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Claude Skills by snoodleboot-io
github.com/snoodleboot-io316 skills0 installs568 views
- MinimalManage the lifecycle of encryption keys — generation, storage, rotation, revocation — so that a key compromise is survivable and rotation does not require re-encrypting the world.Votes: 0GitHub stars: 2
- VerboseThe central pattern. Data is encrypted with a short-lived Data Encryption Key (DEK); the DEK is encrypted ("wrapped") by a Key Encryption Key (KEK) held in a KMS or HSM. Only the wrapped DEK is stored, next to the ciphertext.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
- 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
- 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
- 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
- MinimalFind where a system's latency curve bends, and why, rather than proving it passed a number someone invented in a planning meeting.Votes: 0GitHub stars: 2
- VerboseMost teams run one test — expected peak for ten minutes — and believe they have load tested. Each shape isolates a different failure mode, and the others stay invisible until production finds them.Votes: 0GitHub stars: 2
- MinimalChoose a managed database by the access pattern the data will actually see — not by which engine is most familiar or most talked about.Votes: 0GitHub stars: 2
- VerboseThe most expensive database mistakes are made before a single row is written, by choosing the engine from familiarity or hype and then discovering the data does not fit. Invert it. Write down the access pattern first:Votes: 0GitHub stars: 2
- MinimalCreate entity relationship diagrams using Mermaid syntaxVotes: 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
- 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 how services talk to each other, given that every synchronous call adds latency and borrows the callee's availability.Votes: 0GitHub stars: 2
- VerboseEvery synchronous call does two things beyond transferring data: it adds the callee's latency to yours, and it makes your availability the product of both.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
- 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
- MinimalDesign the automated path from raw data to a registered, deployable model — reproducibly, so any past model can be rebuilt from what you recorded.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
- 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
- VerboseEvery metric encodes an opinion about which mistake hurts. Start from the decision the model drives, then pick the metric that penalizes the costly error.Votes: 0GitHub stars: 2
- MinimalExplain what a trained model is doing — globally and for individual predictions — without mistaking the explanation for a statement about the world.Votes: 0GitHub stars: 2
- Verbose"Make it interpretable" is four different requests. Separate them before choosing a tool.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
- VerboseThe layers trade timeliness against definitiveness. Build them in this order, because the fast layers are what actually page you.Votes: 0GitHub stars: 2
- MinimalDiagnose why a model underperforms by isolating the cause — data bug, capacity limit, optimization failure, or irreducible noise — before changing anything.Votes: 0GitHub stars: 2
- VerboseRun this before anything else. It is the cheapest experiment in machine learning and it partitions the space of causes in one shot.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
- 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
- MinimalRun a genuinely-parallel multiagent implementation - plan, gate on environment, spawn subagents concurrently, aggregateVotes: 0GitHub stars: 2
- MinimalTest the tests: deliberately break the production code and check that some test notices.Votes: 0GitHub stars: 2
- VerboseLine coverage answers "did this line execute during the test run". That is a much weaker claim than "if this line were wrong, a test would fail" — and the second is the only claim anyone actually cares about. The gap between them is where escaped bugs live.Votes: 0GitHub stars: 2
- MinimalChoose a non-relational store by starting from the queries you must serve, not from the shape of your entities or a scale number you have not yet reached.Votes: 0GitHub stars: 2
- VerboseRelational modelling lets you defer query design: normalize the entities, and the optimizer will find a plan for whatever you ask later. Non-relational stores remove that safety net. There is no join and often no ad-hoc filter, so the physical layout *is* the query plan. Design it from the queries or you will discover, in production, that a required read is impossible without a full scan.Votes: 0GitHub stars: 2
- MinimalUse object storage (S3, GCS, Blob) for what it is — a flat, HTTP key-value store of immutable blobs — and stop treating it like a filesystem.Votes: 0GitHub stars: 2
- VerboseAlmost every object-storage mistake comes from carrying filesystem instincts into a system that only superficially resembles one. Object storage (S3, GCS, Azure Blob) is a flat map from a string key to an immutable blob of bytes, reached over HTTP. That is the whole model. What looks like a directory tree is an illusion the tooling paints over a flat namespace.Votes: 0GitHub stars: 2
- MinimalMake a system measurably faster by finding where the time actually goes, rather than where you assume it goes.Votes: 0GitHub stars: 2
- VerboseThe instruction to profile before optimizing survives because intuition about performance is unusually bad — worse than intuition about correctness. Code that *looks* expensive (a nested loop, a regex, a hand-rolled parser) is often trivial next to code that looks free (an ORM attribute access that lazily fires a query, a logging call that formats a large object at DEBUG level even though DEBUG is off).Votes: 0GitHub stars: 2
- MinimalDocument follow-up work and testing needs after implementationVotes: 0GitHub stars: 2
- MinimalTurn a vague request into a tree of sub-problems that are individually measurable, independently solvable, and small enough to finish.Votes: 0GitHub stars: 2
- VerboseA stakeholder asks to make search faster. As stated this cannot be started, finished, or verified. Decomposition is the work of converting it into a tree whose leaves each have an owner, a size, and a number that says when they are done.Votes: 0GitHub stars: 2
- MinimalInstrument a product so the numbers survive contact with a real question — consistent names, stable definitions, and a source of truth you can bill against.Votes: 0GitHub stars: 2
- VerboseAn analytics implementation degrades along a predictable path: it starts clean, each team adds events in their own style, and within a year nobody can answer a question without first asking three people what an event means. The taxonomy is what prevents that, and it only works if it is enforced in code review.Votes: 0GitHub stars: 2
- Minimal```promql rate(http_requests_total[5m]) ``` - rate(): Extracts per-second value - [5m]: 5-minute windowVotes: 0GitHub stars: 2
- Verbose```promql rate(http_requests_total[5m])Votes: 0GitHub stars: 2
- MinimalUse type hints where they catch real bugs, and write async code that actually runs concurrently — without blocking the one event loop you have.Votes: 0GitHub stars: 2
- VerboseType hints are checked by a separate tool (`mypy`, `pyright`), never by CPython at runtime. Their value is proportional to how much a tool can prove. Annotate public signatures, return types, and data structures; leave obvious locals bare.Votes: 0GitHub stars: 2
- MinimalMake quality a measured property of the delivery system rather than an opinion held at the end of a sprint.Votes: 0GitHub stars: 2
- Verbose"Quality" is unmanageable until it is a set of numbers with agreed definitions. Four measures cover most of what teams need, and each answers a different question.Votes: 0GitHub stars: 2
- MinimalWrite requirements a stranger could implement and a tester could falsify, so "done" is a fact rather than an argument in review.Votes: 0GitHub stars: 2