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Ralph Specum Implement

Community
tzachbon
ralph-specum-implement

This skill should be used only when the user explicitly asks to use `$ralph-specum-implement`, or explicitly asks Ralph Specum in Codex to run implementation for approved tasks, quick mode, or an explicit continue request.

Overview

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

Use it in TypingMind

Enable Ralph Specum Implement 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 Implement 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 Implement 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 Implement

You are a coordinator, not an executor -- delegate each task to a spec-executor sub-agent.

Contract

  • Resolve the active spec by explicit path, exact name, or .current-spec
  • Require tasks.md
  • Recompute task counts from disk before execution
  • Merge state fields only
  • Reconcile prototype records before dispatch and block only dependent work
  • Remove .ralph-state.json only when all tasks are complete, verified, and activePrototypes is empty

Action

  1. Resolve the active spec. If none exists, stop.
  2. Require tasks.md. Read .progress.md, current state, and current task markers.
  3. Parse tasks.md once into ordered top-level task rows. Include only unindented checkboxes outside fenced example blocks whose next token is a concrete numeric task ID, V<number>, VE<number>, or VF; exclude nested and example checkboxes, completion criteria, and placeholder IDs. From that one list derive total, the completed count across all rows, and next_index as the zero-based position of the first incomplete row or total when none remains. Do not derive next_index from the completed count; non-prefix completion cases resume at the earliest incomplete row.
  4. Resolve the dispatch task index before merging state. For fresh execution, use next_index. For a prototype return, require a validated non-negative returnTaskIndex and verify that it identifies the first eligible incomplete task. Merge state once with:
    • phase: "execution"
    • awaitingApproval: false
    • totalTasks: total
    • taskIndex: next_index for fresh execution, or the validated returnTaskIndex for a prototype return
    • preserve taskIteration, maxTaskIterations, globalIteration, maxGlobalIterations, commitSpec, and relatedSpecs
  5. Before dispatch, run prototype_records.py reconcile whenever state exists and run select-downstream whenever activePrototypes is nonempty or prototype history exists. Request --target execution, --target "task:$TASK_INDEX", and --path for every declared current-task path. Stop when an active blocker or stale input targets the work, or when any matching targetDecisions entry is not both proofAvailable: true and eligible: true. Missing dependency or approved-transfer proof blocks conservatively. Report the prototype ID and resume active work through $ralph-specum-prototype --resume <id>; route terminal staleness to its earliest affected phase or task.
  6. On a prototype return, verify that the merged taskIndex still equals the validated returnTaskIndex and identifies the first eligible incomplete task before dispatch.
  7. Delegate each task to a spec-executor sub-agent. Pass the task description, file targets, success criteria, and context from .progress.md. The sub-agent implements the task and outputs TASK_COMPLETE. Do NOT implement tasks yourself. Execute tasks in order until complete or blocked.
  8. [P] tasks may batch only when file sets do not overlap and verification is independent.
  9. [VERIFY] tasks stay in the same run and must produce explicit verification evidence.
  10. Marker syntax must be explicitly present in tasks.md. If markers are absent, treat tasks as non-batchable by default.
  11. VE tasks are valid quality tasks when the spec includes autonomous end-to-end verification.
  12. Native task sync metadata should be preserved when present.
  13. After each task or safe batch:
  • mark the checkbox
  • update .progress.md
  • merge the state update
  • use the task Commit line unless commits were explicitly disabled
  1. Before any batching, generated-task, CI, review-fix, branch-publication, or PR-lifecycle push, apply the Prototype Evidence Push Gate in ../../references/workflow.md. Normal mode may ask at that boundary for separate explicit authorization naming every outbound **/prototypes/*.md record. Quick mode asks no question and skips every push. A skipped or denied push ends the dependent remote lifecycle path: do not run gh pr create, gh pr merge, gh pr checks, gh pr view, gh api, gh run, gh issue, remote review polling, issue writes, or later remote steps that depend on that push. Quick mode continues or finishes locally and reports Remote lifecycle skipped: prototype evidence stayed local. Preserve the existing normal remote lifecycle only after the gate completes the push. Never push an isolated prototype source branch. commitSpec authorizes local commits only.
  2. On failure or interruption, persist the current state and stop with a resumable summary.
  3. On full completion, reconcile again. If activePrototypes remains nonempty, preserve .ralph-state.json and stop with its IDs. Otherwise remove state and report completion.

Resume Rules

  • Resume from the persisted task state when execution was already in progress.
  • If disk state and task checkboxes disagree, prefer tasks.md for completion and repair state to match.
  • If approval is still pending for tasks, stop and get approval unless quick mode or explicit user direction says to continue.
  • A stale task or dependent active prototype always wins over resume dispatch. An unrelated active prototype does not pause the current task.

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 Implement AI skill do?

This skill should be used only when the user explicitly asks to use `$ralph-specum-implement`, or explicitly asks Ralph Specum in Codex to run implementation for approved tasks, quick mode, or an explicit continue request.

Why use Ralph Specum Implement on TypingMind?

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

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 Implement?

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

Is the Ralph Specum Implement 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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