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Ralph Specum Start

Community
tzachbon
ralph-specum-start

This skill should be used only when the user explicitly asks to use `$ralph-specum-start`, or explicitly asks Ralph Specum in Codex to start or resume a spec.

Overview

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

Use it in TypingMind

Enable Ralph Specum Start 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 Start 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 Start 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 Start

Use this for the start and new entrypoints.

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
  • Keep .current-spec in the default specs root
  • Keep the standard Ralph files stable
  • Merge .ralph-state.json. Do not replace the full object

Action

  1. Parse explicit name, goal, exact --quick or exact --interactive, --resume <prototype-id>, commit flags, optional specs root, and optional --tasks-size fine|coarse. Reject both mode flags together, -q, variants, and natural-language substitutes.
  2. Classify new versus resume intent, then resolve the classified target by explicit path, exact name, or .current-spec. A new-spec request does not inherit or recover the existing current spec.
  3. If the same name exists in multiple configured roots, stop and require a full path.
  4. Check active epic context from specs/.current-epic when no explicit spec was chosen.
  5. For large or cross-cutting goals, route to triage instead of forcing a single spec.
  6. new is an alias here. Create the spec directory if needed.
  7. Initialize or merge state with:
    • source: "spec"
    • name
    • exact goal text used by the context gate
    • basePath
    • phase: "research"
    • taskIndex: 0
    • totalTasks: 0
    • taskIteration: 1
    • maxTaskIterations: settings default or 5
    • globalIteration: 1
    • maxGlobalIterations: 100
    • commitSpec: settings auto_commit_spec or true
    • relatedSpecs: []
    • awaitingApproval: false while the goal interview is active
    • preserve or set quickMode
    • preserve or set granularity when --tasks-size was supplied
    • preserve or set epicName when starting from an epic suggestion
  8. Update .current-spec.
  9. Write .progress.md with goal, current phase, next step, blockers, learnings, and skill discovery results.
  10. On resume, prefer tasks.md and present files over stale state when they disagree.
  11. Run phase_gate.py mode through "$RALPH_CODEX_PLUGIN_ROOT/scripts/phase_gate.py" with STATE and the exact supplied mode flag. No flag normalizes invalid legacy quick state to interactive.
  12. Run skill discovery pass 1 against the goal. Collect plugin skills, project .agents/skills, project .claude/skills, and the current Codex harness catalog. Always select explicitly named skills and record shadowed duplicates.
  13. Load "$RALPH_CODEX_PLUGIN_ROOT/skills/interview-framework-codex/SKILL.md", its required algorithm and domain-modeling references, and every selected domain contract in both interactive and quick mode. In interactive mode, follow that algorithm for the start goal territory: outcome and observable success, material scope boundaries, critical constraints, and viable high-level approach. Ask no spec-path, branch, task-size, or discoverable question.
  14. 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 a research-analyst child. Apply the shared hard-transition invariant before fresh or resumed research 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.
  15. Keep the existing child packet, identity tuple, and receipt behavior unchanged: pass the absolute helper path, state path, identity tuple, a unique teammate dispatch identity, and the verbatim phaseSkillLoad manifest. The child must record its loads and pass check-agent-write with that unique identity before writing research.md.
  16. Validate research.md, merge phase: "research" and awaitingApproval: true in interactive mode or false in exact quick mode, update progress, and present artifact approval when interactive.
  17. In exact quick mode, record the quick bypass, generate missing artifacts in order, skip normal approval pauses, and continue into implementation in the same run.

Prototype Reconciliation and Resume

After resolving the classified target and before normal resume or quick routing. Skip existing-current-spec recovery for new-spec intent:

  1. Use "$RALPH_CODEX_PLUGIN_ROOT/scripts/resolve_spec_paths.py" and only its resolved basePath. When both basePath and <basePath>/.ralph-state.json exist, run "$RALPH_CODEX_PLUGIN_ROOT/scripts/prototype_records.py" reconcile --base-path "$BASE_PATH" --state "$BASE_PATH/.ralph-state.json", then re-read state.
  2. Treat a missing activePrototypes field as an empty map. Sort entries by created, then ID.
  3. Resume an explicit active ID through $ralph-specum-prototype --resume <id> and stop this skill. In normal mode, resume the sole active entry automatically. When several remain, list deterministic IDs with question, status, blocker, returnPhase, and returnTaskIndex, then stop for an explicit ID.
  4. In quick mode, ask no question. Sort entries that block design by created, then ID. At the post-requirements boundary, route through $ralph-specum-prototype --quick; the prototype skill takes over the oldest design blocker and owns every decision. Preserve earlier quick flow and unrelated entries.
  5. Treat each resume_review candidate as recovery work, not terminal evidence. Parse the exact candidate, verify its ID and hash, and reconstruct a minimal recovery entry from the record and source pointers. Before reviewer dispatch, reserve its ID through create-only locked_state.py upsert-prototype with status: reviewing, the exact candidateHash, null owner and lease fields, a null or absent harnessRun.id, blocker and return fields, source pointers, and recovery timestamps. This is a recovery-only entry: cancellation verifies that owner, leaseToken, and harnessRun.id are all null or absent and that no builder is associated, then skips interrupt and release; inconsistent builder ownership fails closed. On a concurrent reservation, re-read and continue only if its candidateHash matches; never overwrite it. Route the restored entry through deterministic exact-candidate review and publish recovery. If reconstruction or reservation cannot proceed, stop and report the candidate ID and candidate hash. Exclude quarantined or malformed records. If no overlay exists, continue the existing start behavior without extra output.
  6. Quick mode completes its automatic local route and does not reach the interactive Response Handoff below.

Branch Isolation

  • If the user wants isolation, offer a feature branch in place or a worktree with a feature branch.
  • If a worktree is created, stop after creation and ask the user to continue from that worktree.

Response Handoff

  • After creating or resuming the spec, name the resolved spec path and summarize the current state briefly.
  • After setup in normal mode, begin the goal grill in the same run without stopping at a setup prompt.
  • After final approve and delegate, dispatch research in the same turn.
  • After research.md in interactive mode, name the file, summarize it, and end with exactly one artifact approval prompt:
    • approve current artifact
    • request changes
    • continue to requirements
  • With exact --quick, do not show this prompt; continue directly through the remaining phases 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 Start AI skill do?

This skill should be used only when the user explicitly asks to use `$ralph-specum-start`, or explicitly asks Ralph Specum in Codex to start or resume a spec.

Why use Ralph Specum Start on TypingMind?

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

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 Start?

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

Is the Ralph Specum Start 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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