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Hive.Terminal Tools Troubleshooting

OrganizationPopular
aden-hive
hive.terminal-tools-troubleshooting

Read when a terminal-tools call returned something surprising — empty stdout despite no error, exit_code is null, output_handle came back expired, "too many jobs" / "session busy" / "too many PTYs", warning was set unexpectedly, semantic_status disagrees with exit_code. Diagnostic recipes only — load on demand. Don't preload; the foundational skill covers the happy path.

Overview

Publisheraden-hive
Repositoryhive
Skill namehive.terminal-tools-troubleshooting
Stars
11.1K
Forks
5.7K
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by aden-hive on GitHub. Read the source before you install it.

Installation

Install the Hive.Terminal Tools Troubleshooting 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.
  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/aden-hive/hive.git /tmp/hive
mkdir -p .claude/skills
cp -r /tmp/hive/core/framework/skills/_preset_skills/terminal-tools-troubleshooting .claude/skills/hive.terminal-tools-troubleshooting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hive.Terminal Tools Troubleshooting 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 Hive.Terminal Tools Troubleshooting 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 Hive.Terminal Tools Troubleshooting 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.

Troubleshooting terminal-tools

Recipes for surprising results. Match the symptom to the section.

Empty stdout despite the command "should have" produced output

Possible causes:

  1. Output went to stderr instead. Check stderr in the envelope (or use merge_stderr=True for jobs).
  2. Output was fully truncated because max_output_kb is too small. Check stdout_truncated_bytes > 0. Bump max_output_kb or paginate via output_handle.
  3. Command produced no output (correct, just unexpected — silent flags, no matches).
  4. Pipeline issue: the last stage of a pipe ran but stdout went elsewhere (> /dev/null, redirected via 2>&1).
  5. Process is buffering its output and didn't flush before exit. Add stdbuf -oL (line-buffered) or unbuffer to the command.

exit_code: null

CauseOther field
Auto-backgroundedauto_backgrounded: true, job_id: <X>
Hard timeout, process killedtimed_out: true
Pre-spawn failure (command not found)error: ... set, pid: null
Still running (in terminal_job_logs)status: "running"

output_handle returned expired: true

5-minute TTL. Either (a) you waited too long, or (b) the store evicted it under memory pressure (64 MB total cap, LRU eviction). Re-run the command.

To reduce risk: paginate the handle as soon as you receive it, or use terminal_job_* for huge outputs (4 MB ring buffer with offsets — no expiry).

"too many jobs" / JobLimitExceeded

TERMINAL_TOOLS_MAX_JOBS (default 32) hit. Either:

  • Wait for jobs to exit (poll with terminal_job_logs(wait_until_exit=True))
  • Kill old jobs: terminal_job_manage(action="list") to see what's running, then signal_term the abandoned ones
  • Raise the cap via env (rare)

"session busy"

A terminal_pty_run was issued while another _run is in flight on the same session. PTY sessions are single-threaded conversations. Wait for the prior call to return, or open a second session.

"PTY cap reached"

TERMINAL_TOOLS_MAX_PTY (default 8) hit. Close idle sessions (terminal_pty_close). Idle reaping is lazy; force it by opening — no, actually, opening throws when the cap is hit. Just close manually.

warning is set, the command worked

Informational only. The pattern matched (e.g. rm -rf literally appears, or git push --force was used). The command ran. The warning is your "did I mean to do that?" prompt — verify the side effect was intended before continuing.

semantic_status: "ok" but exit_code: 1

Working as designed. Some commands use exit 1 for legitimate non-error states:

  • grep / rg exit 1 when no matches found
  • find exit 1 when some directories were unreadable (typical on /proc, etc.)
  • diff exit 1 when files differ
  • test / [ exit 1 when condition is false

The semantic_message field explains. Trust semantic_status, not raw exit_code.

semantic_status: "error" but exit_code: 0

Shouldn't happen. If it does, file a bug.

truncated_bytes_dropped > 0 in terminal_job_logs

Your since_offset was older than the ring buffer's floor — bytes evicted before you could read them. Either:

  • Poll faster (lower latency between calls)
  • Use merge_stderr=True (single 4 MB ring instead of 4 MB × 2)
  • Accept the gap and move forward from next_offset

terminal_pty_open succeeds but the first _run times out

The session may not have produced its first prompt sentinel within the 2-second startup window. Try:

  • A terminal_pty_run(sid, read_only=True, timeout_sec=2) to drain whatever's accumulated
  • A noop command (terminal_pty_run(sid, command="true")) to force a prompt cycle

Could also indicate the bash process died at startup — terminal_pty_run(sid, ...) would then return "session has exited".

shell="/bin/zsh" returned an error

By design. terminal-tools is bash-only on POSIX. Use shell=True (default /bin/bash) or omit shell= to exec directly.

A command in shell=True is interpreted differently than expected

Bash, not zsh, semantics. **/* doesn't recurse without shopt -s globstar; =cmd expansion doesn't work; arrays use arr[idx] not ${arr[idx]} differently than zsh. When in doubt, the foundational skill's "bash, not zsh" section is the canonical statement.

Frequently asked questions

What does the Hive.Terminal Tools Troubleshooting AI skill do?

Read when a terminal-tools call returned something surprising — empty stdout despite no error, exit_code is null, output_handle came back expired, "too many jobs" / "session busy" / "too many PTYs", warning was set unexpectedly, semantic_status disagrees with exit_code. Diagnostic recipes only — load on demand. Don't preload; the foundational skill covers the happy path.

Why use Hive.Terminal Tools Troubleshooting on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aden-hive/hive/tree/main/core/framework/skills/_preset_skills/terminal-tools-troubleshooting. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hive.Terminal Tools Troubleshooting?

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 Hive.Terminal Tools Troubleshooting?

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

Is the Hive.Terminal Tools Troubleshooting AI skill free?

Yes. It is published on GitHub by aden-hive 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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