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Ralph Specum

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tzachbon
ralph-specum

Use only when the user explicitly invokes `$ralph-specum`, requests Ralph Specum in Codex, or asks Ralph Specum to handle a named phase or optional prototype.

Overview

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

Use it in TypingMind

Enable Ralph Specum 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 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 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

Use this as the primary Codex surface for Ralph Specum. It carries the full reusable workflow and can handle the entire command surface directly when helper skills are not installed.

Derive RALPH_CODEX_PLUGIN_ROOT from this loaded skill: take the directory containing SKILL.md, then its parent skills directory, then the next parent. Never derive the plugin root from the project working directory.

Read These References

  • "$RALPH_CODEX_PLUGIN_ROOT/references/workflow.md" for the phase flow, branch and worktree behavior, quick mode, and command routing
  • "$RALPH_CODEX_PLUGIN_ROOT/references/state-contract.md" for .ralph-state.json, .progress.md, commit rules, and resume semantics
  • "$RALPH_CODEX_PLUGIN_ROOT/references/path-resolution.md" for specs_dirs, .current-spec, ambiguity handling, and default directory behavior
  • "$RALPH_CODEX_PLUGIN_ROOT/references/parity-matrix.md" for Claude-to-Codex feature translation and command mapping
  • "$RALPH_CODEX_PLUGIN_ROOT/references/prototype-coordinator.md" for direct, suggested, resume, quick, cancel, and prototype handoff behavior
  • "$RALPH_CODEX_PLUGIN_ROOT/skills/interview-framework-codex/SKILL.md" and its required algorithm and domain-modeling references for every normal-mode phase interview

Use These Helpers

  • "$RALPH_CODEX_PLUGIN_ROOT/scripts/resolve_spec_paths.py" for spec roots, current spec, and unique or ambiguous name resolution
  • "$RALPH_CODEX_PLUGIN_ROOT/scripts/merge_state.py" for safe top-level state merges
  • "$RALPH_CODEX_PLUGIN_ROOT/scripts/count_tasks.py" for task counts and next incomplete task
  • "$RALPH_CODEX_PLUGIN_ROOT/scripts/phase_gate.py" for mode, skill-load, interview, parent-delegation, and artifact-write gates
  • "$RALPH_CODEX_PLUGIN_ROOT/scripts/locked_state.py" for locked state and activePrototypes mutations
  • "$RALPH_CODEX_PLUGIN_ROOT/scripts/prototype_records.py" for reviewed immutable prototype records and downstream selection
  • "$RALPH_CODEX_PLUGIN_ROOT/scripts/prototype_harness.py" for bounded builder control outcomes and retry metadata
  • "$RALPH_CODEX_PLUGIN_ROOT/templates/" for the canonical Ralph markdown file shapes
  • "$RALPH_CODEX_PLUGIN_ROOT/assets/bootstrap/" when the user wants optional project-local Codex guidance

Primary Routing

Handle these intents directly:

IntentAction
Start, new, resume, quick modeFollow the start flow in ../../references/workflow.md
TriageDelegate to triage-analyst sub-agent to decompose into epic and specs
ResearchDelegate to research-analyst sub-agent to write research.md
RequirementsDelegate to product-manager sub-agent to write requirements.md
PrototypeFollow ../../references/prototype-coordinator.md, or route an explicit helper request to $ralph-specum-prototype
DesignDelegate to architect-reviewer sub-agent to write design.md
TasksDelegate to task-planner sub-agent to write tasks.md
ImplementDelegate each task to spec-executor sub-agent until complete or blocked
StatusShow active spec, backlog state, and per-root listing
SwitchUpdate .current-spec only
CancelStop execution and clean up state, confirm before destructive delete
IndexGenerate specs/.index/ component and external specs
RefactorDelegate to refactor-specialist sub-agent to update spec files
FeedbackOpen or draft GitHub feedback
HelpSummarize the surface and next commands

If the corresponding helper skill is installed and the user invoked it explicitly, keep behavior aligned with that helper. If not, perform the action here.

Core Rules

  1. You are a coordinator, not a doer. Delegate each phase to the appropriate sub-agent and never write spec artifacts (research.md, requirements.md, design.md, or tasks.md) yourself. Apply the shared hard-transition invariant to only start, triage, research, requirements, design, and tasks. For only start, triage, research, requirements, design, and tasks, enforce that invariant. For those phases, gather context, discover and preload contracts, run the interview, obtain final approval, pass the gate, delegate, validate the output, and present results for artifact approval. Keep the existing delegation flows for implement and refactor unchanged; the new phase gates do not apply to them.
  2. Keep the Ralph disk contract stable.
  3. Treat .claude/ralph-specum.local.md as the settings source when present.
  4. Default to ./specs when no valid config exists.
  5. Keep .current-spec in the default specs root.
  6. Merge state fields. Do not replace the whole state object.
  7. Preserve source, name, goal, basePath, phase, taskIndex, totalTasks, taskIteration, maxTaskIterations, globalIteration, maxGlobalIterations, commitSpec, and relatedSpecs.
  8. Also preserve newer state fields when present, especially awaitingApproval, quickMode, granularity, epicName, discoveredSkills, and native task sync metadata.
  9. Write .progress.md after every phase and after every implementation attempt.
  10. Keep pre-delegation interview approval separate from post-generation artifact approval.
  11. Honor the Commit line in tasks during implementation unless the user explicitly disables task commits.
  12. Use branch creation or worktree creation when the user asks for branch isolation or the repo policy requires it.
  13. Run phase_gate.py mode at entry to every affected phase. Only exact --quick enables quick mode; exact --interactive clears it. Reject both together, -q, variants, and natural-language substitutes. No flags normalize invalid legacy quick state to interactive.
  14. In exact quick mode, run discovery and contract loading, record the current manifest and bypassed_quick, generate missing artifacts, default task granularity to fine when unset, and continue into implementation in the same session.
  15. For normal-mode start, triage, research, requirements, design, and tasks, follow "$RALPH_CODEX_PLUGIN_ROOT/skills/interview-framework-codex/references/algorithm.md" before each delegation.
  16. Keep optional prototype work in activePrototypes; preserve the main phase and current checkout until the recorded handoff.
  17. Use child-agent controls and store only agentId for internal builders. Never use create_thread or threadId for them.

Stop Enforcement

Before delegating any affected phase in normal mode, you MUST:

  1. Complete the applicable discovery pass and reload receipts
  2. Finish all critical decision frontiers
  3. Obtain explicit approve and delegate
  4. Pass phase_gate.py check-delegation

After completing any phase artifact (research, requirements, design, tasks), you MUST:

  1. Display the walkthrough summary
  2. Present the approval prompt (approve / request changes / continue to next)
  3. STOP and wait for user response

The ONLY exception is exact --quick mode with a valid bypass receipt. Without it, you MUST NOT auto-continue to the next phase.

Response Handoff

  • After writing research.md, requirements.md, design.md, tasks.md, or refactored spec files outside quick mode:
    • name the file or files that changed
    • give a short summary
    • end with exactly one explicit choice prompt:
      • approve current artifact
      • request changes
      • continue to <named next step>
  • Treat continue to <named next step> as approval of the current artifact and permission to enter that phase's discovery and interview path.
  • Treat apply the changes during artifact review as immediate authorization to delegate already-recorded revision feedback through a new unique dispatch. Redisplay and remain at artifact approval. Ask one focused change question only when no feedback is pending.
  • Treat control-only continue, proceed, and go ahead as approval of nothing.
  • After normal-mode start or new setup, run discovery pass 1 and begin the goal grill. Explicit final approval delegates research immediately.

Current Workflow Expectations

  • Use critical-decision frontier interviews for start, triage, research, requirements, design, and tasks when quick mode is not active. Ask no setup, administration, or discoverable question.
  • Route obviously large or cross-cutting efforts to triage before normal spec generation.
  • Support active epic state via specs/.current-epic and per-epic state in specs/_epics/<epic-name>/.
  • Treat task planning as POC-first with [P] markers for safe parallel work and [VERIFY] checkpoints for explicit quality validation.
  • Support VE tasks when the plan needs autonomous end-to-end verification.
  • During implementation, recompute task counts from disk, resume from the first incomplete task, and prefer task file truth over stale state.
  • Native task sync is part of the current Ralph execution model. Keep Codex wording aligned with that behavior without promising Claude-only hook mechanics.

Bootstrap

Bootstrap project-local files only when the user wants them.

Suggested bootstrap files:

  • "$RALPH_CODEX_PLUGIN_ROOT/assets/bootstrap/AGENTS.md" to give a consumer repo local Ralph guidance
  • "$RALPH_CODEX_PLUGIN_ROOT/assets/bootstrap/ralph-specum.local.md" to seed local settings

Do not bootstrap by default. Installation into $CODEX_HOME/skills is enough.

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 AI skill do?

Use only when the user explicitly invokes `$ralph-specum`, requests Ralph Specum in Codex, or asks Ralph Specum to handle a named phase or optional prototype.

Why use Ralph Specum on TypingMind?

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

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?

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

Is the Ralph Specum 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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