Add a monitoring dashboard to NanoClaw. Installs @nanoco/nanoclaw-dashboard and a pusher that sends periodic JSON snapshots.
npx skills add https://github.com/nanocoai/nanoclaw --skill add-dashboard
Adds a local monitoring dashboard showing agent groups, sessions, channels, users, token usage, context windows, message activity, and real-time logs.
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 │
└──────────────┘
pnpm install @nanoco/nanoclaw-dashboard
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.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:
// 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.
DASHBOARD_SECRET=<generate-a-random-secret>
DASHBOARD_PORT=3100
Generate the secret: node -e "console.log('nc-' + require('crypto').randomBytes(16).toString('hex'))"
Run from your NanoClaw project root:
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.
Once the service is restarted, confirm the dashboard is live:
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.
| Page | Shows |
|------|-------|
| Overview | Stats, token usage + cache hit rate, context windows, activity chart |
| Agent Groups | Sessions, wirings, destinations, members, admins |
| Sessions | Status, container state, context window usage bars |
| Channels | Live/offline status, messaging groups, sender policies |
| Messages | Per-session inbound/outbound messages |
| Users | Privilege hierarchy: owner > admin > member |
| Logs | Real-time log streaming with level filter |
DASHBOARD_SECRET matches in .envDASHBOARD_PORT in .envlogs/nanoclaw.log existsReverse the apply steps. Safe to re-run even if some pieces are already gone.
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.
pnpm run build
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Take nanocoai/add-dashboard from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npm.
Without those the skill loads but fails at the first command.