From loopify-requirements
Convert prompts, vague tasks, product requirements, coding requests, research plans, monitors, reminders, and agent workflows into repeatable, verifiable loops. Use when the user asks to "loopify" a requirement, turn a prompt into an agent loop, design unattended or recurring work, define /loop or routine instructions, create guardrails for autonomous agents, convert one-shot tasks into monitored workflows, or make future prompts more reliable through goal/scope/verification/escalation/stop conditions.
How this skill is triggered — by the user, by Claude, or both
Slash command
/loopify-requirements:loopify-requirementsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Turn a request into a loop contract: a bounded process that can wake up, inspect state, act, verify, report, and stop. Optimize for reliability before autonomy.
Turn a request into a loop contract: a bounded process that can wake up, inspect state, act, verify, report, and stop. Optimize for reliability before autonomy.
A requirement is not loop-ready until it answers:
If any answer is missing, produce a Not Loop-Ready section and fill the smallest safe next step.
Classify the request
one_shot: solve once; loopification should produce a checklist or reusable prompt.scheduled: run on a cadence such as hourly, daily, weekly.event_driven: run on PR, alert, webhook, email, file change, or user message.monitor: watch for a condition and report or act.long_horizon: split work into iterations with state carryover.Pick the autonomy level
L0: human-run checklist.L1: reusable prompt for manual runs.L2: supervised loop that proposes actions.L3: scheduled loop with read-only or low-risk writes.L4: event/API/GitHub-triggered routine with bounded writes.L5: autonomous write/merge/publish. Use only when verification, rollback, and escalation are explicit.Default to the lowest level that solves the user’s problem.
Write the Loop Contract Use the user’s words, but convert vague wishes into observable state, allowed actions, and success checks. Separate facts, assumptions, open questions, and unsafe automation.
Rewrite the prompt Produce instructions an agent can run repeatedly. Include:
Design rollout Start with a small supervised loop. Promote only after the loop has clean logs, stable verification, and no unresolved high-risk failures.
Use this structure unless the user asks for code or implementation:
## Loopified Requirement
- Goal:
- Trigger:
- State:
- Scope:
- Autonomy level:
## Loop Contract
- Allowed actions:
- Forbidden actions:
- Verification:
- Escalation:
- Stop conditions:
- Audit log:
## Loop Prompt
```text
...
```
## Rollout
- First safe run:
- Promotion gate:
- Failure drill:
For implementation work, add the actual automation artifact after the contract: cron command, Claude Code /loop prompt, routine prompt, GitHub Action, monitor config, or script.
references/loop-patterns.md when choosing loop types, guardrails, or failure modes.scripts/loopify.py for a deterministic scaffold from a raw prompt before refining it with task-specific evidence.npx claudepluginhub sunflowerslwtech/sunkit --plugin loopify-requirementsInterview-driven loop-engineering architect. Grills the user one question at a time about a task they want to automate, diagnoses where it sits on the maturity ladder (manual run → codified skill → automated → self-improving loop), forces the success-criteria decision through a 5-tier verification ladder, then BUILDS the complete runnable loop construct for their task right then: execution skill, trigger config, state store, verification block, stop rule, and README — with a validation gate baked in as the first run when the task is unproven. Then it offers a test run and, once they're happy, asks whether to wire it up to run on its own — cron, a Claude Code routine, an OS scheduled task, or left manual — and sets up that trigger so the loop is actually live and hands-off, not just a folder of files. Use this whenever the user wants to "loop engineer" something, build a self-improving / agentic loop, set up a recurring AI task that gets better over time, turn a manual workflow into an automated loop, build a Ralph-style loop, or asks "should this be a loop?" / "how do I automate this with Claude so it improves itself?". Also trigger when the user describes a repeated task (daily report, content generation, perf optimization, scraping, triage) and wonders how to make Claude do it automatically and learn from each run. Crucially, this skill is willing to tell the user a task should NOT be a loop — it protects them from burning tokens on a loop with no real success criteria. NOT for one-off tasks (just do the task), and NOT for reviewing already-built loops.
Designs and scaffolds unattended, scheduled, self-verifying agent workflows (loops) with schedule, isolation, skill, connectors, verifier, and state building blocks.
Find, compare, adapt, and design bounded AI-agent feedback loops with explicit checks, stop rules, guardrails, and handoffs.