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.
/plugin marketplace add mubit-ai/claude-plugins/plugin install mubit-memoryWrote 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.
[](https://agentmods.dev/skills/mubit-ai/claude-plugins/dashboard)<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.
<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>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.
| Model | Per session | Once 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 | $0.00199 |
Grade A, and why
dashboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill never installs anything. It starts one Node process from the plugin directory,
binds a random port on 127.0.0.1, and opens a browser at it. No packages, no services, no
changes to the user's shell.
disable-model-invocation: true is deliberate: this is a command a person types when they want
to look at something. Nothing in a conversation should decide, on its own, to open a web page.
Start it
node "${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, ormain + 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.
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.
- 1mo ago Changed · +46 lines · +10 tokens per session c02ea195cdbf
- 1mo ago Changed · -10 tokens per session cff5bd0b0a05
- 2mo ago First seen · 87 lines · 38 tokens per session scan A 835b5982c67c
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.
dashboard compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dashboardmubit-ai/claude-plugins | Skill | 12 | 1mo ago | Apache-2.0 | A | 38 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |