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Testing Tui Runtime

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subsy
testing-tui-runtime

How to drive the ralph-tui TUI (`run`, watch mode, pause/quit paths) end-to-end in a real terminal with a fake agent and a scratch beads tracker, without AI credentials.

Overview

Publishersubsy
Repositoryralph-tui
Skill nametesting-tui-runtime
Stars
2.5K
Forks
242
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Testing Tui Runtime 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/subsy/ralph-tui/tree/main/.agents/skills/testing-tui-runtime
  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/subsy/ralph-tui.git /tmp/ralph-tui
mkdir -p .claude/skills
cp -r /tmp/ralph-tui/.agents/skills/testing-tui-runtime .claude/skills/testing-tui-runtime
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Testing Tui Runtime 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 Testing Tui Runtime 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 Testing Tui Runtime 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.

Runtime/E2E testing of the ralph-tui TUI

Use this when a change must be proven in the real TUI (watch mode, engine status labels, pause/resume, shutdown/quit paths) rather than with unit tests.

Build & run

bash
cd /path/to/ralph-tui && bun run build      # produces dist/cli.js

Run the built CLI (not bun run dev) so behaviour matches shipped code:

bash
bun /path/to/ralph-tui/dist/cli.js run [flags]

Scratch tracker (never touch the repo's own .beads)

Create a throwaway project dir with its own beads store and drive scenarios with bd:

bash
export RALPH_TEST_DIR="$(mktemp -d "${TMPDIR:-/tmp}/ralph-tui-run.XXXXXX")" && cd "$RALPH_TEST_DIR"
bd init            # or `bd create ...` which bootstraps the store
bd create "Watch mode epic" --type epic -p 1          # note the epic id bd prints
bd create "Alpha task" --type task -p 2 --parent <epic-id>

A fresh dir per run, not a fixed one: a reused dir carries over .beads tasks, the .ralph-tui session file, locks and guards — exactly the things most scenarios here assert on, so a Recovered stale session banner or a stale-lock prompt from the previous run reads as a finding of the build under test. The unique dir also gives the run a unique epic id, which is how you address its process (see the signal trap below).

Gotchas:

  • bd commands must be run with the scratch dir as the working directory, otherwise you get "no beads database found".
  • With --tracker beads, the TUI requires --epic <id>; without it the TUI shows "No epics found" and interactive epic selection blocks automation.
  • br (beads-rust) may not be installed even though AGENTS.md references it; bd works.

Fake agents (no AI credentials needed)

Put plugins in ~/.config/ralph-tui/plugins/agents/:

  • echo.ts — returns echo-agent: local fake response\n<promise>COMPLETE</promise> instantly.
  • slowecho.ts — same but sleeps ~25s in interruptible 500 ms chunks. Essential for testing quit/pause mid-iteration; an instant agent gives you no window to interrupt.

Select with --agent echo / --agent slowecho.

Making timing visible on screen

Wrap the CLI so quit latency is provable in the recording:

bash
#!/bin/bash
cd "${RALPH_TEST_DIR:?export RALPH_TEST_DIR to the scratch dir first}"
export PATH="$PATH:$HOME/.local/bin"
echo "START $(date +%H:%M:%S.%3N)  args: $*"
bun /path/to/ralph-tui/dist/cli.js run "$@"
echo "=== PROCESS EXITED code=$? at $(date +%H:%M:%S.%3N) ==="
exec bash

Typical invocation: ./runwatch.sh --watch --poll 60 --agent echo --tracker beads --epic <epic-id> --no-setup --force

Use a long --poll (60s) to make a stalled pause/quit obvious, and a short one (10s) when you want auto-pickup to happen quickly.

TUI keys

s start · p pause/resume · q → quit dialog → y · d dashboard · r refresh. Header status labels: Waiting (watch idle), Selecting, Executing, Paused, Complete. Dashboard shows Waiting for new tasks when watch-idle.

Known traps

  • Interrupt (Ctrl+C) path: on builds that include the #431 fix (src/tui/utils/keyboard-shortcuts.ts
    • onInterruptRequest in RunApp.tsx + exitSignals: ['SIGQUIT','SIGABRT'] in run.tsx), keyboard Ctrl+C and external kill -INT <pid> both render the ⚠ Interrupt Ralph? dialog with the display intact; n/Esc cancel, y exits code 0 in ~2s and resets the active task to open, and a second Ctrl+C within ~1s force-quits via process.exit(1) (wrapper code=1, terminal left un-repainted; on builds whose exit handler calls releaseLockSync the force quit no longer leaves a lock file behind, on older ones it does). On older builds Ctrl+C is a complete no-op and kill -INT blanks the display and hangs, so always confirm which build you are on before blaming a change.
  • Ctrl+Shift+C is indistinguishable from Ctrl+C in most terminals: konsole sends byte 0x03 for both (verify with stty -isig; cat -v — Ctrl+Shift+C prints ^C, Alt+C prints ^[c), so the "copy" shortcut opens the interrupt dialog whenever the terminal does not consume it itself (konsole only consumes it when a konsole-level text selection exists). Alt+C is safely distinct. Clipboard contents cannot be verified on a box without xclip/xsel/wl-paste (that is what src/utils/clipboard.ts shells out to on Linux) — record the clipboard write as untested.
  • Early-startup signals need timing, and are quirky: the app's SIGINT/SIGTERM handlers are not installed for roughly the first ~0.7s. Drive it with ( sleep 0.5; kill -INT "$(pgrep -f "cli\.js run.*--epic $EPIC_ID")" ) & ./runwatch.sh ..., where $EPIC_ID is this run's epic — check the pattern matches exactly one pid (pgrep -cf ...) before signalling, and note the wrapper script is a different process from the CLI it launches, so signal the pid you matched rather than the wrapper. Do not use pkill -f 'dist/cli.js run': it hits every ralph run on the box, including a parallel test or a main worktree build you are comparing against, and mutates their session and task state. Historic behaviour (before the #434 fix in src/session/lock.ts): SIGINT in that window exited 130 but left a stale lock, and SIGTERM was delivered but ignored (the TUI mounted and kept running). On builds where registerLockCleanupHandlers owns the startup signals (guarded by process.listenerCount(signal) > 1), expect code=143 for SIGTERM / code=130 for SIGINT at every delay in the window, with .ralph-tui/ralph.lock removed; a signal landing before acquireLockWithPrompt (~0.05s) uses default disposition but cannot leave a lock. Always sweep several delays (0.05/0.2/0.3/0.4/0.5/0.7s), log that the signal was actually sent, and check ls .ralph-tui/ralph.lock plus the next launch's banner each time. Two signals in quick succession inside the window should still yield a single clean 130/143, never code=1.
  • Recovered stale session is not the same thing as Stale lock detected: the lock file can be correctly removed while .ralph-tui/session.json keeps status: "running", in which case every next launch prints ⚠ Recovered stale session / Session status set to "interrupted" (resumable) — even after a graceful q/y exit (code=0). Check jq .status .ralph-tui/session.json and session-meta.json before attributing that banner to a lock/signal change; it may be an independent session-persistence bug.
  • In TUI mode the process does not exit by itself when all tasks are done in non-watch mode; it parks on Complete until you quit. To prove "exits when work runs out", use headless mode: --no-tui (alias --headless) exits with code 0 and prints a run summary. Headless watch mode logs [watch] All tasks complete — polling every Ns and stays alive.
  • Killed sessions may leave a stale lock (older builds; fixed once the lock layer owns startup signals and the exit handler releases synchronously): the next launch prompts "Remove the stale lock and continue? (Y/n)" before the TUI starts. Answer y first; keys you send too early (e.g. s) land in that prompt. Prefer graceful q/y exits to avoid this.
  • After creating a task externally, allow up to one full poll interval plus a few seconds before concluding auto-pickup failed.
  • Lock guard file (.ralph-tui/ralph.lock.guard) and lockId: on builds that serialize lock mutations, .ralph-tui/ralph.lock carries a lockId nonce and every create/stale-clean/ --force/release goes through a guard file created with O_EXCL. Useful observer helper: print the lock's pid/lockId, whether the guard exists (and its contents), and pgreped ralph pids, and sample it before/after every scenario. The guard should never be observed present while a run merely executes — only during a mutation. Things worth planting by hand: an empty or not-json guard (should be reclaimed as malformed after ~250ms and the run starts normally), a guard naming a dead pid (reclaimed), and a guard naming a live pid (respected: acquire fails after ~2s with Timed out waiting for the session lock (another ralph-tui process may be starting or exiting)). On such builds a guard held past the sync budget at exit time is expected to leave the lock behind (later recovered as stale) rather than unlink it — a lock left behind there is a pass; a hang, a multi-second stall, or a live process's lock disappearing is the bug. Make sure the "live pid" in a planted guard is really alive (kill -0); a pid that has already exited silently converts the test into the dead-pid case.
  • run --resume may fail where standalone resume works: run.tsx acquires the session lock before calling resumeSession, which acquires again and refuses because a lock exists — observed as Resuming previous session... / Failed to resume session with exit 1, while ralph-tui resume on the same interrupted session starts fine. Reproduced on main too, so treat it as pre-existing unless the build under test claims to fix it; always cross-check against a git worktree build of main before calling it a regression.
  • Watch for an orphaned run with no lock file: observed once — the TUI painted nothing at all yet the engine kept executing iterations and .ralph-tui/ralph.lock was absent. That is the signature of an uncaught exception handler that releases the lock without exiting. It did not reproduce in follow-up attempts; if you hit a blank TUI, capture stdout/stderr by wrapping the run in script -q -c "./runwatch.sh ..." /tmp/run.log (this preserves the TTY, unlike a pipe) before killing it. Note runwatch.sh ends with exec bash, so a script-wrapped run leaves you in an inner shell — type exit to continue a scripted loop.

Devin Secrets Needed

None — the fake agent plugins and the local bd store avoid all external credentials.

Frequently asked questions

What does the Testing Tui Runtime AI skill do?

How to drive the ralph-tui TUI (`run`, watch mode, pause/quit paths) end-to-end in a real terminal with a fake agent and a scratch beads tracker, without AI credentials.

Why use Testing Tui Runtime on TypingMind?

Because you install it once and use it with any model. Testing Tui Runtime 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 Testing Tui Runtime in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/subsy/ralph-tui/tree/main/.agents/skills/testing-tui-runtime. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Testing Tui Runtime?

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 Testing Tui Runtime?

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

Is the Testing Tui Runtime AI skill free?

Yes. It is published on GitHub by subsy 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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