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Ralph

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jpicklyk
ralph

Launcher for the Ralph-style queue drain script — emits the right `node ralph-loop.mjs` invocation based on the user's filter and bounds. The actual loop runs as a Node script that spawns one `claude -p --worktree` per iteration; this skill is the configurator, not the loop. Use when a user says: drain the backlog, ralph the queue, run Ralph loop, work the queue, batch through queue items, autonomous queue worker.

Overview

Publisherjpicklyk
Repositorytask-orchestrator
Skill nameralph
Stars
204
Forks
22
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 jpicklyk on GitHub. Read the source before you install it.

Installation

Install the Ralph 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/jpicklyk/task-orchestrator.git /tmp/task-orchestrator
mkdir -p .claude/skills
cp -r /tmp/task-orchestrator/claude-plugins/task-orchestrator/skills/ralph .claude/skills/ralph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ralph 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 Ralph 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 Ralph 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.

ralph — Queue Drain Launcher

This skill is a launcher for the Ralph drain script (scripts/ralph-loop.mjs in this plugin). It does not run a loop in the current session. It helps you configure the right invocation, previews what will be processed, and emits the exact command to run.

The actual loop runs as a separate Node process. Each iteration spawns a fresh claude -p in its own git worktree (via Claude Code's -w flag), claims one item from the TO queue, and works it to terminal per the item's schema. Outcome is signaled via a RALPH_OUTCOME: JSON marker that the script parses for circuit-breaker decisions.

Why a separate script, not a slash-command loop? Per Geoff Huntley's canonical Ralph: the value comes from carving off small bits of work into independent context windows. Each iteration is a fresh process — fresh context, fresh worktree, isolated logs, clean exit codes. The skill-as-orchestrator pattern (one session, internal loop) accumulates context within a session and doesn't preserve fresh-context isolation. For autonomous cadence, compose with /loop: /loop 30m node scripts/ralph-loop.mjs --filter "tag=bug-fix".


Step 1 — Resolve the filter

Determine what subset of the queue to drain.

If $ARGUMENTS is non-empty, parse it as a filter expression. Supported keys:

KeyEffect
tag=<value>Items whose tags field contains <value> (substring)
type=<value>Items with this exact type
priority=<value>Items at this priority (high, medium, low)
parentId=<uuid-or-prefix>Only direct children of this container
ancestorId=<uuid-or-prefix>Only items anywhere in this ancestor's subtree, any depth — broader than parentId. Used for the queue preview in Step 3; see the note below Step 1 on where it does and doesn't reach.

Multiple keys combine with AND (space-separated). Examples: tag=bug-fix priority=high, type=quick-fix, parentId=89d02e32, ancestorId=<projectRootId>.

Project-scoped default: when a project rootId is known (session context injected by the SessionStart hook, or .taskorchestrator/config.yaml's project.rootId), default the filter to include ancestorId=<rootId> unless the user explicitly asks to drain across all projects (e.g., "ralph everything", "drain across all projects"). This keeps the Step 3 preview scoped to the current project when multiple project roots share one database.

Known limitation — ancestorId is preview-only. The Step 3 preview uses query_items, which supports ancestorId. The actual per-iteration claim (claim_item, driven by iteration-prompt.md) does not support ancestorId — its selector only understands tag, type, priority, and parentId (see that file's filter-key table). So a filter like ancestorId=<projectRootId> will scope what you see in the preview but is silently dropped when translated for the actual drain — iterations will still claim from the whole queue unless the filter also includes a parentId/tag/type that discriminates the project's items. If precise per-project draining matters, prefer tagging or typing items distinctly per project until claim_item gains ancestorId support.

If $ARGUMENTS is empty, ask the user via AskUserQuestion:

◆ Ralph loop — what should I drain?
  1. Bug-fix backlog (tag=bug-fix)
  2. Quick fixes (type=quick-fix)
  3. Tech debt container (parentId=<lookup>)
  4. Anything in queue (no filter)
  5. Custom filter — specify

If the user picks "Tech debt container", search via FTS: query_items(operation="search", query="Tech Debt", limit=5) and use the resulting UUID. (FTS mode triggers when query is present; depth is a list-mode-only filter and is ignored in FTS mode.)


Step 2 — Resolve bounds

Set sensible loop bounds. Show defaults and let the user adjust.

◆ Loop bounds (default in parens):
  Max iterations:           10
  Gate-failure budget:      3 consecutive
  Error budget:             2 consecutive
  Per-iteration USD cap:    $5
  Claim TTL per iteration:  1800s (30 min)
  Model:                    sonnet
  Cleanup on terminal:      smart (remove if no commits/changes; preserve otherwise)

  Adjust any?  Reply "ok" to use defaults.

If the user adjusts, capture the overrides. Common patterns:

  • High-volume drain: --max 30
  • Low-confidence run: --gate-budget 1 --error-budget 1
  • Architecture-heavy items: --model opus --budget 15
  • Preserve every worktree (debugging-heavy session): --no-cleanup

Step 3 — Preview the queue

Show what the loop will see. Use query_items with the resolved filter to display the first 5 candidates and the total count:

query_items(operation="search", role="queue", claimStatus="unclaimed", limit=10, ...)

Pass ancestorId="<rootId>" in ... when a project rootId is known (resolved above) and the filter didn't already override scope — this gives an accurate project-scoped preview, subject to the preview-only limitation noted in Step 1.

Display:

◆ Queue preview — filter: tag=bug-fix priority=high
  Total claimable: 7 items
  First 5 by priority:
    ◉ d4fc7b2e  Fix duplicate UUID race in claim_item            (high)
    ◉ 92da8e9a  Gate response includes stale guidance            (high)
    ◉ 0c916953  subagent-start hook double-advance               (high)
    ◉ 00769317  advance_item applied:false contradicts DB        (medium)
    ◉ 2e4c9e77  create_work_tree dependency semantics            (low)

If the count is zero, tell the user nothing matches and stop here. If the count exceeds the iteration cap by a lot, mention that subsequent runs would pick up the rest.


Step 4 — Emit the command

Print the exact command to run. The user copies and pastes it.

◆ Ready to launch. Run this command:

  node claude-plugins/task-orchestrator/scripts/ralph-loop.mjs \
    --filter "tag=bug-fix priority=high" \
    --max 10 \
    --budget 5 \
    --ttl 1800 \
    --model sonnet

  → Spawns one `claude -p --worktree=ralph-...` per iteration
  → Each iteration is fresh context, isolated worktree
  → Logs print live as iterations run
  → Final summary lists outcomes and preserved worktrees

  For autonomous cadence (re-run every 30 min until empty):
    /loop 30m node claude-plugins/task-orchestrator/scripts/ralph-loop.mjs --filter "tag=bug-fix priority=high"

  Dry-run first to verify the iteration command:
    node claude-plugins/task-orchestrator/scripts/ralph-loop.mjs --dry-run --filter "tag=bug-fix priority=high"

Adjust the path if the user is in a different working directory — the script lives at claude-plugins/task-orchestrator/scripts/ralph-loop.mjs relative to the project root.

If the user wants the skill to launch the script directly (rather than emit a command), suggest using the Bash tool — but flag the tradeoff:

⚠ Running the script via Bash from this session means the loop output streams
   into our conversation transcript. That works for short drains but burns
   context on long ones. For drains over 5 iterations, prefer running the
   command in a separate terminal so this session stays free for other work.

Step 5 — Post-run handoff

After the script finishes (the user reports back with the summary, or pastes the output), help interpret the outcomes:

Final summary lineAction
Exit reason: queue emptyLoop succeeded — nothing to do
Exit reason: iteration cap reachedRe-run with same filter to continue, or raise --max
Exit reason: gate failure budget exhaustedInspect preserved worktrees; the schema expects notes the iteration agent can't fill autonomously (often review-checklist)
Exit reason: error budget exhaustedInspect last error; likely repo/build/network issue affecting all iterations

For preserved worktrees from gate-blocked or errored iterations:

bash
# inspect
git -C <repo-path> worktree list | grep ralph-

# resume manually with /status-progression on the item ID
# or clean up after inspection
git -C <repo-path> worktree remove ralph-<id>

Outcomes & exit codes (script reference)

The iteration agent emits RALPH_OUTCOME: {...} as its final message. The loop driver maps each status to circuit-breaker behavior:

StatusEffect on loop
terminalCounter ✓; resets gate-failure and error counters; loop continues
gate-blockedCounter ⊘; increments consecutive gate-failure counter; loop continues unless budget hit
errorCounter ✗; increments consecutive error counter; loop continues unless budget hit
skipCounter —; no counter changes; loop continues
no-itemLoop exits cleanly (queue empty)

skip includes resource-lease contention. When advance_item rejects a transition into WORK with errorCode: "resource_unavailable" (transient — a shared resource the item declares via a resources: trait is currently held by another item), the iteration prompt releases its claim on that item and emits skip naming the contended key, rather than retrying the same item or treating it as an error. This is deliberate: a resource_unavailable rejection is expected contention, not a failure of this iteration, and the lock is not going to free up within the iteration's own lifetime — the next iteration should pick a different item, not spin against the same lock. See Workflow Guide §11 — Resource Leasing for the full contention/retry model.

The script's own exit code:

CodeMeaning
0Loop completed normally (queue empty, iteration cap, or gate-budget exit)
2Loop stopped due to consecutive errors
64CLI argument error
70Could not read iteration prompt

Composition with other tools

Want to...Compose with
Run Ralph on a schedule/loop 30m node .../ralph-loop.mjs --filter ...
Inspect what's claimable before running/work-summary to see queue contents
Resume a single gate-blocked item from Ralph/status-progression <item-id>
Clean up a preserved worktreegit worktree remove <path>
Check the iteration prompt templateRead skills/ralph/iteration-prompt.md in this plugin

Examples

Example 1: Drain the bug-fix queue

User: "ralph the bug-fix backlog"

$ARGUMENTS = "tag=bug-fix"

Step 1 parses the filter: tag=bug-fix.

Step 2 uses defaults (max=10, budgets=3/2, $5/iter, sonnet).

Step 3 shows 7 claimable items, top 5 by priority.

Step 4 emits:

node claude-plugins/task-orchestrator/scripts/ralph-loop.mjs \
  --filter "tag=bug-fix" \
  --max 10

User runs it. Each iteration spawns a fresh claude -p --worktree=ralph-<id> with the iteration prompt. The agent uses claim_item selector mode to atomically find and claim one item (a single MCP call — no race window), invokes /schema-workflow to drive it through whatever phases its schema declares, commits changes, and exits with the outcome marker.

5 iterations later: 3 terminal, 1 gate-blocked (a required note couldn't be filled — e.g., needed external context the iteration didn't have), 1 errored (test failure unrelated to the fix). Loop exits when consecutive gate failures hit the budget (3) or queue empties — whichever comes first.


Example 2: Scheduled overnight drain

User wants the queue drained periodically.

After Step 4 emits the standard command, suggest:

/loop 1h node claude-plugins/task-orchestrator/scripts/ralph-loop.mjs --filter "tag=bug-fix priority=high"

The /loop skill schedules it. Every hour, the script runs. If the queue is empty, it exits within seconds. If items appear (e.g., from CI or other agents), they get drained on the next tick.


Example 3: One-shot drain of a specific container

User: "ralph everything in tech debt"

Step 1 searches for "Tech Debt", finds container 89d02e32. Filter resolves to parentId=89d02e32.

Step 4 emits:

node claude-plugins/task-orchestrator/scripts/ralph-loop.mjs \
  --filter "parentId=89d02e32" \
  --max 20

Smart cleanup is on by default — worktrees with no commits or uncommitted changes (e.g., from items whose schema only required note-fills) get auto-removed; worktrees with real diffs are preserved for review/push.


Troubleshooting

Problem: the script reports error: could not read iteration prompt

Cause: The script expects iteration-prompt.md at ../skills/ralph/iteration-prompt.md relative to its own location. The repo layout may have shifted, or the script was copied elsewhere.

Solution: Verify both files exist in the plugin layout: claude-plugins/task-orchestrator/scripts/ralph-loop.mjs and claude-plugins/task-orchestrator/skills/ralph/iteration-prompt.md. Don't move them independently.


Problem: every iteration ends with error: iteration agent exited cleanly without RALPH_OUTCOME marker

Cause: The iteration agent isn't following the prompt — it's exiting without emitting the outcome marker. Likely the agent ran out of budget, hit a tool restriction, or ignored the prompt's exit instructions.

Solution: Run with --dry-run and inspect the prompt that would be sent. If the prompt looks correct, raise --budget (some iterations need more headroom). If it persists, run one iteration manually with claude -p --worktree=test-1 "$(cat skills/ralph/iteration-prompt.md)" to debug interactively.


Problem: claim contention — every iteration gets skip or no_match because all candidates are already claimed

Cause: Stale claims from a crashed previous run, or another worker is already draining. TTL is the recovery mechanism.

Note: With selector mode, claim_item returns no_match (kind=permanent) when all queue items matching the filter are already claimed, rather than returning an already_claimed error. The iteration treats this as a no-item exit.

Solution: Check query_items(operation="search", claimStatus="claimed") to see who holds claims. If they're all from a crashed ralph-<pid>-<ts> actor, wait for TTL expiry (default 30 min per the loop's settings) or release them via claim_item(releases=[...]) if you know they're stale.


Note: Ralph workers skip sub-items of in-progress features (ancestor-claim filtering)

When a fleet agent claims a feature using the hybrid topology (Scenario D in the Fleet Deployment Guide), the feature's child tasks are automatically protected from Ralph workers with broad selectors. A Ralph drain using role=queue will see no_match for any candidate whose ancestor is currently claimed by a different agent — this is the correct and desired behavior, not a bug. Ralph workers naturally focus on top-level claimable work; items inside an in-progress orchestration session are shielded. There is no depth-0/no-parent filter today — omitting parentId in a claim_item selector means unrestricted matching across all depths, it does not restrict to top-level items (same limitation as the ancestorId preview-only gap noted in Step 1). If precise top-level-only draining matters, use the workaround: tag or type top-level items distinctly (e.g., tag=top-level or a dedicated type) so the selector can discriminate them without a depth filter.


Problem: the iteration agent dispatches subagents (Agent tool) and that's confusing the loop

Cause: The iteration prompt doesn't forbid subagents, but they add complexity (the iteration agent's worktree is the dispatched subagent's working directory too).

Solution: Subagent dispatch within an iteration is allowed and sometimes useful (e.g., for parallel reads). It's fine as long as the iteration agent collects subagent results, fills notes, and emits the outcome marker as its own final message. If subagents are causing context bloat, edit the iteration prompt to discourage them.


Design notes

Why claude -p per iteration, not a slash-command loop. Huntley's canonical Ralph relies on fresh context per iteration. A slash-command loop in a single Claude session accumulates context across iterations, even with compaction — that's the variant Huntley publicly criticized when Anthropic's plugin shipped. The script-driver pattern (this design) preserves fresh context cleanly: each iteration is a new OS process with no memory of previous iterations.

Why a Node script, not bash. Cross-platform — every Claude Code install has Node. Stdlib-only — no npm install, no plugin dependencies. The script uses node:util.parseArgs, node:child_process.spawn, and node:fs/promises. Targets Node 18+ to match Claude Code's own minimum.

Why a dedicated ralph-iteration output style. Iterations run under their own output style (task-orchestrator:ralph-iteration), passed via claude --settings. The default workflow-orchestrator output style is shaped for interactive orchestration — tier classification, delegation tables, plan-mode discipline, the workflow-analyst footer — all of which are wrong for a single-item per-iteration agent. The Ralph output style suppresses that chrome and authoritatively encodes per-iteration rules (schema is contract, no auto-memory, no further dispatch, RALPH_OUTCOME marker as final message). Iteration agents get the right system prompt instead of one designed for a different mode.

Why --permission-mode bypassPermissions on each iteration. In claude -p (non-interactive) mode there is no UI prompt to approve MCP or tool calls — unpermitted calls auto-deny and the iteration aborts. Ralph cannot operate autonomously without bypassing the permission gate. The risk surface is bounded by four things working together: (1) the --worktree flag confines file edits to a single isolated tree; (2) the MCP server's own ACL still controls what TO operations are valid; (3) --max-budget-usd caps API spend per iteration; (4) the iteration prompt is tightly schema-scoped — it can't dispatch subagents, can't enter plan mode, and must emit RALPH_OUTCOME as its final message. If a deployment needs stricter permission control, swap the --permission-mode flag in the script for --allowed-tools with an explicit allowlist.

Why smart cleanup is the default. Each iteration's --worktree creates a fresh git worktree under .claude/worktrees/ralph-.... Without active cleanup, long-running deployments (especially scheduled Ralph via /loop) accumulate worktrees indefinitely — both on disk and in git worktree list. The cleanup heuristic checks two things after each terminal (or no-item) outcome: (1) does the worktree have uncommitted changes? (2) does it have commits ahead of origin/main? If neither: remove. If either: preserve, since there's something worth inspecting or pushing. gate-blocked, error, and skip outcomes always preserve regardless — debugging context matters. Pass --no-cleanup to opt out entirely (debugging-heavy sessions, audit-trail needs, etc.). The heuristic intentionally errs toward preservation: if origin/main can't be compared (e.g., upstream missing), the worktree is preserved rather than removed.

Why no PR creation in the script. The end state of an iteration is determined by the item's schema, not by Ralph. A bug-fix schema's review or terminal phase might prescribe pushing and opening a PR; an agent-observation schema's "done" might be just filling a single note. The script doesn't assume a code-change workflow — it just runs iterations and captures outcomes. Workflow logic lives in the schema, where users can configure it per their project.

Why the RALPH_OUTCOME: marker instead of exit codes. An agent inside claude -p doesn't directly control the parent process's exit code (that's Claude Code's harness). Structured stdout output is the cleanest channel — the script regex-matches the marker, parses the JSON, and decides loop control. This also makes outcomes inspectable in iteration logs after the fact.

Why TTL=1800 default (not the documented 900). Default TTL is 15 minutes. A non-trivial iteration (read context, fill notes, do code work, run tests, commit, advance) can exceed that. 1800s gives 30-minute headroom without requiring a heartbeat. For longer iterations, raise --ttl further; the cap is 86400 (24 hours).

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 Ralph AI skill do?

Launcher for the Ralph-style queue drain script — emits the right `node ralph-loop.mjs` invocation based on the user's filter and bounds. The actual loop runs as a Node script that spawns one `claude -p --worktree` per iteration; this skill is the configurator, not the loop. Use when a user says: drain the backlog, ralph the queue, run Ralph loop, work the queue, batch through queue items, autonomous queue worker.

Why use Ralph on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jpicklyk/task-orchestrator/tree/main/claude-plugins/task-orchestrator/skills/ralph. 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 Ralph?

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 Ralph?

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

Is the Ralph AI skill free?

Yes. It is published on GitHub by jpicklyk 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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