Design Meta Skill logo

Design Meta Skill

Organization
VectorSpaceLab
design-meta-skill

Designs and validates a reusable Creator meta-skill workflow for an evidence-backed recurring construction gap after distill-ml-knowledge selects the design-reusable strategy. Use it to parameterize future source or task anchors, verify the workflow, and prepare an approved managed Creator-infrastructure import.

Overview

PublisherVectorSpaceLab
RepositoryAREX-Skill
Skill namedesign-meta-skill
Stars
341
Forks
26
Bundled files
4
LicenseApache-2.0
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by VectorSpaceLab on GitHub. Read the source before you install it.

Installation

Install the Design Meta Skill 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/VectorSpaceLab/AREX-Skill/tree/main/cli/packages/coding-agent/src/disco/skills/design-meta-skill
  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/VectorSpaceLab/AREX-Skill.git /tmp/AREX-Skill
mkdir -p .claude/skills
cp -r /tmp/AREX-Skill/cli/packages/coding-agent/src/disco/skills/design-meta-skill .claude/skills/design-meta-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design Meta Skill 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 Design Meta Skill 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 Design Meta Skill 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.

Design Reusable Meta Skill Bundle

Design a parameterized Creator workflow for future distillation anchors. This skill does not produce the operating graph for the current task and does not execute the final research or software task. The paper distinguishes only task-agnostic and task-oriented distillation; design-reusable is a Creator construction strategy, not a third distillation form.

distill-ml-knowledge owns anchor identification, Q scoping, adequacy assessment, composition assessment, and strategy selection. This skill owns the reusable workflow design, validation, approval, and managed Creator-infrastructure import after it receives that exact handoff.

Required Handoff

Require the current routing handoff with:

  • decision revision and construction strategy: design-reusable;
  • anchor kind, anchor identity, and distillation form;
  • current task tau = (q, D, E, g) when the gap came from a task-oriented run;
  • future anchor class and intended operating output when it came from a task-agnostic source run;
  • scoped capabilities Q, required evidence/source behavior, and verification/recovery needs;
  • Creator-visible capability matrix and inventory snapshot;
  • uncovered recurring construction contract, recurrence evidence, and why extension or bounded composition is inadequate;
  • construction constraints, unknowns, assumptions, and approval state.

If invoked without this handoff, read ../distill-ml-knowledge/SKILL.md and return through canonical routing. Do not start another reuse, composition, or gap decision here. If the visible inventory or requirements changed materially, return the evidence for a new routing revision.

Workflow

  1. Validate the handoff and stop with the exact missing blocking fields. Do not inspect Researcher-only skills or hidden paths.

  2. Read reusable-bundle-specification.md. Draft the exact future-anchor and four-stage specification, preserving exclusions and assumption owners. Obtain approval before material source exploration, file generation, or compute use.

  3. Stage all artifacts outside live skill roots, for example:

    text
    ~/.disco/agent/creator-runs/<run>/<timestamp>/
      routing-handoff.md
      reusable-bundle-spec.md
      evidence-plan.md
      draft/<meta-skill-id>/
      verification/
      construction-record.md
  4. Read generation-verification-and-review.md and generate a workflow that later performs scope, ground, construct, and verify for caller-supplied anchors.

  5. Parameterize all source, task, environment, permissions, budget, graph, and handoff inputs. Never hardcode the current checkout, credentials, temporary task state, or machine-specific paths.

  6. Keep every draft meta SKILL.md explicitly metadata.disco-role: meta. Do not include target-specific agents/ or agents/openai.yaml. Require every operating root, router, and sub-skill produced by the future workflow to declare metadata.disco-role: operating.

  7. Verify the candidate workflow on representative anchors: one task-agnostic source anchor, one task-oriented task anchor with source discovery, one source/access failure and recovery, one graph verification failure and local repair, one task-bound/project deployment, and one self-contained/managed deployment. Confirm uncertain reuse defaults to project scope, each graph stays in one scope, and repo output uses its specialized locked transaction.

  8. Run the validator before review:

    bash
    node scripts/validate_meta_skill.mjs <draft-meta-skill-dir>
  9. Present the routing revision, reusable-bundle specification, evidence plan, candidate files, verification results, unresolved gaps, exact managed target, collisions, and overwrite impact. Obtain approval for this exact artifact revision.

  10. After approval, import only the reviewed runtime meta bundle as Creator infrastructure:

    bash
    node scripts/import_meta_skill.mjs \
      --agent-dir ~/.disco/agent \
      <creator-run-dir>/draft/<meta-skill-id>

    Use --overwrite only after separate approval. The helper refuses to replace an operating or unclassified target.

  11. Tell the user to run /reload. The next invocation of the imported meta skill, not this design run, constructs and verifies the operating graph and writes its researcher-handoff.md.

Required Outputs

  • validated routing handoff and recurring-gap evidence;
  • approved reusable-bundle specification;
  • staged, parameterized meta-skill draft and evidence plan;
  • verification results, repairs, unresolved points, and construction record;
  • separate managed Creator-infrastructure import proposal or result;
  • contract for later operating graph verification, one-scope deployment, locked import, and Researcher handoff.

The future workflow's output is accepted operating graph G and construction record R; the current meta bundle itself is not an operating graph. Its reusability is assessed against recurring anchors and the generated workflow's ability to support both project deployment under .agents/skills/ and managed deployment under ~/.disco/agent/skills/.

Failure Rules

  • Missing or stale handoff: return to distill-ml-knowledge.
  • Unresolved source access, credentials, hardware, permissions, or budget: keep the item in the future construction environment and stop before use.
  • Failed strict verification: report the unverified bundle and do not import it.
  • Invalidated recurring gap: return evidence for a new routing revision.
  • Collision, overwrite, expanded source access/cost, or weakened verification: obtain separate approval.
  • Never write drafts or review artifacts into live skill roots, split one future operating graph across scopes, send repo metadata through the generic importer, or execute the final downstream task here.

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 Design Meta Skill AI skill do?

Designs and validates a reusable Creator meta-skill workflow for an evidence-backed recurring construction gap after distill-ml-knowledge selects the design-reusable strategy. Use it to parameterize future source or task anchors, verify the workflow, and prepare an approved managed Creator-infrastructure import.

Why use Design Meta Skill on TypingMind?

Because you install it once and use it with any model. Design Meta Skill 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 Design Meta Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/cli/packages/coding-agent/src/disco/skills/design-meta-skill. 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 Design Meta Skill?

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 Design Meta Skill?

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

Is the Design Meta Skill AI skill free?

Yes. It is published on GitHub by VectorSpaceLab under the Apache-2.0 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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