Agent Skills Directory

Generate a weighted sales forecast with best/likely/worst scenarios, commit vs. upside breakdown, and gap analysis. Use when preparing a quarterly forecast call, assessing gap-to-quota from a pipeline CSV, deciding which deals to commit vs. call upside, or checking pipeline coverage against your number.

Analyze pipeline health — prioritize deals, flag risks, get a weekly action plan. Use when running a weekly pipeline review, deciding which deals to focus on this week, spotting stale or stuck opportunities, auditing for hygiene issues like bad close dates, or identifying single-threaded deals.

Send USDC to an Ethereum address or ENS name. Use when you or the user want to send money, pay someone, transfer USDC, tip, donate, or send funds to a wallet address or .eth name. Covers phrases like "send $5 to", "pay 0x...", or "transfer to vitalik.eth".

Analyzes sales pipeline health, revenue forecasting accuracy, and go-to-market efficiency metrics for SaaS revenue optimization. Use when analyzing sales pipeline coverage, forecasting revenue, evaluating go-to-market performance, reviewing sales metrics, assessing pipeline analysis, tracking forecast accuracy with MAPE, calculating GTM efficiency, or measuring sales efficiency and unit economics for SaaS teams.

Support SOX 404 compliance with control testing methodology, sample selection, and documentation standards. Use when generating testing workpapers, selecting audit samples, classifying control deficiencies, or preparing for internal or external audits.

Build an interactive HTML dashboard with charts, filters, and tables. Use when creating an executive overview with KPI cards, turning query results into a shareable self-contained report, building a team monitoring snapshot, or needing multiple charts with filters in one browser-openable file.

Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.

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Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.

Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.

Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

QA an analysis before sharing -- methodology, accuracy, and bias checks. Use when reviewing an analysis before a stakeholder presentation, spot-checking calculations and aggregation logic, verifying a SQL query's results look right, or assessing whether conclusions are actually supported by the data.