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/sarkarsaurabh27/agent-loop-learningWrote 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/rules/sarkarsaurabh27/agent-loop-learning/review-agent)<a href="https://agentmods.dev/rules/sarkarsaurabh27/agent-loop-learning/review-agent"><img src="https://agentmods.dev/badge/rules/sarkarsaurabh27/agent-loop-learning/review-agent/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/rules/sarkarsaurabh27/agent-loop-learning/review-agent"><img src="https://agentmods.dev/badge/rules/sarkarsaurabh27/agent-loop-learning/review-agent.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 | $0.00033 | $0.00552 |
| Opus 5.5 | $0.00013 | $0.00221 |
| Sonnet 5.5 | $0.00007 | $0.00110 |
| Haiku 4.5 | $0.00003 | $0.00055 |
Grade A, and why
review-agent 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.
What it actually says
Review Agent Design
When the user asks to review, audit, or check an agent design:
-
Explore the current repo first — do not ask the user for anything yet. Search for agent-related code:
- Files matching
*agent*,*tool*,*prompt*,*chain*,*workflow*insrc/,lib/,app/, root - Imports of agent frameworks:
langchain,langgraph,openai,anthropic,autogen,crewai - System prompt definitions, tool definitions, agent loop logic
CLAUDE.md,AGENTS.md, or any README describing the agent architecture Read the relevant files. Only ask the user if no agent code is found after exploring.
- Files matching
-
Read all 9 best-practice docs from
best-practices/:01-multi-agent-orchestration.md02-worker-prompting.md03-context-and-memory.md04-tool-design.md05-verification-and-testing.md06-security-and-permissions.md07-prompt-engineering.md08-performance-and-startup.md09-benchmarks-reference.md
-
Score each dimension: ✅ Solid / ⚠️ Partial / ❌ Gap
-
Output format:
### Agent Review: [name]
**Summary** (2–3 sentences)
| # | Dimension | Score | Finding |
|---|-----------|-------|---------|
| 01 | Orchestration | ✅/⚠️/❌ | ... |
| 02 | Worker prompting | | |
| 03 | Context & memory | | |
| 04 | Tool design | | |
| 05 | Verification | | |
| 06 | Security | | |
| 07 | Prompt engineering | | |
| 08 | Performance | | |
#### Top 3 improvements
For each: What / Why (cite benchmark) / How (implementation sketch)
#### What's already strong
2–3 callouts
- Always cite benchmark numbers with source — e.g. "17.2× → 4.4× error amplification (Google DeepMind, Dec 2025)".
- Framework-agnostic analysis. Note if any finding is model-specific.
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 · 58 lines · 33 tokens per session scan A 3d0ee17a9663
review-agent is a cursor rule published in the GitHub repository sarkarsaurabh27/agent-loop-learning (3 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 552 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 2026-08-31.
Other cursor rules, from other repositories
clean-code-reviewer
Eliminates technical debt using SOLID, DRY, YAGNI, and Addy Osmani production-grade engineering principles. / TR: SOLID, DRY, YAGNI ve Addy Osmani üretim seviyesi mühendislik ilkeleri ile kod kalitesini denetleyen yetenek.
sweep-benchmarks
Audit xrspatial modules for asv benchmark coverage gaps: missing benchmarks, backend parameterization gaps, unrepresentative inputs, broken or silently-skipped benchmarks.
code-review
BMAD BMM Agent: code-review.
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.