Scientific delegation framework for orchestrators — provide observations and success criteria while preserving agent autonomy. Use when assigning work to sub-agents, before invoking the Agent tool, or when preparing delegation prompts for specialist agents.

Use for Agently runtime extensions: AgentExecution plugins and final review/artifact policies, Actions and MCP, Shell/BashExecutor and PowerShell permissions, ExecutionResource lifecycle, TaskWorkspace, RecordStore, auto_func and KeyWaiter, FastAPIHelper, or optional DevTools observation.

Create a professional paid-advertising strategy covering objectives, economics, platform selection, campaign architecture, audiences, budget, creative, measurement, experiments, governance, rollout, and reporting. Use for ad plan, media plan, PPC strategy, paid-social strategy, campaign architecture, advertising roadmap, or channel planning.

Show session analytics, learning patterns, correction trends, heatmaps, and productivity metrics. Computes stats from project memory and session history. Use when asking for stats, statistics, progress, how am I doing, coding history, or dashboard.

Structured approach to explore features before production development. Templates for objectives, acceptance criteria, architecture, prototypes, and verification. This skill should be used when the user asks to "explore a feature", "start an exploration", "create a task folder", or mentions exploration protocol.

Code review guidelines covering code quality, security, and best practices.

Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.

libingest - Document ingestion pipeline. IngestPipeline orchestrates configurable transformation steps. IngestStep defines individual processors like pdf-to-images, images-to-html, extract-context, annotate-html. Converts PDF, PowerPoint, images to Schema.org annotated HTML. Use for document processing, knowledge extraction, and content transformation.

libagent - Agent orchestration library for conversational AI. AgentMind class coordinates LLM completions, memory management, tool execution, and multi-turn conversations. AgentAction handles tool calls and action processing. Use for building chat agents, RAG pipelines, and AI assistants. Integrates with libmemory, librpc, and libllm

Configure the LaunchDarkly hosted MCP server during onboarding. Use when the parent LaunchDarkly onboarding skill reaches the MCP offer, after the first flag works. Supports Cursor, Claude Code, Windsurf, GitHub Copilot, and other MCP-compatible agents. OAuth authentication; no API keys for the hosted server.

Use when structuring or decomposing any frontend UI into reusable components, regardless of framework. Applies Brad Frost's atomic design methodology — atoms, molecules, organisms, templates, and pages — to guide consistent component hierarchy, naming, and composition decisions.

Plan-spec-implement workflow for structured development. Only use when explicitly directed by user or when mentioned in project AGENTS.md file. Generates ephemeral plans in ~/.dot-agent/, applies specs to project docs, then implements test-first.

Define and implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service performance.

Use when asked to brainstorm, evaluate whether an idea is worth building, run office hours, or think through a new product idea or design direction before any code is written.

Use this skill when adding authentication, handling user input, working with secrets, creating API endpoints, or implementing payment/sensitive features. Provides comprehensive security checklist and patterns.

Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

CI-only self-improvement workflow using gh-aw (GitHub Agentic Workflows). Captures recurring failure patterns and quality signals from pull request checks, emits structured learning candidates, and proposes durable prevention rules without interactive prompts. Use when: you want automated learning capture in CI/headless pipelines.

Monitors context window health during large, long-running, multi-session, or explicitly context-sensitive work. Uses bounded external anchors, one cold-context review when self-recovery is uncertain, and a clean handoff on real drift. Do not auto-activate merely because a medium task has an intent frame and plan.