Borrowing it
Nothing to install: this file belongs to sourjya/kiro-rails. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sourjya/kiro-rails/main/.claude/commands/review-ai-agent-surface.mdgit clone --depth 1 https://github.com/sourjya/kiro-railsWrote 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/commands/sourjya/kiro-rails/review-ai-agent-surface)<a href="https://agentmods.dev/commands/sourjya/kiro-rails/review-ai-agent-surface"><img src="https://agentmods.dev/badge/commands/sourjya/kiro-rails/review-ai-agent-surface/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/commands/sourjya/kiro-rails/review-ai-agent-surface"><img src="https://agentmods.dev/badge/commands/sourjya/kiro-rails/review-ai-agent-surface.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 | $0.00035 | $0.05233 |
| Opus 5 | $0.00017 | $0.02617 |
| Sonnet 5 | $0.00007 | $0.01047 |
| Haiku 4.5 | $0.00003 | $0.00523 |
Grade A, and why
review-ai-agent-surface 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 11d 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 — 333 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Before scanning, read these context documents if they exist:
docs/security/THREAT_MODEL.md- trust boundaries, what is in/out of scopedocs/decisions/ADRs - architectural decisions that explain intentional agent design (confidence thresholds, autonomous-action boundaries, tool scoping rationale)docs/security/SECURITY_LOG.md- previously reviewed findings (avoid re-reporting)- Any agent capability registry, tool catalog, system prompt files, or intent/approval schema definitions
Use documented trust boundaries and intentional design decisions to skip findings on explicitly trusted paths. Do not flag documented exceptions as findings.
Act as a principal-level AI security architect and agentic systems auditor performing a comprehensive review of an AI-powered or agentic feature.
Your mission is not to verify that the agent produces useful output. It is to determine whether the agent can be made to act against the user, the tenant, or the platform - whether untrusted content can redirect its goals, whether its tools can be driven beyond their intended scope, whether it inherits more privilege than the requesting user holds, whether its memory or context can be poisoned across turns or sessions, and whether every consequential action it takes is attributable, reversible, and gated to the right confidence threshold. An agent that works for a cooperative user but cannot be trusted with a hostile one is not production-ready.
This review aligns to the OWASP Top 10 for Agentic Applications (ASI01-ASI10, December 2025), the OWASP Top 10 for LLM Applications (2025), and the OWASP MCP Top 10. Finding IDs use the AISxx prefix and cross-reference the relevant ASI/LLM/MCP category.
This prompt is invoked at feature-complete time for any feature that: calls an LLM, exposes a chat or natural-language interface, runs an autonomous or semi-autonomous agent, registers or invokes tools/functions, consumes an MCP server, retrieves content into a model context (RAG), or executes model-generated code, queries, or workflows. It runs alongside review-code-security.md Tier 2, not instead of it - this prompt owns the AI-specific attack surface; the security prompt owns the conventional surface.
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.
- 11d ago First seen · 333 lines · 35 tokens per session scan A fa0c80315230
review-ai-agent-surface is a command published in the GitHub repository sourjya/kiro-rails (9 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 5,233 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.