miguel-conde

Validation procedures for agentic deliverables in the SW-TEAM framework. Loaded by the SW-TEAM Agent QA agent at session startup to validate spec templates (field completeness, placeholder format), SKILL.md files (FIXED/ADAPT contract, Layer A cross-references), .agent.md files (frontmatter, tools, handoff self-containment), and integration artefacts (copilot-instructions.md, KB starters). Use when running Agent QA for any SW-TEAM dev phase or validating a single deliverable type. Load alongside a domain-context SKILL (e.g., {domain}-builder-context) for package name, output folder structure, and the domain validation scenario.

Methodology for producing and reviewing design briefs for any SW-TEAM framework build. Loaded by the SW-TEAM-Architect agent at session startup. Covers design brief structure (Sections A–D), function design rules, SKILL.md scaffold rules, agent prompt architecture rules, design review workflow, and seven non-negotiable design constraints. Domain-agnostic — contains no project-specific vocabulary. Load alongside a domain-context SKILL (e.g., {domain}-builder-context) in the Architect agent.

Full quality gate procedure for any SW-TEAM Python package: ruff format, ruff check, mypy, pytest with coverage. Loaded by the SW-TEAM-Code-QA agent at session startup. Covers the 4-stage gate, test writing conventions, contract verification checklist, and per-module/per-phase reporting formats. Domain-agnostic — contains no project-specific vocabulary. Load alongside a domain-context SKILL (e.g., {domain}-builder-context) for package name, coverage threshold, and test fixtures.

Methodology for building all five SW-TEAM deliverable types from a design brief: {package}/ Python functions (stateless, typed, tested), spec templates (placeholder-driven .md), SKILL.md files (FIXED/ADAPT scaffolds with Layer A calls), .agent.md definitions (frontmatter + system prompt), and integration artefacts. Loaded by the SW-TEAM-Implementer agent at session startup. Domain-agnostic — contains no project-specific vocabulary. Load alongside a domain-context SKILL (e.g., {domain}-builder-context) for package name, fixture shapes, and commit scopes.

Generic orchestration protocol for SW-TEAM-style multi-phase, gated, KB-backed, artefact-logged projects. Covers gate pattern, subagent invocation rules, KB protocol, artefacts.jsonl protocol, loop-back handling, and session startup protocol. Domain-agnostic — contains no project-specific vocabulary. Load alongside a domain-specific SKILL (e.g., {domain}-domain) in every orchestrator agent.

Agentic SCRUM Master workflow for SW-TEAM framework development: sprint state persistence, backlog management across five deliverable types ({package}/ code, spec templates, SKILL.md files, .agent.md files, integration artefacts), dual-track QA coordination (Code QA for Python, Agent QA for agentic deliverables), design-brief gating, cross-phase dependency enforcement, and SCRUM ceremonies adapted for agentic framework development. Use when managing dev-phase progress, planning phases, checking exit criteria, tracking blockers, running retrospectives, coordinating handoffs between Architect/Implementer/Code QA/ Agent QA, or maintaining project health dashboards across sessions — even if "SCRUM" is not mentioned explicitly.

Implement Marketing Mix Models (MMM) using the pymc-marketing Bayesian library. Covers model setup, adstock/saturation transformations, fitting, diagnostics, budget allocation, lift test calibration, causal inference, and model evaluation. Use when building or improving MMM models with pymc-marketing, navigating pymc-marketing documentation, choosing the right MMM notebook or API reference, or answering any pymc-marketing MMM implementation question. Combine with the pymc-modeling skill for advanced Bayesian modeling questions.

Complete workflow for creating Agent Skills following the open standard. Use when building new capabilities, setting up skill directories, or designing progressive loading workflows for VSCode Copilot customization.

Complete VSCode Copilot asset management system with intelligent discovery, installation, and deployment capabilities for agents, prompts, instructions, collections, and skill packages.

Professional book intake process for technical editors. Establishes global context, target audience, technical scope, and chapter coherence before editorial work begins. Use when starting technical editing projects or need to understand book structure.

Expert knowledge in guiding users through agentic technical editing workflows. Provides step-by-step instructions for framework setup, context loading, and systematic editing processes. Use when users need guidance on framework usage, setup procedures, or workflow troubleshooting.

Expert knowledge in Expectation-Maximization algorithms for Multi-Touch Attribution, Marketing Mix Models, and applied Bayesian inference in marketing analytics. Use when discussing EM algorithms, MTA methodology, MMM attribution, latent variable models, or marketing attribution problems.

Create and manage structured JSON planning files for data science workflows. Use when user requests DS projects requiring data contracts, evaluation protocols, and MLOps requirements.

Create and manage structured JSON planning files for complex multi-agent workflows. Use when user requests projects requiring 3+ agents, multiple phases with dependencies, or enterprise-level coordination.

Convert raw chapter notes or AI-generated draft text into polished Quarto .qmd chapter files for this Optuna book, following the syllabus, the book style guide, and the existing chapter conventions. Use this skill when you need to turn raw text into a finished or near-finished Quarto chapter for this repository.