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Add Dashboard

OrganizationPopular
nanocoai
add-dashboard

Add a monitoring dashboard to NanoClaw. Installs @nanoco/nanoclaw-dashboard and a pusher that sends periodic JSON snapshots.

Overview

Publishernanocoai
Repositorynanoclaw
Skill nameadd-dashboard
Stars
30.9K
Forks
12.8K
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Add Dashboard 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/nanocoai/nanoclaw/tree/main/.claude/skills/add-dashboard
  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/nanocoai/nanoclaw.git /tmp/nanoclaw
mkdir -p .claude/skills
cp -r /tmp/nanoclaw/.claude/skills/add-dashboard .claude/skills/add-dashboard
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Add Dashboard 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 Add Dashboard 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 Add Dashboard 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.

/add-dashboard — NanoClaw Dashboard

Adds a local monitoring dashboard showing agent groups, sessions, channels, users, token usage, context windows, message activity, and real-time logs.

Architecture

NanoClaw (pusher)              Dashboard (npm package)
┌──────────┐    POST JSON      ┌──────────────┐
│ collects │ ────────────────→ │ /api/ingest  │
│ DB data  │   every 60s       │ in-memory    │
│ tails    │ ────────────────→ │ /api/logs/   │
│ log file │   every 2s        │   push       │
└──────────┘                   │ serves UI    │
                               └──────────────┘

Steps

1. Install the npm package

bash
pnpm install @nanoco/nanoclaw-dashboard

2. Copy the pusher module and its tests

Copy all three resource files into src/. The tests ship with the skill and run against the composed project — they're how you confirm the skill works and is wired in correctly.

.claude/skills/add-dashboard/resources/dashboard-pusher.ts       → src/dashboard-pusher.ts
.claude/skills/add-dashboard/resources/dashboard-pusher.test.ts  → src/dashboard-pusher.test.ts
.claude/skills/add-dashboard/resources/dashboard-wiring.test.ts  → src/dashboard-wiring.test.ts
  • dashboard-pusher.test.ts — behavior: starts the pusher, posts a real snapshot to a fake dashboard.
  • dashboard-wiring.test.ts — the code edit in step 3: asserts (via the TS AST) that index.ts dynamically imports ./dashboard-pusher.js and awaits startDashboard() as colocated statements of main(), after DB init and before the boot-complete log. Delete or misplace the edit and this goes red.

3. Wire into src/index.ts

This is the skill's one integration point, and it's deliberately minimal and self-contained: all the startup logic lives in dashboard-pusher.ts, and the import is colocated with the call so the whole edit is a single block in one place — there's no separate top-of-file import to add (or to remember to remove).

Add this block inside main(), just before the log.info('NanoClaw running') line:

typescript
  // Dashboard (optional; no-ops without DASHBOARD_SECRET)
  const { startDashboard } = await import('./dashboard-pusher.js');
  await startDashboard();

startDashboard() reads DASHBOARD_SECRET/DASHBOARD_PORT itself and no-ops if the secret is unset, so nothing else in core needs to change.

4. Add environment variables to .env

DASHBOARD_SECRET=<generate-a-random-secret>
DASHBOARD_PORT=3100

Generate the secret: node -e "console.log('nc-' + require('crypto').randomBytes(16).toString('hex'))"

5. Build, test, and restart

Run from your NanoClaw project root:

bash
pnpm run build
pnpm exec vitest run src/dashboard-pusher.test.ts src/dashboard-wiring.test.ts   # behavior + wiring
source setup/lib/install-slug.sh
systemctl --user restart $(systemd_unit)              # Linux
# or: launchctl kickstart -k gui/$(id -u)/$(launchd_label)  # macOS

Run build before the tests: it's what guards the @nanoco/nanoclaw-dashboard dependency. dashboard-pusher.ts reaches the package through await import('@nanoco/nanoclaw-dashboard'), so if step 4 was skipped, pnpm run build fails with TS2307: Cannot find module. The behavior test deliberately mocks that package — its startDashboard binds a real dashboard port, a side effect we don't want in a test — so the test alone would pass with the dependency missing. Build is therefore the leg that verifies the dependency is installed; keep it ahead of the tests in the validate step.

6. Verify (runtime smoke check)

Once the service is restarted, confirm the dashboard is live:

bash
curl -s http://localhost:3100/api/status
curl -s -H "Authorization: Bearer <secret>" http://localhost:3100/api/overview

Open http://localhost:3100/dashboard in a browser.

Dashboard Pages

PageShows
OverviewStats, token usage + cache hit rate, context windows, activity chart
Agent GroupsSessions, wirings, destinations, members, admins
SessionsStatus, container state, context window usage bars
ChannelsLive/offline status, messaging groups, sender policies
MessagesPer-session inbound/outbound messages
UsersPrivilege hierarchy: owner > admin > member
LogsReal-time log streaming with level filter

Troubleshooting

  • "No data yet": Wait 60s for first push, or check logs for push errors
  • 401 errors: Verify DASHBOARD_SECRET matches in .env
  • Port conflict: Change DASHBOARD_PORT in .env
  • No logs: Check logs/nanoclaw.log exists

Removal

Reverse the apply steps. Safe to re-run even if some pieces are already gone.

bash
rm -f src/dashboard-pusher.ts src/dashboard-pusher.test.ts src/dashboard-wiring.test.ts
pnpm uninstall @nanoco/nanoclaw-dashboard 2>/dev/null || true

Then, by hand, remove the single dashboard block the skill added to main() in src/index.ts (the // Dashboard (optional…) comment, the await import('./dashboard-pusher.js') line, and the await startDashboard(); call), and remove DASHBOARD_SECRET and DASHBOARD_PORT from .env.

bash
pnpm run build

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 Add Dashboard AI skill do?

Add a monitoring dashboard to NanoClaw. Installs @nanoco/nanoclaw-dashboard and a pusher that sends periodic JSON snapshots.

Why use Add Dashboard on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nanocoai/nanoclaw/tree/main/.claude/skills/add-dashboard. 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 Add Dashboard?

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 Add Dashboard?

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

Is the Add Dashboard AI skill free?

Yes. It is published on GitHub by nanocoai 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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