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Ralph Specum Research

Community
tzachbon
ralph-specum-research

This skill should be used only when the user explicitly asks to use `$ralph-specum-research`, or explicitly asks Ralph Specum in Codex to run the research phase.

Overview

Publishertzachbon
Repositorysmart-ralph
Skill nameralph-specum-research
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 Research 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-research
  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-research .claude/skills/ralph-specum-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ralph Specum Research 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 Research 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 Research 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 Research

You are a coordinator, not a researcher -- delegate ALL work to a research-analyst sub-agent.

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

  • Resolve the active spec by explicit path, exact name, or .current-spec
  • Respect .claude/ralph-specum.local.md when present
  • Default specs root is ./specs
  • Keep the canonical Ralph file names
  • Merge state fields only

Action

  1. Resolve the active spec. If none exists, stop and tell the user to start a spec first.
  2. Read the goal, .progress.md, current state, indexed codebase context, related specs, and epic context when present.
  3. Run phase_gate.py mode through "$RALPH_CODEX_PLUGIN_ROOT/scripts/phase_gate.py" with STATE and exact --quick, exact --interactive, or no flag. Reject both, -q, variants, and natural-language substitutes.
  4. Run skill discovery pass 1 when the state lacks an applicable revision. Select explicitly named skills and record harness-shadowed duplicates.
  5. Load "$RALPH_CODEX_PLUGIN_ROOT/skills/interview-framework-codex/SKILL.md", its required algorithm and domain-modeling references, and all selected domain contracts in both interactive and quick mode. In interactive mode, use its focused brainstorming method for critical evidence scope, decision thresholds, and material unknowns. Inspect source availability, code facts, and existing patterns instead of asking.
  6. 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 the current loaded-manifest identity before creating the child. Apply the shared hard-transition invariant before research writer dispatch. 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.
  7. Delegate research generation to a research-analyst sub-agent. Keep the existing child packet, identity tuple, and receipt behavior unchanged: pass the absolute gate helper path, state path, full identity tuple, unique teammate dispatch identity, verbatim skill manifest, goal, context, and interview results. The sub-agent reloads and records the manifest, passes check-agent-write with that unique identity, and writes research.md. Do NOT write research.md yourself.
  8. Read the sub-agent's output and validate it exists.
  9. Merge state with phase: "research" and awaitingApproval: true (or false when exact --quick is active).
  10. Update .progress.md with the research summary, blockers, learnings, next step, skill discovery, and verification tooling notes when relevant.
  11. If spec commits are enabled, commit only the spec artifacts.
  12. In normal mode, when the user selects continue to prototype, treat research.md as approved and route to $ralph-specum-prototype --suggested --return-phase requirements with the same resolved base path. Let that skill own prototype behavior and its return handoff.

Stop Behavior

  • Without --quick: STOP HERE. Display the walkthrough summary and approval prompt. Do NOT continue to requirements. Wait for the user to explicitly approve and request the next phase.
  • With exact --quick: Record the quick bypass and continue directly into requirements. Do not request a prototype from research; quick mode has one post-requirements request only.

Output Shape

The result should identify existing code patterns, external references, constraints, related specs, risks, verification tooling, and a clear recommendation for the next phase.

Response Handoff

  • After writing research.md, name research.md and summarize the research briefly.
  • When normalized quickMode is false, end with exactly one explicit choice prompt:
    • approve current artifact
    • request changes
    • continue to requirements
    • continue to prototype
  • Treat continue to requirements as approval of research.md.
  • Treat continue to prototype as approval of research.md and route through $ralph-specum-prototype with returnPhase: requirements.
  • With exact --quick, do not show this prompt; continue directly to requirements after the gates succeed.
  • During artifact review, apply the changes immediately delegates already-recorded feedback through a new unique dispatch, redisplays the artifact, 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 Research AI skill do?

This skill should be used only when the user explicitly asks to use `$ralph-specum-research`, or explicitly asks Ralph Specum in Codex to run the research phase.

Why use Ralph Specum Research on TypingMind?

Because you install it once and use it with any model. Ralph Specum Research 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 Research 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-research. 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 Research?

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 Research?

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

Is the Ralph Specum Research 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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