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 ondrej-svec/heart-of-gold-toolkit/plugin install deep-thoughtWrote 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/ondrej-svec/heart-of-gold-toolkit/expert-panel)<a href="https://agentmods.dev/skills/ondrej-svec/heart-of-gold-toolkit/expert-panel"><img src="https://agentmods.dev/badge/skills/ondrej-svec/heart-of-gold-toolkit/expert-panel/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/ondrej-svec/heart-of-gold-toolkit/expert-panel"><img src="https://agentmods.dev/badge/skills/ondrej-svec/heart-of-gold-toolkit/expert-panel.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 | $0.00094 | $0.06066 |
| Opus 5 | $0.00047 | $0.03033 |
| Sonnet 5 | $0.00019 | $0.01213 |
| Haiku 4.5 | $0.00009 | $0.00607 |
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 12d 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 — 518 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Expert Panel
Apply multiple analytical frameworks to content in parallel, then synthesize the findings into convergent signals, productive disagreements, and a prioritized action list.
What this skill is — and what it is not
Each lens is a named analytical framework (evolutionary design, learning science, practitioner reality-check, etc.) with traceable sources (books, articles, published talks). The skill applies those frameworks to the target content and cites specific concepts by name.
Each lens is NOT a persona impersonation. The skill never claims "Fowler says" or "Newport would argue." It claims "applying the evolutionary-design framework (source: Fowler, Refactoring 2nd ed.) to this content, the following pattern emerges." The framework is the lens. The author is the source. The AI is the reader applying the framework — like a careful graduate student who read the book, not like the author in the room.
This distinction matters because:
- AI cannot faithfully represent what a specific person thinks. It can apply their published frameworks.
- Persona impersonation fabricates opinions and creates false authority. Framework application is traceable and falsifiable.
- If a framework produces a wrong finding, you can check it against the source material. If a persona produces a wrong finding, there's nothing to check — it's just plausible-sounding noise.
Grounding tiers
The skill supports three tiers of grounding, from highest to lowest fidelity:
Tier 1 — User-provided reference material (highest fidelity)
The user provides the actual source texts (book chapters, blog posts, talk transcripts, papers) in a references/ directory or via --references <path>. Each lens reads its assigned reference material as primary context and applies the specific arguments from that material to the target content.
This is the closest to "what would the framework's author think" because the model is reading and applying THEIR actual words, not reconstructing them from training data. Citations point to specific sections of the provided material.
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.
- 12d ago First seen · 518 lines · 94 tokens per session scan A b119968a1f72
expert-panel is a skill published in the GitHub repository ondrej-svec/heart-of-gold-toolkit (19 stars, last pushed 22d ago), licensed MIT. It adds 94 tokens to every session and 6,066 once invoked, about $0.0005 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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