optimization-load-balancer
Dynamic task distribution, work-stealing algorithms, queue management, and adaptive load balancing. Use for optimizing task execution across agents, preventing overload, and maximizing throughput.
implement-feature
Implement features from task specifications using Test-Driven Development (TDD) with bmad-commands for file operations and testing. This skill should be used when implementing new functionality from approved task specs.
executing-plans
Use when implementing a multi-step plan. Execute systematically with verification checkpoints to catch errors early.
jira-ticket
Read Jira tickets, extract requirements, summarize in developer-friendly format, optionally search codebase for related files, and update ticket status with transitions
context-synchronization
Synchronize project documentation after track completion. Use when a track reaches completed status and product, tech-stack, or guidelines documents may need updates.
triple-dash-body
A skill with triple dashes in the markdown body
project-standards
Estándares de calidad, naming conventions y Definition of Done para skills y proyectos. Se aplica siempre como base de cualquier trabajo.
goal-alignment
QUANTITATIVE skill for validating wave deliverables against goal milestones using 0-100% alignment scoring. Prevents scope drift, detects misalignment, enforces goal-wave consistency. Requires goal-management for milestone data. Essential for multi-wave projects to maintain North Star alignment throughout execution.
subagent-driven-development
Use when executing implementation plans with independent tasks in the current session
execute-task
Execute approved task specifications sequentially with TDD, comprehensive testing, and validation. This skill should be used for implementing tasks from approved specs with full audit trail.
enterprise-readiness
Assess and enhance software projects for enterprise-grade security, quality, and automation. This skill should be used when evaluating projects for production readiness, implementing supply chain security (SLSA, signing, SBOMs), hardening CI/CD pipelines, establishing quality gates, reviewing code or PRs, writing documentation (ADRs, changelogs, migration guides), or pursuing OpenSSF Best Practices Badge. Aligned with OpenSSF Scorecard, Best Practices Badge (all levels), SLSA, and S2C2F. By Netresearch.
long-horizon-holdout
Use when running experiments on platforms where user value compounds over time, when growth teams claim credit for revenue that would have happened anyway, or when short-term wins don't translate to long-term value
github-issue-triage
Analyze GitHub issues for the Nx repository and provide assignment recommendations based on technology stack, team expertise, and priority classification rules.
bmad-integration
Integracao com BMAD Method para escala adaptativa e workflows guiados. Detecta nivel de complexidade e ajusta agentes automaticamente. Use quando: iniciar workflow, detectar complexidade, mapear agentes BMAD.
estimate-epic-timeline
Estimate Epic timeline by summing all Task points. Use when (1) tasks.md 생성 후, (2) Epic 전체 일정 예측 필요, (3) 병렬/순차 작업 및 Critical Path 분석.
serena-file-processing
Master instructions for using Serena MCP tools for code navigation, search, navigation, and editing. Includes rules for terminal usage and memory management.
house-operational-architecture
A three-part structural framework for building a scalable company—Foundation (Founding Documents), Posts & Beams (Supporting Structures), and Mechanicals (Operating Cadence). Use when moving from PMF to scaling the org.
problem-solving-techniques
Apply systematic problem-solving techniques when stuck. Use for complexity spirals, innovation blocks, recurring patterns, assumption constraints, simplification cascades, scale uncertainty.
get-available-resources
This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
