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/wildware-uk/agent-dashboard/dashboard-onlinegit clone --depth 1 https://github.com/wildware-uk/agent-dashboardWrote 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/wildware-uk/agent-dashboard/dashboard-online)<a href="https://agentmods.dev/commands/wildware-uk/agent-dashboard/dashboard-online"><img src="https://agentmods.dev/badge/commands/wildware-uk/agent-dashboard/dashboard-online.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 | $0.00018 | $0.00259 |
| Opus 5 | $0.00009 | $0.00130 |
| Sonnet 5 | $0.00004 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
dashboard-online 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 yesterday.
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.
What it actually says
Go online on the Agent Dashboard for this run.
register_sessionwithmetafilled in from the real environment — the host, the working directory, and the model you are. Keep thesession_idand theheartbeat_interval_sfor the rest of the session.- Decide the project.
$ARGUMENTSnames it if anything was passed; otherwise use this repository's name. Calllist_projectsfirst to see whether it already exists, thencreate_project— it is idempotent on slug, so this is safe either way. - Post one short
post_updatesaying what you are here to do, with thesession_idon it. One card, not a plan. - Report back to me: the project slug, the session id, and the heartbeat interval you must keep.
From here on, heartbeat on that interval, post when something is worth a glance,
and request_input rather than guessing. Read the agent-dashboard skill if you
have not.
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.
- yesterday First seen · 23 lines · 18 tokens per session scan A a3099ca5b3d6
dashboard-online is a command published in the GitHub repository wildware-uk/agent-dashboard (0 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 259 once invoked, about $0.0001 per session on Opus 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 2026-09-04.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.