cloud-architecture-selection
Skill9 starsChanged 4 months agoLicence not identifiedView on GitHub
Derive cloud infrastructure from a workload's actual constraints and required capabilities — never from "X is popular" or from whichever provider is already open in a tab. Use whenever choosing or re-evaluating a cloud provider, a managed service, or a deployment architecture; whenever comparing AWS/GCP/Alibaba/Azure/other for a workload; or whenever a choice is being justified by brand familiarity, a tutorial's default, or sunk cost instead of a stated requirement. Trigger on "which cloud should we use", "AWS vs GCP vs Alibaba", "what should we run this on", "pick a database service", "lock-in", "multi-cloud", "should we migrate", "we already have credits/infra on X". Selects capabilities before products, distinguishes hard constraints from trade-offs from incumbency cost, and never treats a price, SKU, region, or service limit as a fact with an unstated as-of date. Composes with decision-record-discipline for the artifact this skill produces.
In this repository91 other files it gives its agents.
- abductive-engineeringSkill
- acquisition-order-of-volatilitySkill
- agent-trust-boundariesSkill
- alert-triage-economicsSkill
- assume-breach-modelingSkill
- atomic-state-mutationSkill
- attack-surface-triageSkill
- audit-before-patchSkill
- authorization-surface-mappingSkill
- beyond-the-fixSkill
- beyond-the-sinkSkill
- claim-provenance-disciplineSkill
- client-side-trust-boundarySkill
- cloud-control-plane-reasoningSkill
- codebase-health-assessmentSkill
- concurrency-reasoningSkill
- container-trust-boundarySkill
- containment-under-uncertaintySkill
- control-effectiveness-vs-existenceSkill
- credential-material-triageSkill
- crypto-misuse-reasoningSkill
- data-leakage-huntingSkill
- data-provenance-mappingSkill
- daubert-defensible-writingSkill
- debt-closure-disciplineSkill
- deception-signal-qualitySkill
- decision-record-disciplineSkill
- dependency-provenanceSkill
- destination-driven-constructionSkill
- detection-engineeringSkill
- deterministic-coreSkill
- diagnosing-bugsSkill
- discriminating-proofSkill
- distributed-write-atomicitySkill
- dont-fall-in-love-with-the-bugSkill
- dual-use-behavior-adjudicationSkill
- exploitability-triageSkill
- falsifiable-testingSkill
- finding-custodySkill
- forensic-logging-designSkill
- forensic-persistenceSkill
- frontend-accessibility-by-constructionSkill
- frontend-component-boundariesSkill
- frontend-performance-budgetsSkill
- frontend-testing-strategySkill
- git-disciplineSkill
- honest-degradationSkill
- honest-technical-oratorySkill
- hypothesis-driven-huntingSkill
- incident-timeline-reconstructionSkill
- intel-source-evaluationSkill
- invariant-huntingSkill
- irreversible-action-gateSkill
- known-defect-dispositionSkill
- language-selectionSkill
- live-contract-compatibilitySkill
- llm-out-of-the-loopSkill
- model-evaluation-disciplineSkill
- non-overwhelming-uxSkill
- oracle-driven-fuzzingSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 5 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
The licence could not be identified — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

