Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/duc01226/EasyPlatformnpx agentmods add skills/duc01226/easyplatform/ai-engineering-reviewWrote 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/duc01226/easyplatform/ai-engineering-review)<a href="https://agentmods.dev/skills/duc01226/easyplatform/ai-engineering-review"><img src="https://agentmods.dev/badge/skills/duc01226/easyplatform/ai-engineering-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.
<a href="https://agentmods.dev/skills/duc01226/easyplatform/ai-engineering-review"><img src="https://agentmods.dev/badge/skills/duc01226/easyplatform/ai-engineering-review.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 | $0.00056 | $0.14220 |
| Opus 5.5 | $0.00022 | $0.05688 |
| Sonnet 5.5 | $0.00011 | $0.02844 |
| Haiku 4.5 | $0.00006 | $0.01422 |
Grade B, and why
ai-engineering-review scanned grade B with 1 finding 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 3d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
- Optional: when a changed call site, prompt or tool has a high-risk blast radius grep may miss and `.code-graph/graph.db` exists, `python .claude/scripts/code_graph trace <file> --direction both --json` (`--node-mode fi How it starts
The opening of the file, as written. The whole thing — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex compatibility note:
- Invoke
$skill-name: load the registered.agents/skills/<name>/SKILL.mdand execute it with Codex tools; no separateSkilltool or Claude mode switch is needed. Author.claude/**; mirrors are generated.- Track steps before execution and keep progress/evidence current. Explicit skill requests execute their protocol. Use the native question tool for required questions.
- Map foreign tool names to available host capabilities. A name mismatch needs no approval; an actually unavailable required capability stops with its step/evidence and a user question before adaptation.
- Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required
spawn_agentsubagent(s) for that task.- Preserve protocol order unless the user approves a deviation. Workflows use
$start-workflow: gates stay fixed; other steps may flex with logged reasons and evidence.
Quick Summary
Review modes: For review/audit work, standalone defaults to
--review-only(--report-onlyalias);--fix-loopenables review → validate → authorized fix → fresh re-review, default cap 3.--fix-loop --loop-owner=callerreturns a read-only pass to the caller that owns fixes/re-review. Create triage, review, validation and re-review todo tasks first; apply the carriedreview-policycontract for LOW deferral and user-approved bounded extensions. Non-review modes and terminal findings validation keep their own dispatch.
Goal: Review a plan or a change that calls a model — LLM calls, prompts, agents, tool use, RAG, MCP, evals, guardrails, fine-tuned or classical ML — against the AI-engineering protocol, and report evidence-backed findings ranked by real consequence. Skip when nothing in scope touches a model.
Summary:
- Load protocols and project policy → detect AI surfaces and skip when none → map kind, autonomy, data, sinks and trifecta legs.
- Check framing (
AF-1–AF-6) → nine focused engineering passes (AE-1.1–AE-9.4) → conditional RAG / agent / ML sweeps → verify provider facts from current docs. - Write the AI Gate Report → validate findings with
$why-review→ report, or repair and freshly re-review under standalone--fix-loop. Code and plan modes share this authority rule.
[BLOCKING] Execute skill steps in declared order. NEVER skip, reorder, or merge steps without explicit user approval. Inside a workflow run,
$start-workflow→ Workflow-step mode bounds this rule. [BLOCKING] Before each step or sub-skill call, update todo tracking: setin_progresswhen step starts, setcompletedwhen step ends. [BLOCKING] Every completed/skipped step MUST include brief evidence or explicit skip reason. [BLOCKING] If Task tools are unavailable, create and maintain an equivalent step-by-step plan tracker with the same status transitions.
Default scope: The change set under review — uncommitted changes (staged + unstaged) by default, or the branch / PR range when a review base exists (--base <the review base>) — that the AI-signal scan or the signal-grep fallback marks as an AI surface, expanded from each file to the whole surface it belongs to (call site → prompt → tools → sinks → data sources). Override: specify files, directories, a surface, or a plan path.
Modes: --mode=code (default) reviews source, prompts, tool schemas, config and tests · --mode=plan reviews a plan, spec or design against AF-1–AF-6 and returns plan gaps plus REQUIRED plan additions. Both default to read-only; standalone --fix-loop authorizes scoped repairs followed by fresh review.
CONDITIONAL — SKIP when no AI-feature surface is in scope. In workflow context this skill is SKIPPED when Phase 1 finds no AI surface. Standalone with no AI surface → announce
No AI-feature surface detected — ai-engineering-review skippedand report clean; never run an AI review to manufacture coverage.
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.
- 3d ago First seen · 509 lines · 56 tokens per session scan B e5a2521f267a
ai-engineering-review is a skill published in the GitHub repository duc01226/EasyPlatform (12 stars, last pushed yesterday), licensed MIT. It adds 56 tokens to every session and 14,220 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-18.
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