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 skills add jianzhichun/emerge --skill cockpit-renderinggit clone --depth 1 https://github.com/jianzhichun/emergeWrote 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/jianzhichun/emerge/cockpit-rendering)<a href="https://agentmods.dev/skills/jianzhichun/emerge/cockpit-rendering"><img src="https://agentmods.dev/badge/skills/jianzhichun/emerge/cockpit-rendering/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/jianzhichun/emerge/cockpit-rendering"><img src="https://agentmods.dev/badge/skills/jianzhichun/emerge/cockpit-rendering.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 | $0.00034 | $0.00302 |
| Opus 5 | $0.00017 | $0.00151 |
| Sonnet 5 | $0.00007 | $0.00060 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
cockpit-rendering 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 10d 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.
What it actually says
Cockpit Rendering
Purpose
Keep cockpit code focused on serving state and validating actions. Put presentation judgment here: what sections matter, how to label them, and how to keep the operator focused on flywheel progress.
Rendering Principles
- Lead with actionable flywheel state: stable pipelines, canary risks, blocked synthesis, runner health.
- Treat cockpit copy as operator guidance, not implementation truth. Source data still comes from
/api/*and resources. - Do not invent new API fields. If a view needs data, first check existing endpoints and only propose API expansion explicitly.
- Prefer short status labels over narrative prose in tables and cards.
- Expose confidence and verification state when a recommendation could trigger operator action.
Empty States
- No stable intents: explain that repeated successful spans are needed before zero-inference execution appears.
- No runners: point to
admin-runner-operationsorrunner-install-urlinstead of embedding long setup flow in code. - Synthesis blocked: show the reason and the latest sample/job ID so the operator can inspect the right artifact.
Action Labels
Use verb-first labels: Freeze policy, Resume policy, Open runner, Resolve conflict, Retry synthesis. Avoid labels that imply human approval gates for auto-promoted paths.
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.
- 10d ago First seen · 29 lines · 34 tokens per session scan A e0a31d3bbdd4
cockpit-rendering is a skill published in the GitHub repository jianzhichun/emerge (107 stars, last pushed 4mo ago), licensed MIT. It adds 34 tokens to every session and 302 once invoked, about $0.0002 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 2026-08-30.
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