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Found 69 Skills
Diagnose why an existing sales forecast is unreliable and recommend fixes - stage inflation and happy ears, sandbagging, deals without verifiable buyer evidence, stale or mass-pushed close dates, wrong forecast-category assignment, zombie and duplicate opportunities, late-created deals, manager roll-up overrides, and comp incentives that reward bias - separating data-quality from behavioral from genuine demand problems. Use whenever the user mentions forecast accuracy, a forecast miss, reps sandbagging, deals that keep slipping, "why did we miss the number", or "our commit is never right" - even if they never say "forecast". Covers B2B deal-based and B2C/high-volume. Do NOT use for pipeline coverage modeling - use mbfinotti/sales-skills@sales-pipeline-coverage-modeling instead.
Create and adapt Bolt Pipeliner ETL projects using Spark, Pandas, or Polars. Use whenever a user asks to create an ETL pipeline, data lake pipeline, medallion architecture, Airflow pipeline, data-quality workflow, or Bolt Pipeliner project.
Convert a complex Excel/xlsx table into validated, schema-backed JSON. Use when the user wants to parse a spreadsheet into JSON, create or validate a JSON Schema for tabular data, convert an Excel table that has merged cells or hierarchical / multi-level rows, or run any stage of the pipeline (inspect, schema, convert, validate, data-quality review). Triggers on phrases like "convert this Excel to JSON", "parse the xlsx", "make a JSON schema for this table", "validate my JSON instance", "data quality check the conversion".
Take one or many customer-interview files you already have and extract the AJTBD structure from them — segments by Core Jobs, personas, Consideration Set, existing Solutions and Problems, value hypotheses — using Ivan Zamesin's AJTBD / Next Move Theory methodology (distinct from generic Christensen JTBD). Input — a folder or list of files: deep-interview transcripts, interview notes, sales-call or demo transcripts, support/chat logs, survey open-ends. The interviews may be AJTBD or not, well- or poorly-conducted, one file or dozens. The skill first asks which business task you're solving (and helps you choose if you can't name one), then reads each interview in its own subagent (a fan-out so it never overflows context, no matter how many large transcripts), extracts the Core Jobs with an honest per-interview confidence (a clean extraction vs. a weak hypothesis), gives per- interview feedback (what was pulled, what's missing, whether this interview can even serve your business task), clusters the extractions into segments by similar Core Jobs + similar success criteria + similar priority order, and computes each segment's confidence from the supporting interviews' confidence. Output — one report: a data-quality summary, segments by Core Jobs with personas and confidence, structured existing Solutions and Problems, a Consideration Set per segment, value-creation hypotheses, and a gap list of what to interview next. Use when the user says "analyze my interviews", "extract jobs from these transcripts", "I have customer interviews — find the segments", "what jobs are in these calls", "synthesize my interviews", or has interview/transcript files and wants the methodology pulled out of them. The post-fieldwork counterpart to /nmt-interview-guide. Two modes — Quick (default, no internet) and Deep (subagents + web to enrich competitors and the Consideration Set). Plain language; defaults to English.
Review prediction-market, basket, oracle, and trading-agent workflows for compliance, safety, data-quality, privacy, and execution risk. Use before any workflow handles venue auth, user portfolio data, API keys, or trade planning.
When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.
Complete 9-step Clay enrichment workflow for 90%+ data coverage plus 58 Clay templates across 8 categories. Use when building enrichment workflows, setting up Clay tables, or maximizing data quality.
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
Vendor-neutral skill to check a KPI dictionary for conflicting definitions, grain mismatches, and missing ownership.
Use this skill whenever the user mentions IP geolocation feeds, RFC 8805, geofeeds, or wants help creating, tuning, validating, or publishing a self-published IP geolocation feed in CSV format. Intended user audience is a network operator, ISP, mobile carrier, cloud provider, hosting company, IXP, or satellite provider asking about IP geolocation accuracy, or geofeed authoring best practices. Helps create, refine, and improve CSV-format IP geolocation feeds with opinionated recommendations beyond RFC 8805 compliance. Do NOT use for private or internal IP address management — applies only to publicly routable IP addresses.
Data validation using Great Expectations. Expectation suites, checkpoints, and data docs for pipeline monitoring.
Decision-first data analysis with statistical rigor gates. Use when analyzing CSV, JSON, database exports, API responses, logs, or any structured data to support a business decision. Handles: trend analysis, cohort comparison, A/B test evaluation, distribution profiling, anomaly detection. Do NOT use for codebase analysis (use codebase-analyzer), codebase exploration (use explore-pipeline), or ML model training.