Starlight-Intelligence-System: Skill for Claude Code

.grok/skills/excellence-review/SKILL.md

excellence-review is a skill for Claude Code from frankxai/Starlight-Intelligence-System. It costs 61 tokens per session (223 once invoked), scanned A, a copy of excellence-review, MIT.

A set of review rules that requires evidence before software work is shipped, including testing, security checks, and quality reviews.

In plain words
What is it for?
Use it to review plans, test builds, inspect web interfaces, check security, and produce before-and-after evidence.
Why use it?
It helps catch defects and weak decisions before release by requiring independent verification and documented results.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents; built for gstack.

This is frankxai/Starlight-Intelligence-System's own configuration. It tells Claude Code how to work on Starlight-Intelligence-System itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Starlight-Intelligence-System configures →

Part of the starlight-intelligence-system plugin — 8 skills, 122 commands, 7 agents, 4 hooks shipped together

Reuse

Borrowing it

Nothing to install: this file belongs to frankxai/Starlight-Intelligence-System. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/frankxai/Starlight-Intelligence-System/main/.grok/skills/excellence-review/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/frankxai/Starlight-Intelligence-System

Made for: Claude Code.

Or install starlight-intelligence-system, the plugin that ships this one along with the rest of its 8 skills, 122 commands, 7 agents, 4 hooks.

Wrote 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.

agentmods badge for excellence-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/frankxai/starlight-intelligence-system/excellence-review/github.svg)](https://agentmods.dev/skills/frankxai/starlight-intelligence-system/excellence-review)
Your own site
<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.

agentmods 80×15 button for excellence-review

Your own site · 80×15
<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>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 223 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00061 $0.00223
Opus 5.5 $0.00024 $0.00089
Sonnet 5.5 $0.00012 $0.00045
Haiku 4.5 $0.00006 $0.00022

Measured 1mo ago against content hash 06ec7e523389, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo 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.

Origin

This is a copy

100% identical to excellence-review — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.grok/skills/excellence-review/SKILL.md · 19 lines

What it actually says

Excellence Review Skill — Grok Harness

God 99 Mandate

  1. repo-mastery (read all rules files first, map cross-repo)
  2. CEO/Eng/Design plan reviews (subagent or delegate to claude)
  3. Adversarial: verification-loop + santa-method (2 independent pass required)
  4. gstack: qa (tiers quick/standard/exhaustive), browse, design-review, benchmark, canary
  5. Security: cso / security-auditor + supply chain
  6. Polish: rules-distill, skill-comply, document-release

Output format: report + before/after + artifacts. Iterate atomically.

Tools: gstack (headless browser ~100ms), terminal (tests), subagents.

Changes

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.

  1. 1mo ago First seen · 19 lines · 61 tokens per session scan A 06ec7e523389

Subscribe to this mod's changes

excellence-review is a skill published in the GitHub repository frankxai/Starlight-Intelligence-System (11 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 223 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to excellence-review, differing in 0 lines, and is treated as a copy.

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