agent-skills

**Arguments:** $ARGUMENTS

> Reference for: Common Ground

> Reference for: Common Ground

> Reference for: Common Ground

Approve synthesis findings and create implementation tickets from discovery

Create a discovery document for research/customer discovery epics

Synthesize discovery findings into a consolidated analysis document with proposed tickets

**Purpose:** Finalize a ticket after `/execute-ticket` by transitioning Jira and updating the implementation plan.

Execute a Jira ticket following its implementation plan

Create an epic planning document by analyzing Jira tickets and codebase

Generate an implementation plan from a planning document

Complete an epic after all tickets are executed, generate report, and close in Jira

Generate comprehensive sprint retrospective from completed epics

Use when creating or revising academic paper sections, formatting tables/figures for journal submission, writing referee responses, or adapting papers to journal-specific requirements in finance, economics, and real estate research.

Use when investigating errors, analyzing stack traces, or finding root causes of unexpected behavior. Invoke for error investigation, troubleshooting, log analysis, root cause analysis.

Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke for async SQLAlchemy, JWT authentication, WebSockets, OpenAPI documentation.

Use when adding docstrings, creating API documentation, or building documentation sites. Invoke for OpenAPI/Swagger specs, JSDoc, doc portals, tutorials, user guides.

Use when reviewing pull requests, conducting code quality audits, or identifying security vulnerabilities. Invoke for PR reviews, code quality checks, refactoring suggestions.

Use when auditing, optimizing, or architecting the AI agent context layer (CLAUDE.md files, hooks, slash commands, skills, IDE rules) for any codebase, bootstrapping context engineering from scratch, diagnosing agent underperformance, or when the user mentions CLAUDE.md strategy, context quality, agent instructions, or context architecture.

Build Discounted Cash Flow (DCF) valuation models for commercial real estate (CRE). Calculate NOI-based cash flows, levered and unlevered IRR, equity multiple, DSCR, debt yield, exit cap rate reversion, and sensitivity analysis. Use for office, retail, industrial, multifamily, mixed-use, and hotel properties. Trigger on: CRE valuation, property DCF, NOI projection, cap rate analysis, IRR analysis, real estate investment return, acquisition underwriting, hold-period analysis, CRE sensitivity table.

Build discounted cash flow (DCF) valuation models in Excel specifically for commercial real estate (CRE). Use when creating CRE acquisition underwriting models, property-level DCF workbooks, NOI projections, rent roll buildouts, debt schedules, or IRR/equity-multiple return analyses in Excel or ExcelJS. Covers all CRE property types: office, retail, industrial, multifamily, mixed-use, hotel. Trigger on: 'CRE excel model', 'build underwriting model', 'property DCF excel', 'NOI projection spreadsheet', 'rent roll model', 'IRR excel', 'acquisition model', 'CRE waterfall', 'cap rate model'.

Use when analyzing commercial properties, creating investment memorandums, performing DCF/IRR analysis, evaluating REIT investments, or developing CRE business plans with institutional-grade underwriting standards.

Use when you need fine-grained control over every plot element, creating novel plot types, custom visualizations, or integrating with specific scientific workflows requiring matplotlib.

Use when building MCP servers or clients that connect AI systems with external tools and data sources. Invoke for MCP protocol compliance, TypeScript/Python SDKs, resource providers, tool functions.

Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, or managing experiment tracking systems.

Use when building n8n workflows, configuring nodes, setting up triggers, implementing data transformations, or integrating AI agents into automation workflows.

Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation, missing value handling, groupby operations, or performance optimization.

Use when designing prompts for LLMs, optimizing model performance, building evaluation frameworks, or implementing advanced prompting techniques like chain-of-thought, few-shot learning, or structured outputs.

Use when generating publication-quality LaTeX tables and figures from PyFixest econometric models, including regression tables, event study plots, and summary statistics for academic research papers.

Use when creating publication-ready journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Use when the user wants to create, brainstorm, draft, or post social media content for X (Twitter), LinkedIn, or Instagram. This includes generating post ideas, writing captions or threads, adapting content across platforms, planning a posting cadence, or navigating to a platform's posting interface via browser tools. Trigger whenever the user mentions social media, posting, tweets, LinkedIn posts, Instagram captions, content calendars, thought leadership posts, or sharing research/business updates online — even if they just say something like 'I should post about this' or 'help me share this.'

Use when requesting STATA code patterns for empirical accounting research methods including entropy balancing, PSM, DiD, RDD, IV, event studies, survival analysis, or regression specifications.

Use when pulling data from WRDS databases, merging financial datasets via linking tables, validating panel structure, or constructing financial variables for finance and real estate research.