Results for “engagement-loops”

Orchestrates bounded, governed iteration loops over existing agent commands and offices, with explicit stopping conditions, checkpoints, and honest terminal states.

Use this skill whenever the user wants to work with the Loops CLI from the terminal. This includes installing or updating the CLI, authenticating, storing and selecting API keys, validating credentials, and running commands for contacts, contact properties, lists, events, transactional email, campaigns, email messages, themes, components, and uploads. Trigger on phrases like "Loops CLI", "loops auth login", "loops campaigns create", "loops uploads create", "loops email-messages update", "loops themes list", "loops components get", "loops contacts create", "loops events send", "loops transactional send", "loops api-key", "loops agent-context", "brew install loops-so/tap/loops", or any time the user wants to use Loops from the shell instead of application code.

Guides users through an interactive tour of the context-matic MCP server, explaining its purpose and demonstrating all available APIs with live tool calls.

Creates a new recurring workstream (loop) in a file-based knowledge base: scaffolds the domain folder, runs a real test cycle, and records the result in the timeline and log.

Design habit-forming product loops using the Hook Model (Trigger, Action, Variable Reward, Investment). Use when the user mentions "users arent coming back", "engagement loops", "habit formation", "push notifications", "variable rewards", "daily active users", "habit zone", or "user retention loops". Also trigger when designing notification strategies, building streaks or progress systems, or analyzing why users stop using a product after initial signup. Covers ethics evaluation and onboarding for habits. For friction reduction and B=MAP, see improve-retention. For viral sharing, see contagious.

Patterns, conventions, and guardrails for the closed-loop systems in AegisNex. Covers the AI intelligence graph, Guardian auto-restart, multi-agent orchestration, incident lifecycle, self-improvement memory, and risk/policy gates.

Run Claude Code autonomously in loops, from simple sequential pipelines to multi-agent DAG orchestration.

Run a prompt or skill in this session on a recurring or variable interval (e.g. /loop 5m /foo).

Discovers, finds, audits, repairs, adapts, crafts, runs, debriefs, saves, and prepares repeatable AI-agent loops for publication, treating loops as bounded feedback systems.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

Discover, audit, repair, adapt, and design bounded, verifiable AI-agent loops with explicit triggers, actions, stopping conditions, and guardrails.

Composite skill — query, capture, improve, and persist knowledge in one workflow. Chains recall (RAG query) → sync-memories (write durable note) → rag-curate (improve weak retrievals) → handoff (durable snapshot if session-ending). Use when the work involves "what did we decide", "remember this", "save where we are", or any closing checkpoint.

Defines scope, objectives, rules of engagement, threat model selection, and operational timelines for red team engagements before any offensive testing begins.

Start an autonomous experiment loop with user-selected interval (10min, 1h, daily, weekly, monthly). Uses CronCreate for scheduling.

Discovers, audits, repairs, adapts, and designs bounded AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs.

Discover, find, compare, audit, repair, adapt, craft, run, debrief, and prepare repeatable AI-agent loops for publication. Use when a user asks to analyze code or coding threads for recurring work, find a published loop, interview them to turn a goal into a bounded loop, review a loop...

Drafts structured goal-loop prompts for long-running agent work with verifiable stop conditions, validation commands, and documentation requirements.

Provides patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.

Discover, find, compare, audit, repair, adapt, craft, run, debrief, and prepare repeatable AI-agent loops for publication. Use when a user asks to analyze code or coding threads for recurring work, find a published loop, interview them to turn a goal into a bounded loop, review a loop...

Show agent flow trace timeline and summary

Discover, find, compare, audit, repair, adapt, craft, run, debrief, and prepare repeatable AI-agent loops for publication. Use when a user asks to analyze code or coding threads for recurring work, find a published loop, interview them to turn a goal into a bounded loop, review a loop...