dashboard

dashboard is a skill for Claude Code from mubit-ai/claude-plugins. It costs 38 tokens per session (1,987 once invoked), scanned A, original, Apache-2.0.

A local web dashboard for browsing and searching Mubit lessons, reviewing the recall cost per prompt, and checking ingestion health. It runs only on the same computer and opens in a browser.

In plain words
What is it for?
Use it to start the dashboard, view its report, and choose options such as foreground mode, no browser opening, or JSON output.
Why use it?
It gives a person a way to inspect Mubit data and health without installing packages, adding services, or changing shell settings.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the mubit-memory plugin — 15 skills, 1 agent, 13 hooks, 1 MCP server shipped together

Good fit Use it to start the dashboard, view its report, and choose options such as foreground mode, no browser opening, or JSON output.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add mubit-ai/claude-plugins
Claude Code
/plugin install mubit-memory

Made for: Claude Code.

Or install mubit-memory, the plugin that ships this one along with the rest of its 15 skills, 1 agent, 13 hooks, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for dashboard

README.md
[![agentmods](https://agentmods.dev/badge/skills/mubit-ai/claude-plugins/dashboard/github.svg)](https://agentmods.dev/skills/mubit-ai/claude-plugins/dashboard)
Your own site
<a href="https://agentmods.dev/skills/mubit-ai/claude-plugins/dashboard"><img src="https://agentmods.dev/badge/skills/mubit-ai/claude-plugins/dashboard/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for dashboard

Your own site · 80×15
<a href="https://agentmods.dev/skills/mubit-ai/claude-plugins/dashboard"><img src="https://agentmods.dev/badge/skills/mubit-ai/claude-plugins/dashboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,987 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00038 $0.01987
Opus 5.5 $0.00015 $0.00795
Sonnet 5.5 $0.00008 $0.00397
Haiku 4.5 $0.00004 $0.00199

Measured 1mo ago against content hash c02ea195cdbf, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

dashboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

integrations/claude-code/skills/dashboard/SKILL.md · 133 lines

How it starts

The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.

This skill never installs anything. It starts one Node process from the plugin directory, binds a random port on 127.0.0.1, and opens a browser at it. No packages, no services, no changes to the user's shell.

disable-model-invocation: true is deliberate: this is a command a person types when they want to look at something. Nothing in a conversation should decide, on its own, to open a web page.

Start it

node "${CLAUDE_PLUGIN_ROOT}/bin/dashboard.mjs"

That prints a URL and tries to open it. The output is the whole report — pass it on verbatim rather than paraphrasing it, because the URL carries the token and a retyped one will not work.

Exit Meaning What to tell the user
0 Running. The URL is in the output. Give them the URL. If a browser did not open, they can paste it.
1 It did not come up inside the launch window. Re-run it with --foreground, which keeps the server in this process and prints why it failed instead of detaching.

Add --no-open when there is no browser to open — over SSH or in a container — and --json when the result is going to be parsed rather than read.

What it shows

Everything on the page is anchored on one directory. The context block in the sidebar lists every project directory the data directory has seen, each with its runs under it — the current one, the one before /clear, how many subagent runs — because a directory's memory is spread over several run ids (cc-<slug>-<hash>, -c<N> after each /clear, -sub-<id> per subagent) and the page folds them back together. The header names the directory, the run every write on the page uses, and how many sessions the directory has had.

Five pages, and one chain running through them: a session has turns; a turn was given lessons; the reply earned them an outcome; the turn may have saved lessons of its own; and every lesson carries how often it was injected, worked and failed.

  • Overview — five tiles over a window of 7, 14 or 30 days: turns, lessons injected, worked / failed, lessons saved, recall cost per prompt — each with a delta against the previous equal window, or no earlier window when the history is younger than two. One chart with three tabs: turns per day by outcome (worked, neutral, failed, none injected — a verdict overrides the automatic signal), lessons injected per day, recall cost per prompt. Under it, the most reinforced and the most failed lessons, and a fold-out with what the run writes at and reads from in words.
  • Turns — one row per prompt across the directory's runs, grouped under a session header when there is more than one session: time, prompt, session, agents (main, or main + 3 sub), how many lessons were injected, what they cost, whether the reply used them, and the outcome as a pill. Sort by any numeric column, search the prompts, filter by session or outcome or has injected, choose columns. Opening a row shows the prompt, the meta, Injected (N) with each lesson's content, scope and its own worked / failed counts, the Worked / Did not work buttons, Saved in this turn (N), the subagents that ran under the prompt, and the used-signal note. Rows come from the turn files for six hours and from the per-turn ledger (runs/<run_id>/ledger.jsonl, written by the Stop hook) for thirty days, so the table outlives the pruning.
  • Lessons — one row per lesson: id, content, scope, origin, injected (turns in this directory that were given it, from the ledger), worked, failed, confidence, last outcome, saved. Three views over one load — Written here, + shared (plus session and global lessons other directories saved, which reach here at recall), Everything — and scope, project and origin filters over it; an instant text filter, and Search instance to ask the instance properly. The row menu carries Worked, Did not work and Delete; the drawer carries the provenance lines (saved when and by whom, directory and run, session, prompt with a Show turn link, reach), the counters, the rationale and conditions, and deletion by typing the id.
  • Feed — everything stored on the instance, newest first under day headers. A row resolves by id, so a trace says which hook and which tool.
  • Health — spool depth, breaker, ingest jobs and cold start for the concrete run, and the marker's last recall and reflection.

Read the full file on GitHub · 133 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +46 lines · +10 tokens per session c02ea195cdbf
  2. 1mo ago Changed · -10 tokens per session cff5bd0b0a05
  3. 2mo ago First seen · 87 lines · 38 tokens per session scan A 835b5982c67c

Subscribe to this mod's changes

dashboard is a skill published in the GitHub repository mubit-ai/claude-plugins (12 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 1,987 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

dashboard compared with skills from other repositories

dashboard next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
dashboardmubit-ai/claude-pluginsSkill121mo agoApache-2.0A38Claude Code
imagegenopenai/codexSkill133kyesterdayApache-2.0A113Codex
playwright-climicrosoft/playwrightSkill99kyesterdayApache-2.0A19Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginnersSkill79k4d agoMITA200Claude Code, Codex
systematic-debuggingobra/superpowersSkill313k3d agoMITA21Claude Code
next-cache-components-adoptionvercel/next.jsSkill143kyesterdayMITA95Claude Code
next-partial-prefetching-adoptionvercel/next.jsSkill143kyesterdayMITA103Claude Code