Ralph Specum Triage logo

Ralph Specum Triage

Community
tzachbon
ralph-specum-triage

This skill should be used only when the user explicitly asks to use `$ralph-specum-triage`, or explicitly asks Ralph Specum in Codex to triage a large effort into multiple specs.

Overview

Publishertzachbon
Repositorysmart-ralph
Skill nameralph-specum-triage
Stars
559
Forks
50
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 tzachbon on GitHub. Read the source before you install it.

Installation

Install the Ralph Specum Triage 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/tzachbon/smart-ralph/tree/main/plugins/ralph-specum-codex/skills/ralph-specum-triage
  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/tzachbon/smart-ralph.git /tmp/smart-ralph
mkdir -p .claude/skills
cp -r /tmp/smart-ralph/plugins/ralph-specum-codex/skills/ralph-specum-triage .claude/skills/ralph-specum-triage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ralph Specum Triage 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 Specum Triage 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 Specum Triage 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 Specum Triage

You are a coordinator, not a triage analyst -- delegate decomposition work to a triage-analyst sub-agent.

Contract

  • Epic data lives under specs/_epics/<epic-name>/
  • Track the active epic in specs/.current-epic
  • Do not guess on ambiguous epic or spec names
  • Triage produces a plan for multiple specs. It does not implement them

Action

  1. Parse exact --quick and exact --interactive tokens before routing. Reject both together, -q, variants, and natural-language substitutes.
  2. Check specs/.current-epic. If an active epic exists, load its .epic-state.json as STATE and normalize mode before any possible question. With exact --quick, resume the matching active epic without prompting; an explicit different epic must include its goal. In interactive mode, summarize status and offer resume, details, or a new epic.
  3. Validate the resolved epic name against ^[a-z0-9]+(-[a-z0-9]+)*$ before constructing any path. Reject invalid names. If specs/_epics/<epic-name>/.epic-state.json already exists, reuse and resume it without replacing state or progress. If the directory exists without a valid state, stop and require a different name or an explicit user-authorized reset. Only a missing directory may be initialized.
  4. Resolve or create only the epic directory, .progress.md, and .epic-state.json. Serialize the name and goal through a JSON encoder such as jq --arg. Use .epic-state.json as STATE for every phase gate command with phase triage. Use .epic-state.json as STATE for every writer. After that, do not require .ralph-state.json. Create no research.md, epic.md, or generated plan.md before approval.
  5. For a new epic, run scripts/phase_gate.py mode STATE with the exact parsed mode flag, then register specs/.current-epic before discovery. Exact quick mode asks no setup question; missing required name or goal is an input error.
  6. Resolve the output destination from explicit input and configuration. In exact quick mode, use local Spec files as the deterministic default and ask nothing. Only interactive mode may ask one administrative destination question when the destination remains ambiguous; that answer never satisfies the interview gate.
  7. Ensure skill discovery pass 1 exists for the epic goal. Collect plugin, project .agents/skills, project .claude/skills, and current harness catalog entries. Select explicitly named skills and record shadowed duplicates.
  8. Load skills/interview-framework-codex/SKILL.md, its required algorithm and domain-modeling references, and every selected domain contract in both interactive and quick mode. In interactive mode, follow the algorithm for critical decomposition boundaries, stable cross-spec contracts, dependency choices, and sequencing risks. Ask no storage, naming, branch, or discoverable question.
  9. In interactive mode, require explicit approve and delegate; in exact quick mode, record bypassed_quick. In both modes, run phase_gate.py check-delegation with phase triage and the current loaded-manifest identity before every artifact-producing child. Apply the shared hard-transition invariant before every triage artifact writer. A failed check-delegation in either mode stops this invocation before phase transition, child dispatch, or target-artifact write; after a normal-mode failure, only the next explicit invocation creates a fresh manifest/interview identity. After an exact --quick delegation failure, the next explicit invocation reruns discovery, records a fresh phaseSkillLoad and interview identity, and does not reuse a terminal bypassed_quick interview or its discovery revision. Only a matching in-progress collecting or awaiting_confirmation interview is resumable. Exact --quick retains its existing question-and-approval bypass and discovery, manifest, delegation, and writer checks.
  10. Read-only exploration children may inspect seams, constraints, and existing boundaries, but they write no epic artifact.
  11. Keep the existing epic-state, read-only exploration, and multiwriter flow, plus each child packet, identity tuple, and receipt behavior, unchanged. Use separate unique gated writer dispatches as needed:
  • a triage research writer for research.md
  • a triage plan writer for epic.md and generated spec plan.md files
  • a fresh revision writer for every artifact-review revision Pass the absolute gate helper path, epic state path, identity tuple, unique teammate dispatch identity, and verbatim phaseSkillLoad manifest to each writer. Each writer records its manifest loads and passes check-agent-write with its unique identity before its first filesystem artifact write.
  1. The coordinator does not assemble, format, or revise artifact content. Validate the gated writer outputs and persist .epic-state.json with each spec, its status, dependencies, and gate receipts.
  2. Keep specs/.current-epic set to the active epic name.
  3. Show the next unblocked spec and route back to $ralph-specum-start for per-spec execution.

Output Shape

The result should make it clear:

  • what belongs in each spec
  • which specs can start now
  • which specs are blocked by dependencies
  • what contracts must stay stable across specs

Stop Behavior

  • Without --quick: STOP HERE. Display the epic summary and approval prompt. Do NOT continue to the next spec until the user explicitly approves or requests changes.
  • With exact --quick: Record the quick bypass and continue directly to the first unblocked spec.

Response Handoff

  • After writing epic.md, name epic.md and summarize the epic plan briefly.
  • When normalized quickMode is false, end with exactly one explicit choice prompt:
    • approve current artifact
    • request changes
    • continue to the next spec
  • Treat continue to the next spec as approval of epic.md.
  • With exact --quick, do not show this prompt; continue directly to the first unblocked spec after the gates succeed.
  • During artifact review, apply the changes immediately delegates already-recorded feedback through a fresh unique gated revision writer, redisplays the artifacts, and stays at this approval gate. Ask one focused change question only when no feedback is pending. Control-only continue, proceed, and go ahead approve nothing.

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 Specum Triage AI skill do?

This skill should be used only when the user explicitly asks to use `$ralph-specum-triage`, or explicitly asks Ralph Specum in Codex to triage a large effort into multiple specs.

Why use Ralph Specum Triage on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tzachbon/smart-ralph/tree/main/plugins/ralph-specum-codex/skills/ralph-specum-triage. 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 Specum Triage?

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 Specum Triage?

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

Is the Ralph Specum Triage AI skill free?

Yes. It is published on GitHub by tzachbon 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇