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Ralph Specum Tasks

Community
tzachbon
ralph-specum-tasks

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

Overview

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

Use it in TypingMind

Enable Ralph Specum Tasks 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 Tasks 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 Tasks 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 Tasks

You are a coordinator, not a task planner -- delegate ALL work to a task-planner 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
  • Require requirements.md and design.md
  • Merge state fields only
  • Keep the Ralph disk contract unchanged

Action

  1. Resolve the active spec. If none exists, stop.
  2. Require requirements.md and design.md. Read research.md when present, .progress.md, and current state.
  3. Run phase_gate.py mode through "$RALPH_CODEX_PLUGIN_ROOT/scripts/phase_gate.py" with STATE and exact --quick, exact --interactive, or no flag. Reject both, -q, variants, and natural-language substitutes.
  4. In interactive mode, require artifact approval for the current design.md before starting tasks. Exact quick mode continues with the validated artifact.
  5. Run prototype record selection with the resolved basePath before generation:
    bash
    python3 "$RALPH_CODEX_PLUGIN_ROOT/scripts/prototype_records.py" select-downstream --base-path "$BASE_PATH" --state "$BASE_PATH/.ralph-state.json"
  6. Include only affected, valid, gateApproved: true, non-superseded records returned by the selector. Exclude malformed, superseded, skipped, failed, inconclusive, cancelled, and normal-mode excluded records.
  7. Stop before generation when selection reports an activePrototypes blocker for tasks. Name the active ID and route resume through $ralph-specum-prototype. Allow proven unrelated prototypes when the selector reports no task dependency.
  8. Stop when selection reports stale design.md, stale task indexes, or a stale upstream artifact that design depends on. Route to the earliest stale phase and do not plan from stale design.
  9. Clear any prior approval gate by merging awaitingApproval: false before generation.
  10. Respect granularity from state. Allow --tasks-size fine|coarse to override it. Treat task sizing as administration, not an interview question. In exact quick mode, default unset granularity to fine.
  11. When research.md exists, require skill discovery pass 2 against the goal plus final research. When it is absent, require pass 1 against the goal alone. Run the applicable pass when the state lacks its revision. Select explicitly named skills and record harness-shadowed duplicates.
  12. Load "$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, follow the algorithm for critical delivery slicing, dependency order, rollout risk, and verification thresholds. Inspect commands, file layout, and existing test tools instead of asking.
  13. 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 task-planner dispatch. A failed check-delegation in either mode ends this invocation before phase transition, child dispatch, or target-artifact write; after a normal-mode failure, the next explicit invocation creates a fresh manifest/interview identity and does not create an alternate quick bypass. 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.
  14. Delegate task planning to a task-planner sub-agent. Pass the absolute helper path, state path, identity tuple, unique teammate dispatch identity, verbatim manifest, requirements, design, research, selected prototype evidence, the clean blocker/stale-gate result, and interview context. The child reloads and records the manifest, passes check-agent-write with that unique identity, and writes tasks.md. Do NOT write tasks.md yourself.
  15. Read the sub-agent's output and validate it exists.
  16. Count tasks and merge state with:
  • phase: "tasks"
  • awaitingApproval: true (or false when --quick is active)
  • taskIndex: first incomplete or totalTasks
  • totalTasks: counted tasks
  1. Update .progress.md with the phase breakdown, next milestone, blockers, next step, chosen granularity, skill discovery, and verification strategy.
  2. If spec commits are enabled, commit only the spec artifacts.

Stop Behavior

  • Without --quick: STOP HERE. Display the walkthrough summary and approval prompt. Do NOT continue to implementation. Wait for the user to explicitly approve and request the next phase.
  • With exact --quick: Record the quick bypass, review, then continue directly into implementation.

Output Shape

Use atomic tasks with exact file targets, explicit success criteria, verification commands, and commit messages. Preserve POC-first ordering. Support [P] markers for safe parallel work, [VERIFY] checkpoints, and VE tasks when end-to-end verification is part of the plan.

Response Handoff

  • After writing tasks.md, name tasks.md and summarize the task plan briefly.
  • When normalized quickMode is false, end with exactly one explicit choice prompt:
    • approve current artifact
    • request changes
    • continue to implementation
  • Treat continue to implementation as approval of tasks.md.
  • With exact --quick, do not show this prompt; continue directly to implementation 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 Tasks AI skill do?

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

Why use Ralph Specum Tasks on TypingMind?

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

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 Tasks?

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

Is the Ralph Specum Tasks 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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