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alexjmiller5alexjmiller5/agent-config-public/skills/language-selection/SKILL.md

language-selection

Skill0 starsChanged 41 days agoMITView on GitHub

Use when choosing a programming language or app stack, comparing Go, Rust, Zig or other languages, starting a new app whose stack is undecided, or proposing a language migration for an existing app. Do not use for routine changes within an established stack.

1 other project uses a version of this.

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The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Existing or new?: Keep an existing app's language unless a measured performance or concrete integration benefit justifies changing it.

    line 15
  2. Distinctive:

    Narrow the defaults: Use the table below as a starting point, subordinate to hard constraints.

    line 27
  3. Distinctive:

    Prove uncertainty: When two candidates differ materially, spike the difficult library, target deployment and resource budget.

    line 29
  4. Distinctive:

    Use the declared machine tools for common work and a pinned project development environment for specialist compilers or different versions.

    line 51
  5. Very specific:

    Read Go references for the relevant tool or language.

    line 61

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Language selection

Choose for the product's workload, platform, reliability and maintenance. An AI author's ability to write an unfamiliar language removes personal syntax familiarity as a constraint; it does not remove integration or operational cost.

Gates, in order

  1. Existing or new? Keep an existing app's language unless a measured performance or concrete integration benefit justifies changing it. For a performance migration, set an end-to-end target, profile representative inputs and prefer replacing a hot component before a whole-app rewrite.
  2. Hard constraints. Identify platform APIs, required libraries, hosting, signing, persistence and distribution. Consult the applicable deployment policy. A favorite language does not justify new hosting. Treat an unspecified host as unresolved before committing to a deployment plan.
  3. Resource targets. State relevant startup, peak memory, throughput or latency targets. Separate CPU/allocation work from network/database/model waits. New apps may choose Rust prospectively for tight resource budgets or useful type/ownership guarantees; an existing incident is not required.
  4. Narrow the defaults. Use the table below as a starting point, subordinate to hard constraints. Do not force one language across every layer.
  5. Prove uncertainty. When two candidates differ materially, spike the difficult library, target deployment and resource budget. Record the chosen language, runner-up and reason in the project's AGENTS.md. Explain the recommendation during design; do not reopen a settled choice on every edit.
WorkloadFirst candidateSwitch when
Native Apple UI or Apple-bound integrationSwiftA measured/shared core justifies an additional language
Browser UI, extensions, thin API in an established web stackTypeScriptA supported alternative has a specific integration or resource advantage
Ordinary portable service, sync tool, CLI or daemonGoHosting/libraries or stronger ownership/invariant/resource requirements favor another language
Demanding native core, parser, tight memory/latency budgetRustRequired interop, libraries or a measured spike favor a different implementation
ML/data libraries or Python-centric runtime integrationPythonThe actual bottleneck and dependency path support moving a component
Specialized C-facing library or explicit allocator controlEvaluate Zig against RustLifetime correctness, compiler/editor compatibility and the target build are proven
JVM/.NET/BEAM, shared mobile UI, scientific/GPU kernel requirementsEvaluate the matching ecosystemA concrete platform/library/product need earns the extra runtime or toolchain

A compile/typecheck pass is useful evidence, not proof of correct product behavior. Verify external-input validation, tests, lint, realistic resource behavior, packaging and the installed app. Do not promise a universal language speed ranking or better AI coding success without measurements.

Toolchains

Use the declared machine tools for common work and a pinned project development environment for specialist compilers or different versions. Keep builds reproducible and package installation declarative where the environment requires it. A compiler's availability does not authorize changing the app's language.

For Go, use repository just verbs first; otherwise check formatting, go vet ./..., go test ./..., go test -race ./... on supported targets, and build/run the binary. Add representative benchmarks and native fuzzing when useful. Inspect dependencies with govulncheck; the race detector only covers executed paths. Disable automatic SDK/editor-tool downloads when Nix owns them. Read Go references for the relevant tool or language. For Rust work, use the rust skill if available.

Credits: official language and platform documentation (constraints and tool checks); Go's compatibility, GC and race-detector guides (maintenance and runtime tradeoffs); Rust and Zig references (ownership and allocator choices).

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