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DevOps & Cloud
Deployment, cloud, containers, CI/CD, monitoring, and infrastructure
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- Derive Physical ArchitectureStage 4 skill of /arch. Builds the runtime physical architecture by mapping every logical component to one or more physical components, inheriting system_ref grounding from the inventory, picking specific deployment targets and resources, wiring comms with retry/idempotency stance, and naming a delivery mechanism for every NFR target in the refined quality profile. Mapping cardinality (1:1, N:1, 1:N) is recorded per physical component with rationale for non-trivial cases.Votes: 0GitHub stars: 3
- Author Run LensAuthor a shaped slice's run lens — write the narrative grounding doc (run.md) straight to the LIVE model, and emit the machine-readable per-environment definition (run.yaml) plus any decision as STRUCTURED MANIFEST DATA for the play's keyed persist to write. How the slice ships and operates: the slice-level design (rollout, migrations, CI/CD, TCO) written once, and ONE environment defined per call (local for /launch, or a cloud environment for /deploy — provider, region, compute, services, ne...Votes: 0GitHub stars: 3
- RunWrite a SLICE''s run lens as a narrative grounding doc (run.md) AND a machine-readable per-environment definition (run.yaml) — the slice-level design (rollout, migrations, config/secrets, CI/CD) once, and ONE environment per call (local for /launch, or a cloud environment — provider, region, compute, services, firewalls, security, deploy command — for /deploy). Incremental: re-run to add or edit an environment; the rest are preserved. The END of the NON-FUNCTIONAL realize pipe (architecture →...Votes: 0GitHub stars: 3
- LaunchLand one VALIDATED epic on a human''s evidenced acceptance — the HITL gate closing the execute pipeline (implement → validate → launch). Brings the increment up live on the run lens''s local environment (cloud is /deploy''s), builds HITL testing scenarios from the epic''s user_check + acceptance — each telling the human what to RUN and what to TEST — walks them one at a time expecting a typed answer, and only a complete accepted sign-off releases the close chain that merges the epic (then sta...Votes: 0GitHub stars: 3
- DeployDeploy a delivered increment (a validated, merged epic) to a named CLOUD environment the run lens already defined — defaulting to the lowest cloud tier (dev), running exactly what that environment''s run.yaml declares, then proving it came up healthy with an independent reachability check and recording the deploy as evidence. Lightweight and on-demand: changes nothing in the product model and stops below production (prod stays with CD from main). The /deploy command in the ProductOS command m...Votes: 0GitHub stars: 3
- Verbose**SLO (Service Level Objective):** What we promise (99.9%) **SLI (Service Level Indicator):** How we measure (actual: 99.95%)Votes: 0GitHub stars: 2
- Minimal**SLO (Service Level Objective):** Target (99.9% availability) **SLI (Service Level Indicator):** Measurement (actual 99.95%) **Error Budget:** (1 - SLO) × time (0.1% × month = 43 minutes)Votes: 0GitHub stars: 2
- VerboseServerless is a billing-and-scaling model, not a badge of modernity. The decision turns almost entirely on the shape of your load.Votes: 0GitHub stars: 2
- MinimalBuild systems from managed, event-driven, scale-to-zero compute (FaaS plus managed backing services) so you pay per use and manage no servers — and know the workload shapes where that is the wrong choice.Votes: 0GitHub stars: 2
- VerboseEvery secret system faces the same bootstrap problem: to fetch a secret you need a credential, which is itself a secret. The answer is not to store one, but to have the platform attest to who the workload is.Votes: 0GitHub stars: 2
- Verbose"Multi-cloud" is a loose word that hides four distinct architectures with different costs. Most requests for multi-cloud are really requests for one of the cheaper cousins.Votes: 0GitHub stars: 2
- MinimalDecide whether to run across multiple cloud providers at all — and if so, at what coupling level — knowing that multi-cloud usually costs more than the lock-in it is meant to avoid.Votes: 0GitHub stars: 2
- VerboseThe layers trade timeliness against definitiveness. Build them in this order, because the fast layers are what actually page you.Votes: 0GitHub stars: 2
- MinimalDetect a model degrading in production before the business notices — despite ground-truth labels arriving days or weeks after the prediction.Votes: 0GitHub stars: 2
- MinimalChoose metrics that track the decision the model actually drives, and report them with enough uncertainty that a difference is believable.Votes: 0GitHub stars: 2
- VerboseSoftware CI assumes a git sha plus a lockfile determines the build. ML breaks that: the same commit trained on Tuesday's table and Thursday's table produces two different models, both labelled with the same sha.Votes: 0GitHub stars: 2
- VerboseA model in production is never just weights. It is a bundle whose parts must move together, because any one of them changing alters the predictions.Votes: 0GitHub stars: 2
- MinimalGet a trained model serving traffic safely — packaged, versioned, and rolled out so a bad model is caught before it reaches every user.Votes: 0GitHub stars: 2
- VerboseMessaging systems fall into three fundamental shapes, and most bad choices come from reaching for a familiar product before deciding which shape the problem needs.Votes: 0GitHub stars: 2
- MinimalChoose a messaging system by what your consumers need — delivery guarantee, ordering, retention, fan-out — not by brand familiarity.Votes: 0GitHub stars: 2
- VerboseThe purpose of a readiness review is to produce a decision that could have gone the other way. Most reviews cannot: the criteria are phrased so that "yes" is the only grammatically available answer, and the meeting becomes a ritual performed on the way to a launch that was never in question.Votes: 0GitHub stars: 2
- MinimalDecide whether to ship using criteria agreed before anyone was emotionally invested, and keep the ability to undo the decision cheaply.Votes: 0GitHub stars: 2
- VerboseTwo numbers with two different audiences. `requests` talks to the **scheduler**: it reserves capacity and decides placement. `limits` talks to the **kernel**: it caps what the container may actually consume.Votes: 0GitHub stars: 2
- MinimalSize, cap, and scale workloads so pods get the CPU and memory they need without starving the node.Votes: 0GitHub stars: 2