Results for “recurring-automation”

Discover, compare, audit, repair, adapt, craft, run, debrief, and publish repeatable AI-agent loops for engineering workflows.

Automate repetitive tasks and workflows using scripting, file watchers, scheduled jobs, CI triggers, and API polling to eliminate manual toil. Use when the user requests task automation or provides relevant inputs for this workflow.

When the user wants to set up a recurring, self-running marketing workflow — a repeatable loop an AI agent runs on a cadence (weekly, daily, on a trigger) rather than a one-off task. Also use when the user mentions 'marketing loop,' 'recurring marketing workflow,' 'automate my marketing,' 'marketing on autopilot,' 'weekly marketing review,' 'ad fatigue check,' 'content refresh loop,' 'churn watch,' 'ranking drop alert,' 'always-on marketing,' 'marketing automation workflow,' or 'run this every week.' Use this to pick, adapt, and schedule an ongoing marketing loop that orchestrates the other marketing skills. For one-off marketing ideas, see marketing-ideas. For the experimentation loop specifically, see ab-testing.

Automates repetitive infrastructure and deployment tasks with Terraform, OpenTofu, Ansible, GitHub Actions, and n8n, enforcing safe practices like plan review and confirmation before applying changes.

Define and manage recurring automated agent runs — CI monitoring, dep updates, security scans

This skill should be used when the user asks about "limit order", "stop loss", "DCA", "TWAP", "schedule", "automate", "recurring order", "price trigger", "cancel automation", "my automations", or any automated trading operation. Provides guidance on limit orders, scheduled commands, and automated strategies.

Master the AI tools that automate repetitive work and connect your tools. From simple Zapier integrations to complex AI-powered workflows, reclaim hours every week. Use when "automation, workflow, integrate tools, connect apps, zapier, no-code, automate process, rpa, automation, workflow, integration, no-code, zapier, make, rpa" mentioned.

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

Analyze a screen recording of a manual process and produce targeted, working automation scripts. Extracts frames and audio narration from video files, reconstructs the step-by-step workflow, and proposes automation at multiple complexity levels using tools already installed on the user machine.

Enforce code quality with Ruff in a controlled, iterative workflow: run checks with optional scope and rule overrides, apply safe and unsafe autofixes, review diffs, and resolve remaining findings.

Implements a continual learning loop for AI coding agents using hooks, two-tier memory (global and local), and automatic pattern detection to persist and apply learnings across sessions.

Produces an evidence-backed inventory of live automations (jobs, hooks, connectors, MCP servers, wrappers) and recommends keep/merge/cut/fix-next actions before rewriting anything.

`analysis-agent`/`task-agent`/`review-agent`: use for recurrence guards on known defects, incidents, or escaped failures; skip speculative risk without a prior failure mechanism.

Use when you want the AI to check back periodically for updates or progress

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

Use when work must continue across multiple Codex sessions with `.autonomous/` tracking, resumable execution, or autonomous handoff. Use for long-running, multi-session, or resume-later tasks.

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.

Analyzes repeated agent failures or noisy routing and proposes evidence-backed changes to instructions, skills, gates, or runbooks.

Composite skill — safely refactor a module end-to-end with sequencing, parallel implementation, post-refactor cleanup, and rationale capture. Chains refactor-plan (phased plan + rollback) → three-man-team (architect/builder/reviewer in parallel) → fix-the-suite post-refactor → adr-write → docs-sync. Use for non-trivial refactors that need both careful sequencing and durable record.

通用多轮迭代改进：对任意研究制品反复调用 /review → 解析反馈 → 修复 → 更新 wiki，直到达标

Self-referential loop until task completion with architect verification

Defines, validates, runs, resumes, and audits repo-local multi-step automations using the `asc workflow` CLI and `.asc/workflow.json`, including step outputs and safe release/TestFlight workflows.

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...

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...

Audit existing business processes to identify AI automation opportunities. TRIGGERS - Use when user wants to find AI opportunities, audit processes for automation, or assess AI readiness.

Autonomous evolutionary code improvement engine with tournament selection

自主Claude代码循环的模式与架构——从简单的顺序管道到基于RFC的多智能体有向无环图系统。

`analysis-agent`/`task-agent`/`review-agent`: use when repeated failure needs a new hypothesis or proof path; skip an initial failure with verified cause and a different action.

Designs and implements CI/CD pipelines, infrastructure automation, and workflow optimization to reduce manual tasks.

Use this when the system needs scheduled tasks, cron-style jobs, periodic maintenance, sync loops, cleanup tasks, or non-interactive background automation that is not simply an API request or queue worker.

Autonomous error recovery — detect failures, diagnose root cause, apply fixes, and resume without stopping

Start an autonomous experiment loop with user-selected interval (10min, 1h, daily, weekly, monthly). Uses CronCreate for scheduling. Use when the user runs /ar:loop or asks to run an autoresearch experiment continuously on a schedule.

Identifies operational toil, prioritizes automation by ROI, and provides patterns for self-healing systems and runbook automation.