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Create Agent

Organization
Asymmetric-al
create-agent

Guides a one-question-at-a-time design interview, captures alignment in agent/EVE-BRIEF.md, then scaffolds and implements a runnable eve agent with verbose teaching comments. Use when the user wants to create a new eve agent, build an agent from scratch, or invokes /create-agent.

Overview

PublisherAsymmetric-al
Repositorycore
Skill namecreate-agent
Stars
383
Forks
7
Bundled files
2
LicenseAGPL-3.0
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.

  • 2 bundled files

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

  • Open source

    Published by Asymmetric-al on GitHub. Read the source before you install it.

Installation

Install the Create Agent 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.
  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/Asymmetric-al/core.git /tmp/core
mkdir -p .claude/skills
cp -r /tmp/core/docs/ai/skills/create-agent .claude/skills/create-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create Agent 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 Create Agent 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 Create Agent 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.

create-agent

Turn a rough agent idea into a runnable eve agent through structured Q&A, a written brief, then scaffold + implementation.

Phases

Task Progress:
- [ ] Phase 1 — Design interview (one question at a time)
- [ ] Phase 2 — Write agent/EVE-BRIEF.md and get confirmation
- [ ] Phase 3 — Scaffold eve, implement, verify

Do not skip Phase 1 or Phase 2. Do not scaffold or write agent code until the user confirms the brief.


Phase 1 — Design interview

Interview the user until you have a shared, implementable picture of the agent.

Rules

  1. One question at a time. Wait for the user's answer before the next question.
  2. Include a recommended answer with every question — state your pick and why in one or two sentences.
  3. Explore instead of asking when the answer is in the repo, docs, or env (existing agents, package.json, API keys, monorepo layout).
  4. Follow the decision tree. Resolve blockers before dependent questions (purpose → surfaces → tools → scope → runtime).
  5. Use concrete scenarios. When behavior is fuzzy, invent a user message and ask what the agent should do step by step.
  6. Tighten vague language. Replace overloaded words ("handle", "manage", "process") with specific capabilities or tool names.
  7. Surface contradictions. If a new answer conflicts with an earlier one, call it out before moving on.

What to cover (in rough order)

AreaGoal
PurposeOne sentence: what job does this agent do?
Users & entryWho talks to it, and through what channel(s)?
Core loopTypical turn: user says X → agent does Y → user sees Z
Eve surfacesWhich slots matter? (tools, skills, channels, connections, subagents, schedules, hooks, sandbox, evals)
Tools & dataNamed capabilities, inputs/outputs, real APIs vs connections
ScopeWhat is deliberately out of scope for v1?
ModelPreferred model string (default: openai/gpt-4.1-mini via AI Gateway)
SecretsEnv vars needed for real integrations (always include AI_GATEWAY_API_KEY)
LocationProject directory name and whether it lives inside an existing monorepo

Stop interviewing when you can fill every section of EVE-BRIEF template without guessing.

End Phase 1

Summarize decisions in a short bullet list. Ask: "Does this match what you want? Ready for me to write EVE-BRIEF.md?"


Phase 2 — EVE-BRIEF.md

Write agent/EVE-BRIEF.md using the template in reference.md.

  • If eve is not scaffolded yet, write the brief to a temp path or describe that it will land at agent/EVE-BRIEF.md after init — then place it there immediately after scaffold.
  • After writing, show the brief and ask for explicit confirmation before Phase 3.
  • Record any mock/stub fallback and the reason in the brief's data-source column.
  • Fill the Teaching comments section (which surfaces get annotated, primary "start here" files for the README table).

Phase 3 — Scaffold, implement, verify

0. Read eve docs (required before any implementation)

After eve is installed, must read bundled docs at node_modules/eve/docs/ before writing agent code:

  1. Introduction and Getting Started
  2. Every surface page listed in the brief (tools, channels, connections, etc.)
  3. Any API or pattern referenced by sibling agents in the repo

Never rely on hardcoded snippets from this skill or training memory. API shapes, imports, and conventions change — local docs are the source of truth. See reference.md.

1. Detect project layout

Before scaffolding, inspect the repo:

SituationAction
No agent/ directory anywherenpx eve@latest init <dir> (standalone) or npx eve@latest init . (add to existing app)
One existing eve agentMirror its project structure (package.json scripts, tsconfig, env pattern)
Multiple eve agents (monorepo)Inventory siblings; mirror naming, workspace deps, and folder conventions from the nearest agent

Do not assume a specific monorepo shape. Discover conventions from the codebase. See reference.md.

2. Install / scaffold

No eve project yet:

bash
npx eve@latest init <agent-dir>

init installs deps and may start the dev server — run in a controllable process and stop the server before editing files.

Existing project, no agent/ directory:

bash
cd <project-root>
npx eve@latest init .

eve already present: skip init; ensure agent/agent.ts and agent/instructions.md exist.

Optional: --channel-web-nextjs only when the user asked for Web Chat and they are using Next.js.

3. Place EVE-BRIEF.md

Write or move agent/EVE-BRIEF.md into the scaffolded agent/ directory.

4. Implement from the brief

Minimum bar for every agent:

  • agent/agent.ts — model config per brief (follow docs for current defineAgent API)
  • agent/instructions.md — identity, when to use tools, tone, guardrails from the brief
  • At least one real capability — tools, connections, or other surfaces from the brief
  • Channel config if the brief requires it
  • .env.example with AI_GATEWAY_API_KEY= and every env var for real integrations
  • README with clone-and-run steps

Implement only surfaces listed in the brief. For each surface, read the matching page in node_modules/eve/docs/ and follow patterns from the nearest existing agent in the repo when one exists.

Teaching comments (required):

Every file you create or substantially edit during scaffold + implementation must include verbose, educational comments that teach the user how eve works — not just what the line does. Treat the generated agent as a guided tutorial the user will read while running it.

See reference.md — Teaching comments for the full checklist. At minimum:

  • File header on every agent/**/*.ts file: which eve surface this file belongs to, how eve discovers/loads it, and what role it plays in a turn.
  • Section blocks before each logical unit (imports, schema, handler, export): explain the eve concept (e.g. tool identity from path, defineAgent slots, channel request lifecycle).
  • Inline comments on eve-specific APIs, config keys, and conventions the user would not know from TypeScript alone — link to the matching doc page under node_modules/eve/docs/ when helpful.
  • instructions.md: short callouts above each major section explaining how system instructions shape model behavior in eve.
  • README: a "How this agent is structured" section that maps files → eve surfaces and points to the most-commented files to read first.

Do not strip comments eve init generated if they teach a concept. Prefer adding over removing. Skip comments only on truly obvious TypeScript (e.g. import of a local type). Never use placeholder comments like // TODO: explain — write the explanation.

Conventions:

  • Always eve, never Eve
  • Tool identity is path-derived: agent/tools/foo.tsfoo
  • Prefer real connections (agent/connections/) and real data/APIs when feasible
  • Use mock/stub only when the user explicitly chooses it, credentials are not available yet, or a real integration would block v1 — document the choice and reason in EVE-BRIEF.md
  • Do not commit .env or secrets

5. Monorepo housekeeping (only when applicable)

If the repo already hosts multiple eve agents under a shared parent directory:

  1. Update any existing agent index or catalog the repo maintains — for ship-eve, add a row to both agents/README.md and the root README.md with Eve surfaces and Eve capabilities demonstrated (use labels from the capabilities key in agents/README.md)
  2. Register the new package in the workspace manifest (pnpm-workspace.yaml, package.json workspaces, etc.)
  3. Match package naming and shared config from sibling agents

Skip this step for standalone projects.

6. Verify

bash
pnpm install   # or npm install
pnpm typecheck # if available
pnpm build     # eve build

Tell the user how to run pnpm dev, what to ask in the TUI, and how to exercise the agent over HTTP per the eve docs. Point them to the most-commented files as the learning path. Do not commit unless asked.


Examples

User: "I want an agent that summarizes my GitHub PRs"

→ Phase 1 asks: which repo/PR scope? channel? recommended: GitHub connection or API-backed get_pr tool with real token, HTTP channel for v1. Mock only if the user has no credentials yet — note that in the brief.

User: "/create-agent" in a repo that already has eve agents

→ Inventory sibling agents, mirror their layout, interview, brief, scaffold alongside them, update any repo index.


Additional resources

  • reference.md — EVE-BRIEF template, repo layouts, docs discovery, question bank

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 Create Agent AI skill do?

Guides a one-question-at-a-time design interview, captures alignment in agent/EVE-BRIEF.md, then scaffolds and implements a runnable eve agent with verbose teaching comments. Use when the user wants to create a new eve agent, build an agent from scratch, or invokes /create-agent.

Why use Create Agent on TypingMind?

Because you install it once and use it with any model. Create Agent 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 Create Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Asymmetric-al/core/tree/develop/docs/ai/skills/create-agent. 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 Create Agent?

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 Create Agent?

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

Is the Create Agent AI skill free?

Yes. It is published on GitHub by Asymmetric-al under the AGPL-3.0 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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