Shellscript Coder logo

Shellscript Coder

Organization
sno-ai
shellscript-coder

Implement and refactor production shell scripts, command-line tools, automation, process wrappers, and Git or infrastructure helpers with reliable failures, useful logs, and machine-readable output.

Overview

Publishersno-ai
Repositorysno-station-skills
Skill nameshellscript-coder
Stars
857
Forks
609
Bundled files
1
LicenseApache-2.0
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 sno-ai on GitHub. Read the source before you install it.

Installation

Install the Shellscript Coder 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/sno-ai/sno-station-skills/tree/main/plugins/sno-station-standalone/skills/shellscript-coder
  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/sno-ai/sno-station-skills.git /tmp/sno-station-skills
mkdir -p .claude/skills
cp -r /tmp/sno-station-skills/plugins/sno-station-standalone/skills/shellscript-coder .claude/skills/shellscript-coder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Shellscript Coder 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 Shellscript Coder 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 Shellscript Coder 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.

Shell Script Coder

Every development task has a finite budget including setup, tests, and reporting. Before running tests, estimate duration from available timings and identify the changed behavior or directly affected consumer. If timing is unknown, state an estimate; do not build a timing system or run a preliminary suite. Full evaluation, unit, integration, or repository suites require the owner's explicit request, never a skill, PRD, review, or default command. Remove checks that neither prove the requested main function nor expose its defects. Repeat only after a relevant change or failure. Every new security check requires the owner's explicit approval for that specific check; remove auxiliary security, assertion, hash, or bookkeeping blockers of useful work.

Write small, production-grade Bash that fits the repository and remains understandable during failure. Local instructions define the runtime and style; they may strengthen these rules but not weaken reliability.

Read Before Writing

  • Confirm the required shell from the shebang, callers, CI, and repository docs. Use Bash when the target is Bash; do not claim POSIX portability unless it is required and tested.
  • Read the complete target script, immediate callers, sourced libraries, config, and nearby tests. Reuse existing argument, logging, state, and error conventions.
  • Before changing a tool version, or using a third-party CLI, API, or service in a way neither the PRD nor existing scripts already show, read its current official documentation; for a version change, read the release notes between the two versions. Name the page and version in the report. When a PRD governs the work and lacks or contradicts that fact, add it to the PRD's execution notes.
  • Decide the observable success result, failure result, exit codes, and verification command before editing.
  • Preserve unrelated work. Touch only requested behavior and remove only orphans created by the change.

Boundaries

  • No backward-compatibility aliases, dual paths, legacy flags, or fallbacks unless the user confirms an external published contract requires them.
  • No speculative options or abstractions. Prefer one explicit path over a configurable framework.
  • Use deterministic shell or an existing deterministic helper for routing, parsing, retries, validation, and state transitions. Never ask a model to decide them.
  • Never use eval or construct executable commands in strings. Use functions and arrays.
  • Never print secrets, tokens, credential-bearing URLs, raw environment dumps, or sensitive command lines.

Reliability

  • Start new Bash scripts with set -Eeuo pipefail unless a documented caller contract makes one option incompatible. Handle expected nonzero statuses locally instead of globally weakening failure behavior.
  • Quote every expansion unless intentional word splitting is proven and commented. Use arrays for argument lists, -- before user-controlled paths, [[ ... ]] for tests, and printf rather than echo for data.
  • Validate required arguments, enums, paths, and dependencies before mutation. Reject unknown flags and malformed state with a specific message and nonzero exit.
  • Keep functions focused, use local, pass values explicitly, and avoid hidden mutable globals. Constants are uppercase and readonly where practical.
  • Clean temporary files, locks, mounts, and child processes with traps or an explicit cleanup function. Preserve the original exit status.
  • Write durable files atomically: create in the destination directory, flush when required, then rename. Never expose a partially written state file.
  • Use flock or the repository's existing lock for shared state. Keep the critical section narrow and define what a lock timeout means.
  • Make retryable operations idempotent. Bound retries, use a fresh observation before retrying, and distinguish “already completed” from “failed.”
  • Bound external commands with a timeout when they can hang. Propagate signals to children and verify termination when orphaned processes would be harmful.
  • For work that may exceed two minutes, emit per-unit progress with completed/total, rate, and ETA.

Messages and Agent-Facing Interfaces

Design messages as operational evidence, not narration.

  • Keep stdout reserved for the primary result. Send progress, diagnostics, and errors to stderr. If --json is supported, stdout contains exactly one valid JSON result and no decoration.
  • Error messages name: operation, target, failed condition, consequence, and the next safe action. Include relevant identifiers and paths; omit secrets and noisy stack traces unless a debug mode already exists.
  • Progress messages identify the current stage and include stable context so a detached AI agent can tell which task and resource produced the line.
  • Use stable, documented exit codes when callers branch on failure classes. Otherwise use 0 for success and a small nonzero set; do not invent dozens of codes.
  • Prefer non-interactive flags and stdin over prompts. A command intended for agents must support --help, reject missing inputs immediately, and never wait silently for a terminal response.
  • Make input contracts explicit: required flags, accepted values, path ownership, preconditions, and whether rerunning is safe.
  • Make output easy to inspect and bind to the action. For a new JSON interface, prefer a minimal envelope such as ok, operation, target, and either result or error; include recorded commits, digests, or state paths when they are the proof a controller needs.
  • Generate JSON with jq or another repository-standard encoder, never by string concatenation.

Failure Design

For every external command or multi-step mutation, answer:

  1. What can fail or time out?
  2. What state may already have changed?
  3. Can the exact operation be retried safely?
  4. What evidence lets an AI agent choose retry, repair, or stop?

Fail closed for publication, deletion, credentials, shared state, and Git ref movement. A partial success must be recorded explicitly; do not collapse it into a generic failure that encourages a destructive retry.

Verification

Run the narrowest relevant checks first:

  1. bash -n on every touched Bash file.
  2. The repository's ShellCheck or code-check command when configured; do not add a new lint dependency just for ceremony.
  3. Focused tests for one accepted path and the nearest rejected boundary.
  4. When relevant, tests for timeout, interruption, retry/idempotency, concurrent writers, paths with spaces, and partial failure.
  5. Stop after the affected checks pass; the repository's default suite is not permission to expand the test scope.

Use temporary directories, local bare Git remotes, and boundary fakes that assert observable behavior. Do not mock repository-internal logic. Re-read the final diff and remove any line that does not improve required correctness, diagnostics, or operability.

Delivery

Report the changed behavior, input/output contract, important failure semantics, and exact verification commands with results. State any unverified external dependency plainly; never report success over a failing or skipped required check.

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 Shellscript Coder AI skill do?

Implement and refactor production shell scripts, command-line tools, automation, process wrappers, and Git or infrastructure helpers with reliable failures, useful logs, and machine-readable output.

Why use Shellscript Coder on TypingMind?

Because you install it once and use it with any model. Shellscript Coder 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 Shellscript Coder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sno-ai/sno-station-skills/tree/main/plugins/sno-station-standalone/skills/shellscript-coder. 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 Shellscript Coder?

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 Shellscript Coder?

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

Is the Shellscript Coder AI skill free?

Yes. It is published on GitHub by sno-ai under the Apache-2.0 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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