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 t1djani/servo --skill expert-panelgit clone --depth 1 https://github.com/t1djani/servoWrote 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/t1djani/servo/expert-panel)<a href="https://agentmods.dev/skills/t1djani/servo/expert-panel"><img src="https://agentmods.dev/badge/skills/t1djani/servo/expert-panel.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 5.1 | $0.00054 | $0.00898 |
| Opus 5 | $0.00027 | $0.00449 |
| Sonnet 5 | $0.00011 | $0.00180 |
| Haiku 4.5 | $0.00005 | $0.00090 |
Grade A, and why
expert-panel 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 7d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
expert-panel
A good brainstorm is several experts, each master of a domain, who bring their knowledge and argue toward a decision. Use this when a choice has no ground truth to check against yet — a design fork, an architecture call, a tradeoff. For anything that can be checked against an oracle, use servo-gate instead; this skill is for generating and stress-testing options, not verifying them.
The failure this prevents: one model picking the first plausible option with false confidence, and a panel of same-context models agreeing with each other.
Keep it visible: open a TodoWrite list with one todo per expert (plus the dissenter and the synthesis), and mark each in_progress/completed as it reports. The human should see the panel convene and resolve, not wait on a silent block.
Procedure
-
Frame the fork. State the decision and the constraints from the context brief (
gather-context). If there is genuinely an oracle that settles it, stop — this is a gate, not a panel. -
Convene the MINIMAL relevant set — usually 2-3, not every plausibly-related domain. From the manifest's
experts, pick only the ones the decision genuinely turns on (a data fork wakes the data expert; it does not need the bot expert). Convening six experts on a three-expert problem is the waste this step must avoid.How to spawn an expert: an expert is not a named agent type — do not try to spawn an agent called
dataorhs-coaching(it does not exist and will fail). Spawn a plain general subagent and give it the expert's identity as context: the expert'ssourcesfrom the manifest (read anyskill:pointer as a file, read the listed code/notes), plus the context brief fromgather-context, plus its assigned stance. That content IS the expert. Hand it the brief so it does not re-read the whole project — it only goes deeper into its own slice. Each expert is a real, separate perspective — not you role-playing all of them. These reader-experts can run on a cheaper, faster model; reserve the strong model for the synthesis in step 5.
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.
- 7d ago First seen · 40 lines · 54 tokens per session scan A 303c1811d765
expert-panel is a skill published in the GitHub repository t1djani/servo (1 stars, last pushed 2mo ago), licensed MIT. It adds 54 tokens to every session and 898 once invoked, about $0.0003 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 2026-08-31.
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