Apex Profile logo

Apex Profile

Organization
tonone-ai
apex-profile

Scope the tonone agent roster for this project — install a curated subset of agents instead of the full 100-agent bundle. Use when "cut down the agent list", "profile for this project", "too many agents", "only need the engineering core", or after apex-stats shows a roster that's mostly unused.

Overview

Publishertonone-ai
Repositorytonone
Skill nameapex-profile
Stars
73
Forks
9
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by tonone-ai on GitHub. Read the source before you install it.

Installation

Install the Apex Profile 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/tonone-ai/tonone.git /tmp/tonone
mkdir -p .claude/skills
cp -r /tmp/tonone/skills/apex-profile .claude/skills/apex-profile
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Apex Profile 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 Apex Profile 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 Apex Profile 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.

Apex Profile

You are Apex — the engineering lead. Scope the installed agent roster to what this project actually uses.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Why this exists

Each tonone agent is registered as its own plugin (<agent>@tonone-ai) in .claude-plugin/marketplace.json, self-contained with its own skills, hooks, and scripts. Installing the monolithic tonone@tonone-ai plugin registers all ~100 agents in every session regardless of project. Toggling individual agent plugins on/off via enabledPlugins in .claude/settings.json gives per-project scoping without any new plugin architecture — this skill automates writing that config correctly.

Steps

  1. Read current state. Check for an existing .claude/settings.json and .claude/settings.local.json in the project root. If either has an enabledPlugins block, show what's currently enabled (in particular, whether tonone@tonone-ai — the full bundle — is on).

  2. Ask how to pick the roster (AskUserQuestion, single-select):

    • Preset — pick from a named project-type roster (below)
    • Custom list — user names the agents directly
    • From apex-stats — run /apex-stats first, then default the roster to every agent with at least one spawn in the lookback window
    • Full roster (undo) — revert to installing all agents

    Presets (curated, extend as needed — cite these verbatim, don't invent new ones without asking):

    PresetAgents
    web-appapex, spine, flux, prism, proof, warden, crest
    mobile-appapex, touch, spine, flux, proof, warden
    platform-infraapex, forge, relay, terra, kube, vigil, warden
    security-auditapex, warden, red, blue, hunt, patch, chain, sast
    ml-productapex, cortex, flux, feat, fit, score, drift, embed

    Always include apex — it's the orchestrator and the front door for any takeover/status/review workflow, even in a scoped profile.

  3. Ask where to write it (AskUserQuestion, single-select) — this is a real tradeoff, not a formality:

    • .claude/settings.json — committed, shared with the team, everyone gets the same scoped roster
    • .claude/settings.local.json — gitignored, this developer only, doesn't affect teammates
  4. Merge, don't overwrite. Read the target file if it exists (else start from {}). Update only the enabledPlugins key, preserving every other key untouched:

    bash
    python3 - "$TARGET_FILE" "$ROSTER_CSV" <<'PYEOF'
    import json, sys, pathlib
    
    target = pathlib.Path(sys.argv[1])
    roster = [a.strip() for a in sys.argv[2].split(",") if a.strip()]
    
    data = json.loads(target.read_text()) if target.exists() else {}
    plugins = data.setdefault("enabledPlugins", {})
    
    if roster:
        plugins["tonone@tonone-ai"] = False
        for agent in roster:
            plugins[f"{agent}@tonone-ai"] = True
    else:
        # full roster / undo
        plugins["tonone@tonone-ai"] = True
        for key in list(plugins):
            if key != "tonone@tonone-ai" and key.endswith("@tonone-ai"):
                del plugins[key]
    
    target.parent.mkdir(parents=True, exist_ok=True)
    target.write_text(json.dumps(data, indent=2) + "\n")
    PYEOF
  5. Report. Roster size vs full 100, which file was written, and that the change takes effect on the next Claude Code session (plugin enablement is read at startup, not live). Note each enabled agent brings its own skills automatically (e.g. spine@tonone-ai includes spine-api, spine-design, etc. — no separate skill toggles needed). If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, and the report path.

Notes

  • This scopes the Agent tool menu, not this skill itself or other gstack/root skills — apex-status, apex-profile, etc. stay available regardless of roster.
  • Disabling tonone@tonone-ai while enabling individual <agent>@tonone-ai plugins is intentional — the monolithic bundle and the per-agent plugins both register the same agents/*.md files, so leaving the bundle on defeats the scoping.
  • If unsure which agents a project needs, run /apex-stats first — it's the evidence this skill should act on.
  • Recommended default: install the lean core (the preset, not the full 100) and lean on /apex-route for occasional long-tail needs — that combination gets a small "Available agent types" listing every session AND full access to all 100 specialists. Reserve installing an extra agent natively for one that apex-stats shows is used often enough that the per-route persona-read cost isn't worth paying repeatedly.

Frequently asked questions

What does the Apex Profile AI skill do?

Scope the tonone agent roster for this project — install a curated subset of agents instead of the full 100-agent bundle. Use when "cut down the agent list", "profile for this project", "too many agents", "only need the engineering core", or after apex-stats shows a roster that's mostly unused.

Why use Apex Profile on TypingMind?

Because you install it once and use it with any model. Apex Profile 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 Apex Profile in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tonone-ai/tonone/tree/main/skills/apex-profile. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Apex Profile?

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 Apex Profile?

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

Is the Apex Profile AI skill free?

Yes. It is published on GitHub by tonone-ai 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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