Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/mixpanel/ai-plugins/cleanup-dashboardsgit clone --depth 1 https://github.com/mixpanel/ai-pluginsWrote 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/commands/mixpanel/ai-plugins/cleanup-dashboards)<a href="https://agentmods.dev/commands/mixpanel/ai-plugins/cleanup-dashboards"><img src="https://agentmods.dev/badge/commands/mixpanel/ai-plugins/cleanup-dashboards.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01893 |
| Opus 5 | $0.00000 | $0.00946 |
| Sonnet 5 | $0.00000 | $0.00379 |
| Haiku 4.5 | $0.00000 | $0.00189 |
Grade A, and why
cleanup-dashboards 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 4d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Command: Cleanup Dashboards
Audits the dashboard estate in a project: identifies stale (not touched in a long time), duplicate, empty, and sparse boards. Recommends archive or delete.
Staleness is a first-class signal here — a board with many reports that nobody has touched in months is still a cleanup candidate. Do not equate "has reports" with "healthy."
Contents
- Phase 1 — Fetch all dashboards (with recency)
- Phase 2 — Deep inspection
- Phase 3 — Classification (structural + recency flags, duplicates)
- Phase 4 — Report
- Phase 5 — Action (interactive delete paths)
- Output
- Error Handling
Execution
Phase 1 — Fetch all dashboards (with recency)
- Fetch the dashboard set per the Fetching the dashboard set rule (sortable entity-search preferred, sorting on the most recent timestamp the API exposes; plain list as fallback).
- For each dashboard, extract whatever the response provides:
id,title,descriptionlast_modified(a.k.a. modified/updated),last_viewed(if present),created_atcreator/owner(if available)
- Cache in
dashboard_list_cache.
Recency field discovery (do this once, silently): Scan the result set — not just the first object — to determine which timestamp fields the API populates, since field coverage can vary board to board. Choose the preferred field by coverage across boards, in priority order: last_viewed → last_modified/updated_at → created_at. Per board, if the chosen field is missing, fall back down the same order for that board and label its recency basis accordingly. Record the field(s) used so the report can label the column accurately. If NO board exposes any timestamp field, skip the Stale classification entirely and tell the user in the report header: "Recency data not available from the API — staleness not assessed; showing structural flags only."
Phase 2 — Deep inspection
For each dashboard from Phase 1, read its full layout. Fire in parallel (batches of 5 to avoid rate limits). Skip any dashboard already in dashboard_layout_cache.
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.
- 4d ago First seen · 143 lines · 0 tokens per session scan A 857de9b8b0f0
cleanup-dashboards is a command published in the GitHub repository mixpanel/ai-plugins (15 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,893 tokens. 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.
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