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Terminal

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jstanden
terminal

The agent terminal -- its commands, absolute paths, how `search` actually matches, the `|` Twig pipeline, and /tmp as a compute surface. Load it before your first search comes back empty.

Overview

Publisherjstanden
Repositorycerb
Skill nameterminal
Stars
83
Forks
38
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Terminal 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/jstanden/cerb/tree/12.0.x/features/cerberusweb.core/assets/agent_filesystems/cerb-agents/skills/terminal
  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/jstanden/cerb.git /tmp/cerb
mkdir -p .claude/skills
cp -r /tmp/cerb/features/cerberusweb.core/assets/agent_filesystems/cerb-agents/skills/terminal .claude/skills/terminal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Terminal 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 Terminal 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 Terminal 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.

The agent terminal

Your terminal tool runs ONE command line per call -- verb, arguments, and flags together, exactly as you would type them at a prompt. Everything below is a COMMAND, not a tool of its own: there is no separate search tool, only search ... typed into the terminal.

It is NOT a Unix shell. It has only the documented commands -- ls, find, search, read, write, append, edit, copy, rm -- plus a trailing | pipeline. A few Unix names work as aliases -- cat (read), grep (search), dir (ls), cp (copy) -- and everything else errors, echo and awk included.

search needs a fulltext index, which only a mounted volume has. With nothing but /tmp you do not have it -- filter /tmp with a pipeline instead.

Paths

Write every path in full. cd exists, but assume it does not stick: your terminal call starts at / every time, so a cd is gone by your next call. Use an absolute path (/cerb-docs/references/docs/kata.md) or the @<filesystem>/path shorthand (@cerb-docs/references/docs/kata.md). A mount is read-only unless its overview says otherwise; /tmp is always read-write.

Searching the mounted filesystems

The search command matches file CONTENTS with AND, not OR -- every word in the query must appear in the same file. Piling on keywords narrows to zero hits fast, and zero hits is NOT evidence the topic is undocumented.

  • Start with the one or two most distinctive terms, then narrow with --path or --ext instead of adding more words.
  • --terms is the OR view, and it is how you find the right word. It counts how many files contain EACH word, ranked, instead of searching for all of them: search --terms school college academic university tuition. One call, any number of synonyms, no reading. Take the highest count, require it with +, and throw the next set of words at it: search --terms +college tuition fees pricing. Repeat until the word list is good, THEN run the real search.
  • A wildcard or stem term lists the words it covers, inline on one indented line beneath it: auto* reports automations (476) automation (374) automatically (228) .... That is how you learn the vocabulary this corpus actually uses instead of guessing at it; a trailing ... means the list was capped.
  • A + term is echoed as a heading above the counts; every count below it is within the files that contain it, so numbers collapse fast. That collapse is the signal -- keep the words that survive it.
  • A miss falls back to this view automatically, and keeps any + you already had, so a failed AND search still tells you which words exist alongside it.
  • A - instead of a count means the index skips that word as too common (the, not, can, com, www, ...) -- it was never searched for, which is not the same as finding nothing.
  • Run separate searches for alternatives; there is no OR.
  • A search returns the 25 most relevant files, not every match. --top <n> raises or lowers that cap and --top 0 returns every match unranked, so a full list is one flag away. Don't report a capped list as the whole answer.
  • Use find to match names and search to match contents. Add --lines only after you've picked a file worth reading in context.
  • find also carries every Markdown file's frontmatter, so you can select on metadata without opening anything. Each row has its keys under meta, and --fields title,description prints chosen ones as columns: find *.md --fields name | files|filter(f => f.meta.name starts with "cerb"). Listing never opens a file, so this costs nothing.
  • Prefer search/find to locate a file, then read only the part you need (--offset/--limit).

/tmp -- a compute surface, not an overflow buffer

/tmp is a read-write scratch area. Reach for it whenever you'd otherwise scan a long piece of text by hand, or need to hold intermediate work across steps.

  • Write an arbitrary string to a file (write /tmp/scratch.txt), then read it back through a trailing Twig filter chain to count, filter, reshape, or diff it -- for example staging alternate drafts, or reducing a large document to just the lines that matter, before committing a final version anywhere.
  • It is per-session, and it is NOT indexed by search. Filter it with a pipeline rather than searching it.
  • A command whose output is too large to return is saved there and referenced by path.

The | pipeline

Pipe ANY command through Twig filters with a trailing | (lines, filter, map, length, column, ...). The pipeline runs on the FULL output before any truncation, so it is how you reduce a big result to just what you need instead of paying for the whole thing in context.

read /tmp/scratch.txt | lines|filter(l => "field" in l)
search widgets | results|map(r => r.meta.title ?? r.path)
ls /docs | files|filter(f => not f.is_dir)|column("name")

lines preserves original line indices as keys. Each command sets its own row variable -- lines, results, files above -- and that is what makes a pipe iterate ROWS rather than text. For a longer transform than fits on one line, put a Twig template in script instead of a trailing |.

This is Cerb scripting, not a pipeline language of its own -- the same Twig, the same filters, functions and operators you would write anywhere else in Cerb. Read @cerb-agents/skills/scripting/SKILL.md for what is available rather than guessing at a filter name here.

cerb -- the command line into this installation

Some agents also have cerb, a command line into THIS Cerb install rather than into files. It reports what this installation actually has, custom record types and fieldsets included, which the documentation cannot. Run cerb help to see what it answers, and cerb <topic> help for one topic's usage. Its rows pipe like any other command's.

Frequently asked questions

What does the Terminal AI skill do?

The agent terminal -- its commands, absolute paths, how `search` actually matches, the `|` Twig pipeline, and /tmp as a compute surface. Load it before your first search comes back empty.

Why use Terminal on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jstanden/cerb/tree/12.0.x/features/cerberusweb.core/assets/agent_filesystems/cerb-agents/skills/terminal. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Terminal?

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 Terminal?

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

Is the Terminal AI skill free?

It is published on GitHub by jstanden. Check the repository for licensing terms. 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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