ai-engineering-review

ai-engineering-review is a skill for Claude Code from duc01226/EasyPlatform. It costs 56 tokens per session (14,220 once invoked), scanned B, original, MIT.

A review procedure for changes involving artificial intelligence, such as language-model calls, prompts, agents, tools, retrieval, tests, or safety rules. It checks them against an AI-engineering process and reports evidence-based findings by consequence.

In plain words
What is it for?
Use it to review AI features, model prompts, agent workflows, retrieval systems, tool access, evaluations, safety controls, or machine-learning changes.
Why use it?
It helps find meaningful problems in AI-related plans or code instead of treating every change like ordinary software. It can be skipped when no model-related work is involved.

Skill for Claude Code

Written for Claude Code: $ARGUMENTS substitution. Also seen: reads .claude/ paths; mentions subagents; installed under .agents/ (shared by several agents).

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is node .claude/scripts/ai-signal-scan.cjs --json # working tree vs HEAD plus untracked; also --staged, --unstaged, --files a b c.

Good fit Use it to review AI features, model prompts, agent workflows, retrieval systems, tool access, evaluations, safety controls, or machine-learning changes.

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Install

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.

Clone the repo
git clone --depth 1 https://github.com/duc01226/EasyPlatform
agentmods
npx agentmods add skills/duc01226/easyplatform/ai-engineering-review

Made for: Claude Code.

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README.md
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Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 14,220 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 3d ago against content hash e5a2521f267a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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
.agents/skills/ai-engineering-review/SKILL.md · 509 lines

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.md and execute it with Codex tools; no separate Skill tool 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_agent subagent(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-only alias); --fix-loop enables review → validate → authorized fix → fresh re-review, default cap 3. --fix-loop --loop-owner=caller returns a read-only pass to the caller that owns fixes/re-review. Create triage, review, validation and re-review todo tasks first; apply the carried review-policy contract 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: set in_progress when step starts, set completed when 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 skipped and report clean; never run an AI review to manufacture coverage.

Read the full file on GitHub · 509 lines

Changes

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.

  1. 3d ago First seen · 509 lines · 56 tokens per session scan B e5a2521f267a

Subscribe to this mod's changes

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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