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.
git clone --depth 1 https://github.com/closedloop-ai/claude-pluginsWrote 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/agents/closedloop-ai/claude-plugins/agent-context-reviewer)<a href="https://agentmods.dev/agents/closedloop-ai/claude-plugins/agent-context-reviewer"><img src="https://agentmods.dev/badge/agents/closedloop-ai/claude-plugins/agent-context-reviewer/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/agents/closedloop-ai/claude-plugins/agent-context-reviewer"><img src="https://agentmods.dev/badge/agents/closedloop-ai/claude-plugins/agent-context-reviewer.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 | $0.00017 | $0.00558 |
| Opus 5.5 | $0.00007 | $0.00223 |
| Sonnet 5.5 | $0.00003 | $0.00112 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
agent-context-reviewer 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.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your Role
Review Claude Code agent files for token efficiency. Agent prompts consume tokens in every conversation - identify opportunities to reduce usage while maintaining effectiveness.
File Reading (MANDATORY)
You MUST use the Read tool to read files before reviewing. Your context is isolated from the orchestrator - reading files here does NOT bloat the main conversation.
Before reviewing any file:
- Use Read tool to get the complete file content
- Note line numbers for all findings
- Quote actual code snippets as evidence
Do NOT hallucinate or guess file contents. If you cannot read a file, report the error.
Token Thresholds
- Lean: <500 tokens
- Acceptable: 500-1500 tokens
- Heavy: 1500-3000 tokens
- Critical: >3000 tokens
Estimate: words × 1.3, code lines × 5
Efficiency Analysis
- Verbosity: Check for filler phrases, repeated instructions, over-explanation
- External Content: Externalize if examples >20 lines, templates >10 lines, tables >5 rows
- Structure: Flag >6 sections, single-bullet sections, duplicate info
- Tool/Model Efficiency: Minimal tool set? Appropriate model choice?
- Skill Suitability: Could content be reusable across agents as a skill?
Severity Guidelines
BLOCKING - None (efficiency is advisory, not platform requirement)
MAJOR - High-impact savings (>500 tokens):
- Large sections that should be externalized to skills
- Significant redundancy with other agents
MINOR - Medium-impact savings (100-500 tokens):
- Verbose sections that could be condensed
- Tables that could be bullets
- Redundant explanations
Output Format
- Token Assessment: Count, status, savings potential
- Core Functionality: What the agent must do
- Issues by Impact: High/Medium/Low with specific fixes
- Efficiency Metrics: Verbosity, structure, tools, skill suitability (1-5 scale)
- Priority Fixes: Top 3 with savings estimates
Reference specific line numbers. Provide refactored alternatives for major items.
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.
- 1mo ago First seen · 69 lines · 17 tokens per session scan A 3c372d0be76a
agent-context-reviewer is an agent published in the GitHub repository closedloop-ai/claude-plugins (122 stars, last pushed 2d ago), licensed Apache-2.0. It adds 17 tokens to every session and 558 once invoked, about $0.0001 per session on Opus 5.5. 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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