Agent Skills Directory

Financial leadership for startups and scaling companies. Financial modeling, unit economics, fundraising strategy, cash management, and board financial packages. Use when building financial models, analyzing unit economics, planning fundraising, managing cash runway, preparing board materials, or when user mentions CFO, burn rate, runway, fundraising, unit economics, LTV, CAC, term sheets, or financial strategy.

Swap or trade tokens on Base network. Use when you or the user want to trade, swap, exchange, buy, sell, or convert between tokens like USDC, ETH, and WETH. Covers phrases like "buy ETH", "sell ETH for USDC", "convert USDC to ETH", "get some ETH".

Decompose financial variances into drivers with narrative explanations and waterfall analysis. Use when analyzing budget vs. actual, period-over-period changes, revenue or expense variances, or preparing variance commentary for leadership.

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

Generate SOX sample selections, testing workpapers, and control assessments. Use when planning quarterly or annual SOX 404 testing, pulling a sample for a control (revenue, P2P, ITGC, close), building a testing workpaper template, or evaluating and classifying a control deficiency.

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

Atlassian Template and Files Creator/Modifier expert for creating, modifying, and managing Jira and Confluence templates, blueprints, custom layouts, reusable components, and standardized content structures. Use when building org-wide templates, custom blueprints, page layouts, and automated content generation.

/em -postmortem — Honest Analysis of What Went Wrong

Atlassian Confluence expert for creating and managing spaces, knowledge bases, and documentation. Configures space permissions and hierarchies, creates page templates with macros, sets up documentation taxonomies, designs page layouts, and manages content governance. Use when users need to build or restructure a Confluence space, design page hierarchies with permission structures, author or standardise documentation templates, embed Jira reports in pages, run knowledge base audits, or establish documentation standards and collaborative workflows.

Atlassian Jira expert for creating and managing projects, planning, product discovery, JQL queries, workflows, custom fields, automation, reporting, and all Jira features. Use for Jira project setup, configuration, advanced search, dashboard creation, workflow design, and technical Jira operations.

Two-layer memory architecture for board meeting decisions. Manages raw transcripts (Layer 1) and approved decisions (Layer 2). Use when logging decisions after a board meeting, reviewing past decisions with /cs:decisions, or checking overdue action items with /cs:review. Invoked automatically by the board-meeting skill after Phase 5 founder approval.

World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.