/goaldevopshigh riskadvancedsafety D · 45 · open-endedForward Futurepre-dates current gate · under review

Ship one customer AI priority

Advance a single customer priority into a validated, gradually released system with monitoring, approvals, and outcome evidence.

prompt
→ Claude
Run this when a customer requests an AI workflow, reports a failure, or reaches an operations review. Choose one priority, such as enriching leads, drafting emails, summarizing meetings, or updating a CRM. Define the owner, inputs, approvals, success metric, and ROI hypothesis. Dry-run it on realistic customer data, fix the smallest verified problem, then release through approved stages and monitor production. Finish with the outcome, evidence, customer update, lessons saved, and next review.
claude-code · codex

Use this when

Use this when an AI workflow must live inside a real customer process and needs validation, approval, gradual rollout, monitoring, and a clear business outcome.

How it runs

  1. Review the customer priority, recent feedback, workflow history, failures, approvals, usage, cost, and ROI signals.
  2. Choose one workflow or improvement and define its owner, systems, data, risk, approval gates, success criteria, and ROI hypothesis.
  3. Dry-run it on realistic customer data, repair the smallest underlying issue, and release through controlled stages.
  4. Monitor production, send the customer update, and store reusable preferences, failures, examples, and ROI observations.

Done when

One customer priority reaches a proven terminal state. The workflow reaches its agreed rollout stage, a production issue is fixed, or a blocker is escalated with an owner and next step.

Why it works

The workflow itself is only one part of a real deployment. This loop keeps validation, approval, rollout, monitoring, learning, and accountability tied to one customer priority.

Implementation note

Do not expand rollout when dry-run evidence, approval state, or monitoring is missing. Keep sensitive, irreversible, financial, and customer-facing actions behind explicit human approval.

Source: Forward Future

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Check whether the deploy finished and summarize what changed

/loopnew

A 5-minute watch loop that polls whether a deploy has finished and, once it lands, summarizes what changed. It is a read-only status watcher rather than an agent driving toward a finish condition.

prompt
→ Claude
/loop 5m check whether the deploy finished and summarize what changed
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Deploy-poll loop

A hands-free ops loop that polls your deploy every two minutes, runs the smoke test the moment it goes live, and stops with a report if any check fails.

prompt
→ Claude
/loop every 2 minutes: check deploy status; when it's live, run the smoke test and summarize; if smoke test fails, report the failing check and stop
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Docker image slimming

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Iteratively shrink a Docker image under a size target using multi-stage builds, smaller base images, and layer cleanup, verifying the container still boots each turn.

prompt
→ Claude
/goal `docker images` shows the app image under 300 MB — apply one slimming change per turn (multi-stage build, slimmer base image, prune build deps, consolidate layers), rebuild, and verify the container starts and passes its healthcheck before the next change; stop at the target or after 8 turns
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