload to Drive", "create a deck", "export to Google", "share on Drive", or any Google-related output format. Apply before running any Google Workspace agents (google-slides-creator, google-slides-reviewer) or calling any mcp__google-* tool. This skill detects the actual MCP configuration, checks stored credentials in all known locations, tests tokens with a lightweight API call using create operations instead of reads, handles re-authentication if needed, and reports auth status clearly so downstream work can proceed safely or fail fast with actionable guidance.

Browse, search, and explore your organization's business context system — glossary terms, product catalog, metric definitions, OKRs/objectives, and team structure. This skill provides interactive access to all business knowledge stored in `.knowledge/organizations/`. Use this skill whenever the user wants to understand business terminology, look up metric definitions, explore what products exist, review company objectives, understand team structure, search for business terms, or generally wants to know "what business context do we have?" Trigger on phrases like "/business", "show me our glossary", "what metrics are defined?", "what products do we have?", "show me our OKRs", "who's on which team?", "look up [business term]", "search for [term] in our business knowledge", "what's the definition of [term]?", "show me our business context", "browse our company knowledge", "what teams do we have?", "find [term] in the knowledge base", or any request to view or search organizational knowledge. This skill is especia

Standardized workflow for uploading local chart PNGs to Google Drive and making them available for insertion into Google Docs and Slides. Use this skill whenever you need to insert charts into Google Docs or Google Slides, when the google-doc-creator or google-slides-creator agents need chart URLs, when building presentations or reports with embedded visualizations, when user asks to "upload charts", "add images to the doc", "put charts in slides", "make charts available for Google", or any time you have chart PNG files that need to be referenced in Google Workspace documents. This skill eliminates the repeated boilerplate of tmpfiles.org upload + Drive save + permissions + URL construction. Always use this skill before calling insert_doc_image or createImage APIs — those require public Drive URLs, which this skill produces. Also use when you see errors like "image URL not accessible" or "permission denied" during doc/slide creation — those usually mean charts weren't properly uploaded to Drive first.