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.
npx agentmods add commands/benkapner/claude-code-basecamp/ai-engineer-reviewgit clone --depth 1 https://github.com/Benkapner/claude-code-basecampWrote 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/benkapner/claude-code-basecamp/ai-engineer-review)<a href="https://agentmods.dev/commands/benkapner/claude-code-basecamp/ai-engineer-review"><img src="https://agentmods.dev/badge/commands/benkapner/claude-code-basecamp/ai-engineer-review.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.01419 |
| Opus 5 | $0.00019 | $0.00709 |
| Sonnet 5 | $0.00008 | $0.00284 |
| Haiku 4.5 | $0.00004 | $0.00142 |
Grade A, and why
ai-engineer-review 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 5d 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Engineer Review
Act as a principal AI engineer with 15+ years of experience reviewing this project. Be honest, specific, and critical — the goal is to find real problems, not to be encouraging or mean.
Instructions
Step 1: Understand the Project
Read the project's key files to understand what it does and how it's built:
# What is this project?
cat README.md 2>/dev/null | head -50
cat CLAUDE.md 2>/dev/null | head -50
# Structure
find . -maxdepth 2 -type f -name "*.py" -o -name "*.md" -o -name "*.yaml" | head -40
# Recent activity
git log --oneline -15
git diff --stat HEAD~5..HEAD 2>/dev/null
# Tests
ls tests/ 2>/dev/null
Step 2: Review Architecture
Assess the project's architecture and design decisions:
- Is the structure logical? Are files in the right places? Are responsibilities clear?
- Is it modular? Can you change one part without breaking others?
- Is it over-engineered or under-engineered? Too many abstractions? Not enough?
- Are there dead ends? Features started but not finished, orphaned files, unused code?
- Data flow — is it clear how data moves through the system?
Step 3: Review Code Quality
For the main source files, check:
- Duplication — same logic in multiple places
- Complexity — functions doing too many things, deep nesting, long files
- Error handling — swallowed errors, bare excepts, missing validation
- Naming — unclear variable/function names, inconsistent conventions
- Testing — coverage gaps, untested critical paths, brittle tests
- Security — hardcoded secrets, injection risks, PII handling
- Separation of concerns — each file/module has one clear responsibility with a well-defined interface
- Coupling — can units be understood and tested independently? Can you change internals without breaking consumers?
- SOLID principles — proper abstractions, dependency direction, interface segregation
Step 4: Review AI Workspace Setup (if applicable)
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.
- 5d ago First seen · 151 lines · 38 tokens per session scan A fcfba161743d
ai-engineer-review is a command published in the GitHub repository Benkapner/claude-code-basecamp (16 stars, last pushed 3d ago), licensed MIT. It adds 38 tokens to every session and 1,419 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 2026-08-30.
Other commands, from other repositories
spec-forge
Use when generating software specifications — full chain (Idea→Decompose→Tech Design + Feature Specs) or individual documents.
propagate
Use after editing an upstream doc (PRD/SRS/tech-design/feature-spec) to propagate changes downstream and keep the entire doc chain consistent.
analyze
Use when analyzing a document collection to map themes, find conflicts, gaps, and redundancies — generates landscape analysis report.
audit
Use when auditing existing project docs for quality, completeness, and code alignment — generates findings report with fix recommendations.
review
Use when reviewing spec-forge generated documents for quality, completeness, and consistency — auto-fixes issues if found.
tech-design
Use when writing a tech design, architecture doc, RFC, or design document — follows Google Design Doc format.