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Ralph Specum Status

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tzachbon
ralph-specum-status

This skill should be used only when the user explicitly asks to use `$ralph-specum-status`, or explicitly asks Ralph Specum in Codex for status or active spec progress.

Overview

Publishertzachbon
Repositorysmart-ralph
Skill nameralph-specum-status
Stars
559
Forks
50
Bundled files
1
LicenseMIT
Links
Skill sourceRepository
  • 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 Status 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-status
  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-status .claude/skills/ralph-specum-status
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ralph Specum Status 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 Status 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 Status 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 Status

Use this to report Ralph state across configured spec roots.

Derive RALPH_CODEX_PLUGIN_ROOT from this loaded skill by resolving two parent directories from the SKILL.md directory. Never derive it from the project working directory.

Contract

  • Read .claude/ralph-specum.local.md when present
  • Default specs root is ./specs
  • .current-spec lives in the default specs root
  • Hidden directories do not count as specs

Action

  1. Resolve configured roots.
  2. Read .current-spec to identify the active spec.
    • If .current-spec is missing or empty, report that there is no active spec and continue listing specs across roots.
  3. Read specs/.current-epic when present and summarize epic status.
  4. For each spec directory, inspect:
    • .ralph-state.json
    • research.md
    • requirements.md
    • design.md
    • tasks.md
  5. When state exists, use the resolved spec path as basePath, run "$RALPH_CODEX_PLUGIN_ROOT/scripts/prototype_records.py" reconcile --base-path "$BASE_PATH" --state "$BASE_PATH/.ralph-state.json", then re-read state. Never construct specs/<name> after resolution.
  6. Inventory prototype records in lexical order:
    • Read activePrototypes, treating a missing field as an empty map.
    • List prototypes/.*.candidate.md, immutable prototypes/*.md finals, and visible or dot-prefixed *.quarantine.md files.
    • Parse candidates and finals with prototype_records.py parse; use select-downstream with state to derive blockers and stale dependencies.
  7. If tasks.md exists, count completed and incomplete tasks.
  8. Group results by spec root.
  9. Show the active spec, current phase, backlog state, approval state, granularity when present, and which artifacts exist.

Output

  • Specs in the default root can be shown by name.
  • Specs in other roots should include the root suffix for disambiguation.
  • Include the next likely command when it is obvious.
  • If an epic is active, include the next unblocked spec.
  • If approval is pending, explicitly tell the user to approve the current artifact, request changes, or continue to the named next step.
  • When prototype data exists, report counts and deterministic rows for active entries, candidates, immutable finals, and quarantines. Include prototype ID, lifecycle status or verdict, blocker targets, returnPhase, returnTaskIndex, sourceDisposition or source pointer, gate approval, and quarantine reason.
  • Include derived activeBlockers, staleArtifacts, and staleTaskIndexes. Omit the prototype section when all overlay counts are zero so legacy output stays unchanged.

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 Status AI skill do?

This skill should be used only when the user explicitly asks to use `$ralph-specum-status`, or explicitly asks Ralph Specum in Codex for status or active spec progress.

Why use Ralph Specum Status on TypingMind?

Because you install it once and use it with any model. Ralph Specum Status 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 Status 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-status. 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 Status?

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 Status?

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

Is the Ralph Specum Status 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.

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