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Level

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matteotitta
level

Score your Claude Code environment on a fun 0-10 ladder (Tourist → Swarm) calibrated to the quickstart's 4 systems — Context, Skills, Integrations, Orchestration. Two front doors - an interactive quiz (zero setup, shareable) or a silent scan of your repo. Outputs a next-level roadmap plus a branded HTML dashboard or a paste-anywhere ASCII card. The default mastery skill of this quickstart. Triggers - "level", "what's my claude code level", "claude code level quiz", "assess my setup", "how good is my setup", "claude code maturity"

Overview

Publishermatteotitta
Repositoryrepo-structure-in-house
Skill namelevel
Stars
70
Forks
21
Bundled files
7
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.

  • 7 bundled files

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

  • Open source

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

Installation

Install the Level 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/matteotitta/repo-structure-in-house/tree/main/.claude/skills/level
  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/matteotitta/repo-structure-in-house.git /tmp/repo-structure-in-house
mkdir -p .claude/skills
cp -r /tmp/repo-structure-in-house/.claude/skills/level .claude/skills/level
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Level 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 Level 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 Level 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.

level — the 10 levels of Claude Code

Most people using Claude Code have no idea how much of it they're actually using. This skill scores you 0-10 on a ladder calibrated to the 4 systems this quickstart teaches — Context · Skills · Integrations · Orchestration — benchmarked against a fully-built setup + the latest Anthropic features, then hands you a focused roadmap and a shareable card.

It's the quickstart's mastery skill: the 8 research skills build your marketing OS; /level tells you how far along the Claude Code curve you are and what to build next. Level 0 is "just installed it." Level 10 is genuinely rare — the documented ceiling in references/benchmark.md.

Pick a front door → Score it → Show the roadmap → Render output → Build first step
       ↓                ↓             ↓               ↓               ↓
   quiz or scan    0-10 + 4 pillars  next level only  html / ascii    optional

Re-run it whenever you want — the score updates as you level up.


Modes and flags

FlagEffect
--mode beginner (default)"Knowledgeable friend" voice — explain why each thing matters, define jargon. Default front door = quiz.
--mode internalTerser, for auditing your own / your team's setup. Default front door = scan.
--brand-kit <path>Brand the HTML dashboard from a brand kit (a DESIGN.md with token frontmatter). Default = your context/brand/brand-kit.md once populated, else plain dark. --brand-kit none forces plain dark.
--format html (default) | ascii | bothOutput shape. html = branded dashboard. ascii = paste-anywhere card (Slack / LinkedIn / a code block). both = each.
--quiz / --assessForce the quiz, or the silent scan (then stop after the assessment).
--buildSkip assessment; jump to building the next step.

No flag → --mode beginner, and offer both front doors ("Want the quick quiz, or should I scan your repo?").


Phase 1 — pick a front door, then score

Both doors converge on the same result: a level (0-10) + a 0-4 sub-score per pillar. references/levels.md is the source of truth — the ladder, the 4-pillar mapping, the scan checklist, and the scoring rule.

Quiz (default in beginner mode)

Read references/quiz.md. Ask the ~8 questions in one AskUserQuestion call (clean clickable options — fun and fast). Map each answer to its pillar + level band, then reconcile to a level + 4 pillar sub-scores per the rule in quiz.md. Needs nothing on disk, so it works before you've built anything — and the result is screenshot-shareable.

Scan (default in internal mode)

Silently inspect the repo using the scan checklist in levels.md — CLAUDE.md, .claude/skills, .mcp.json + plugins, memory / rules, hooks + agents, headless / browser signals, worktrees, scheduled tasks. Then ask the 3 context questions (what you use Claude for / biggest friction / one thing you'd automate). Determine the level. Quantity ≠ level — 50 trivial skills is still Level 3; integration + complexity move the rungs.

Present the assessment

Anchor the ceiling with references/benchmark.md. Present, in the mode's voice: the level + its fun name + vibe; the dual lens (which pillars are strong, which is the gap); a bottom-to-top walk of the rungs cleared, then the first rung that stops you and why it matters (plain language). Most marketers land 1-4; 5 is strong; 7+ is rare. If --assess, stop here.


Phase 2 — the roadmap (next level only)

Show ONLY the next-level transition — focus is everything. Read the matching section in references/roadmaps.md for the assessed level and present its 3-4 steps with time estimates.


Generate the output

Always produce output after Phase 1 (+ Phase 2 unless --assess). Pick the shape from --format (default html). For sharing, offer the ASCII card.

HTML dashboard (--format html, default — or both)

  1. Read references/dashboard-template.html.
  2. Resolve the brand: --brand-kit <path> if given; else your context/brand/brand-kit.md (once populated); else --brand-kit none / the template's dark defaults.
  3. Map brand-kit tokens → dashboard tokens per the template's comment: colors.accent→{{ACCENT}} (primary), colors.tertiary/success→{{DONE}}, colors.background/surface→{{BG}}/{{SURFACE}}, colors.on-background*→text tokens, typography.*.fontFamily→{{FONT}} (+ its web <link> as {{FONT_LINK}}), name/domain→{{BRAND_NAME}}/{{BRAND_URL}}. Use the kit's exact hex — never approximate.
  4. Replace every {{TOKEN}} with real assessment data — per-pillar bars + the shareable result card.
  5. Write to ~/Desktop/claude-code-level.html, open it (open macOS / xdg-open Linux / start Windows), tell the user the path + the brand applied.

ASCII card (--format ascii — or both)

A fixed-width, paste-anywhere card. Build a JSON payload and pipe it to the generator (it asserts every line is identical width, so the box never misaligns):

bash
echo '{"level":N,"name":"<level name>","vibe":"<vibe>",
"pillars":{"Context":c,"Skills":s,"Integrations":i,"Orchestration":o},
"takeaway":["short line 1 (≤48 chars)","line 2"],
"brand_name":"<your brand>","brand_url":"<your domain>"}' \
  | python3 references/ascii-card.py

It prints the card and saves ~/Desktop/claude-code-level.txt. Also paste the card inline in a fenced code block so the user can copy it from chat. brand_name/brand_url come from your brand kit's name + domain.


Phase 3 — build it now (optional)

Ask: "Want me to build the first step of your roadmap right now?" If yes, execute the matching step in references/build-steps.md. Adapt to the person — don't paste templates verbatim. If they score 0-2, point them at the quickstart's 15-minute tour and the 8 research skills to build context fast.


Self-roast (run before delivering)

  • Is the level honest, or inflated? Quantity ≠ maturity — did I check integration + complexity?
  • Did I anchor Level 10 to benchmark.md?
  • Beginner mode: did I explain why each gap matters and define jargon?
  • Did I show only the NEXT level's roadmap, not all 10?
  • Did the output actually render (dashboard opened, or ASCII card pasted inline)?

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 Level AI skill do?

Score your Claude Code environment on a fun 0-10 ladder (Tourist → Swarm) calibrated to the quickstart's 4 systems — Context, Skills, Integrations, Orchestration. Two front doors - an interactive quiz (zero setup, shareable) or a silent scan of your repo. Outputs a next-level roadmap plus a branded HTML dashboard or a paste-anywhere ASCII card. The default mastery skill of this quickstart. Triggers - "level", "what's my claude code level", "claude code level quiz", "assess my setup", "how good is my setup", "claude code maturity"

Why use Level on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/matteotitta/repo-structure-in-house/tree/main/.claude/skills/level. 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 Level?

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 Level?

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

Is the Level AI skill free?

Yes. It is published on GitHub by matteotitta 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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