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Elegant Code

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athola
elegant-code

Guide minimal code via a decision ladder with full safety, edge, and negative-case coverage. Use when adding code, choosing a dependency, or auditing a diff.

Overview

Publisherathola
Repositoryclaude-night-market
Skill nameelegant-code
Stars
343
Forks
37
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by athola on GitHub. Read the source before you install it.

Installation

Install the Elegant Code AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/athola/claude-night-market/tree/master/plugins/conserve/skills/elegant-code
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/athola/claude-night-market.git /tmp/claude-night-market
mkdir -p .claude/skills
cp -r /tmp/claude-night-market/plugins/conserve/skills/elegant-code .claude/skills/elegant-code
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Elegant Code in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Elegant Code on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Elegant Code is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Elegant Code

Write the minimal, elegant code that fully solves the problem: its performance, its edge cases, and its failure cases. Minimal is not fewest lines. It is no incidental complexity, with nothing essential omitted.

What this skill is (and is not)

This skill codifies a discipline that strong models already exhibit some of the time. Its value is not teaching a missing capability. Its value is three things:

  1. A shared vocabulary (the rungs, the floor) that review skills can point at, so "you skipped rung 2" is a concrete, auditable note.
  2. Consistency: the ladder is applied every time, not just when the model happens to think of it.
  3. A guard: the negligence floor below is never traded away for brevity.

It is the generative counterpart to the review-side contract in leyline:additive-bias-defense.

When To Use

  • Before adding a function, file, class, abstraction, or dependency.
  • When choosing between reuse and writing new code.
  • When auditing a diff for lines that can be deleted.

When NOT To Use

  • Throwaway scripts or one-time migrations where review cost is near zero.
  • Cases where the negligence floor (below) is in play: there, add the guard first and optimize length second.

The Decision Ladder

Walk the rungs in order. Climb down only when the current rung does not hold. Stop at the first rung that solves the problem.

#RungAskStop here whenExample
1Need-to-existDoes this need to exist at all?The requirement is speculative or already met. Delete or defer."Config loader for a value used once" -> inline the value
2Builtin / stdlibCan a language builtin or the standard library do it?The stdlib covers it.str.zfill, f"{n:05d}", pathlib, itertools
3Native platformIs there a native platform or framework feature?The platform already ships it.URLSession retry, fetch, framework validators
4Installed dependencyIs an already-installed, audited dependency enough?A dep in the lockfile covers it.reuse requests/urllib3 backoff before adding tenacity
5A few linesCan it be one line or a small explicit block?A short, obvious block solves it.a 20-line retry loop over a retry framework

A new dependency is the last resort, below rung 5, not rung 4. Before adding one, verify it actually exists and is safe with Skill(imbue:dependency-verification): generated code imports non-existent packages at measurable rates, and recurring fake names enable slopsquatting (arXiv 2406.10279, USENIX 2025).

Prefer reuse of an installed, audited dependency over both a new dependency and a hand-rolled reinvention. Zero-dependency code is not automatically cheaper: it relocates the maintenance and bug surface into your own tree (see references/sources.md).

What minimal must still cover

Minimal does not mean fewest lines. It means no incidental complexity, with nothing essential omitted. The decision ladder removes incidental code: reinvention, speculative abstraction, needless dependencies. Three things are essential and are never traded for brevity.

Correctness and safety (the negligence floor)

Never cut for brevity. A review must never propose deleting these as "extra code":

  • Trust-boundary and input validation on untrusted data.
  • Authorization checks (permission gates, row-level security).
  • Data-loss handling (transactions, idempotency keys, retries that cannot double-charge).
  • Error handling on fallible I/O.
  • Security-relevant paths (authn, secrets, crypto).
  • Accessibility.

This floor is evidence-backed, not a slogan. The dangerous failure mode in generated code is omission: roughly 45% of AI-generated samples carry a security flaw, with a measured ~2.74x vulnerability rate versus human code, driven by missing validation, missing authorization, and missing error handling (Veracode 2025). CVE-2025-48757 is a concrete case: generated schemas shipped without row-level security policies. An absent guard is not zero bugs. It is one latent bug per untrusted input.

Performance

Handle the expected input size and the hot path: avoid accidental quadratic loops, N+1 queries, and unbounded memory growth. Do not micro-optimize speculatively. Premature, speculative optimization is itself incidental complexity, so YAGNI applies to performance too. Optimize what the known input size or a profiler justifies, not what feels fast.

Edge and negative cases

The negative path is part of the spec, not extra code. Cover empty, None, and zero inputs; boundary values; malformed or hostile input; partial failure and timeouts; and concurrency where it applies. Code that handles only the happy path is not minimal. It is unfinished.

Elegance, not terseness

Prefer the clearest expression a reader can verify, not the cleverest or the shortest. Terseness and cleverness raise review cost and defect risk: dense one-liners hide edge cases, and correctness-only evaluation misses the maintainability they cost (arXiv 2407.11470). Elegance is minimal incidental complexity plus maximal clarity, which is not the same as minimal characters.

Why minimal (stated honestly)

The case for less code rests on surfaces you can measure, not on a clean "fewer lines means fewer bugs" law. That law is not empirically settled: studies find weak or absent correlation between complexity metrics and defect rate (arXiv 1912.01142, 1912.04014). Argue instead from:

  • Attack surface: fewer imports and branches are fewer places for a known failure mode (hallucinated import, unhandled path).
  • Review cost: a document, and a diff, costs the sum of its readers' time. Less code is less to review correctly.
  • Token and latency cost: shorter output is cheaper to generate and faster to run.
  • Iteration risk: additive self-refinement is not free. One study measured a +37.6% rise in critical vulnerabilities after five refinement rounds (arXiv 2506.11022). Bias the loop toward deletion, then stop.

Verification hooks

After writing, cross-check deletability with real tools instead of guessing from the diff. Gate each behind command -v and fall back cleanly when absent.

bash
# JS/TS: unused files, exports, dependencies
command -v knip >/dev/null && knip || echo "knip absent; skipping"

# Python: dead code
command -v vulture >/dev/null && vulture . || echo "vulture absent; skipping"

# Rust: unused dependencies (nightly)
command -v cargo-udeps >/dev/null && cargo +nightly udeps \
  || echo "cargo-udeps absent; skipping"

Treat tool output as candidates, not verdicts. Code reachable only via reflection, dynamic dispatch, or framework magic can be flagged as dead while being live. Cross-check against the negligence floor before deleting anything.

How this composes

SkillRelationship
imbue:dependency-verificationRung 4/new-dep gate: confirm a package exists before adding it
leyline:additive-bias-defenseReview-side burden-of-proof contract this skill generates against
conserve:code-quality-principlesKISS/YAGNI principles this ladder operationalizes pre-code
conserve:unbloatWhole-codebase cleanup (this skill is per-change)
imbue:scope-guardFeature-level worthiness (this skill is line and dependency level)
pensive:hardenSecurity review that enforces the negligence floor

The /conserve:elegant-code-review command applies this ladder to the current working diff.

Exit Criteria

  • Each addition was walked down the ladder, and the chosen rung is stated (for example, "rung 2: stdlib zfill").
  • Any new dependency passed Skill(imbue:dependency-verification) and reuse of an installed dependency was ruled out first.
  • Every untrusted input has its negligence-floor guard present (validation, authorization, error handling).
  • A deletion tool was run, or its absence was noted, and no flagged candidate that is a guard was deleted.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Elegant Code AI skill do?

Guide minimal code via a decision ladder with full safety, edge, and negative-case coverage. Use when adding code, choosing a dependency, or auditing a diff.

Why use Elegant Code on TypingMind?

Because you install it once and use it with any model. Elegant Code is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Elegant Code in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/athola/claude-night-market/tree/master/plugins/conserve/skills/elegant-code. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Elegant Code?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Elegant Code?

As many as you like. As long as a model supports skills, you can use Elegant Code with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Elegant Code AI skill free?

Yes. It is published on GitHub by athola under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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