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 majiayu000/claude-skill-registry --skill agent-cross-review-1ambda-dataops-platform-2git clone --depth 1 https://github.com/majiayu000/claude-skill-registryWrote 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/majiayu000/claude-skill-registry/agent-cross-review-1ambda-dataops-platform-2)<a href="https://agentmods.dev/skills/majiayu000/claude-skill-registry/agent-cross-review-1ambda-dataops-platform-2"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-cross-review-1ambda-dataops-platform-2/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/majiayu000/claude-skill-registry/agent-cross-review-1ambda-dataops-platform-2"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-cross-review-1ambda-dataops-platform-2.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00046 | $0.01251 |
| Opus 5 | $0.00023 | $0.00626 |
| Sonnet 5 | $0.00009 | $0.00250 |
| Haiku 4.5 | $0.00005 | $0.00125 |
Grade A, and why
agent-cross-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 9d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- agent-cross-review — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Cross-Review
Protocol for structured collaboration between specialized agents.
When to Use
- One agent reviewing another's output
- Handoff between feature and expert agents
- Validating cross-cutting concerns
- Resolving conflicting recommendations
Core Principle
Review in domain, defer on scope.
Each agent excels in their specialty. Cross-review catches blind spots without overstepping boundaries.
Review Protocol
Step 1: Scope Identification
Before reviewing, identify:
## Cross-Review Context
| Item | Value |
|------|-------|
| Reviewer | {agent-name} |
| Author | {agent-name} |
| Artifact | {file or output} |
| Review Type | Technical / Structural / Integration |
Step 2: Domain Check
| Reviewer Type | Review Focus | Defer On |
|---|---|---|
| Feature Agent | Structure, naming, coverage | Pytest patterns, DRY |
| Expert Agent | Code quality, patterns | Project conventions |
| Architecture | Boundaries, dependencies | Implementation details |
Step 3: Calibrated Feedback
Use priority tiers:
### Cross-Review: {artifact}
**CRITICAL** (blocks merge)
- [Issue affecting correctness or security]
**MAJOR** (should fix)
- [Issue affecting maintainability]
**MINOR** (nice to have)
- [Improvement suggestion]
**DEFER** (out of scope for this review)
- [Valid concern but not reviewer's domain]
Role-Specific Protocols
feature-interface-cli Reviewing expert-python
Focus Areas:
- Test file naming follows project conventions
- Coverage targets CLI-critical paths
- Integration with existing command structure
Defer To expert-python:
- Fixture design decisions
- pytest marker selection
- Test helper organization
Template:
## CLI Feature Review
### Structure
- [ ] Test files in correct location
- [ ] Naming follows test_{feature}_cmd.py
- [ ] Coverage priorities align with CLI usage
### Concerns for expert-python
- [List items needing pytest expertise]
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 261 lines · 46 tokens per session scan A c3bb22b07594
agent-cross-review is a skill published in the GitHub repository majiayu000/claude-skill-registry (604 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 1,251 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 2026-09-03.
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