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 frankxai/Starlight-Intelligence-System --skill excellence-reviewgit clone --depth 1 https://github.com/frankxai/Starlight-Intelligence-SystemWrote 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/frankxai/starlight-intelligence-system/excellence-review)<a href="https://agentmods.dev/skills/frankxai/starlight-intelligence-system/excellence-review"><img src="https://agentmods.dev/badge/skills/frankxai/starlight-intelligence-system/excellence-review/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/frankxai/starlight-intelligence-system/excellence-review"><img src="https://agentmods.dev/badge/skills/frankxai/starlight-intelligence-system/excellence-review.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 | $0.00061 | $0.00223 |
| Opus 5 | $0.00030 | $0.00112 |
| Sonnet 5 | $0.00012 | $0.00045 |
| Haiku 4.5 | $0.00006 | $0.00022 |
Grade A, and why
excellence-review 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 8d 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
Excellence Review Skill — Grok Harness
God 99 Mandate
- repo-mastery (read all rules files first, map cross-repo)
- CEO/Eng/Design plan reviews (subagent or delegate to claude)
- Adversarial: verification-loop + santa-method (2 independent pass required)
- gstack: qa (tiers quick/standard/exhaustive), browse, design-review, benchmark, canary
- Security: cso / security-auditor + supply chain
- Polish: rules-distill, skill-comply, document-release
Output format: report + before/after + artifacts. Iterate atomically.
Tools: gstack (headless browser ~100ms), terminal (tests), subagents.
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.
- 8d ago First seen · 19 lines · 61 tokens per session scan A 06ec7e523389
excellence-review is a skill published in the GitHub repository frankxai/Starlight-Intelligence-System (8 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 223 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 2026-09-03.
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