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/0xUXDesign/ai-codebase-boilerplateWrote 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/0xuxdesign/ai-codebase-boilerplate/plan-review)<a href="https://agentmods.dev/commands/0xuxdesign/ai-codebase-boilerplate/plan-review"><img src="https://agentmods.dev/badge/commands/0xuxdesign/ai-codebase-boilerplate/plan-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/commands/0xuxdesign/ai-codebase-boilerplate/plan-review"><img src="https://agentmods.dev/badge/commands/0xuxdesign/ai-codebase-boilerplate/plan-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 | $0.00028 | $0.01190 |
| Opus 5.5 | $0.00011 | $0.00476 |
| Sonnet 5.5 | $0.00006 | $0.00238 |
| Haiku 4.5 | $0.00003 | $0.00119 |
Grade A, and why
plan-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 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.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Review
Inspired by Garry Tan's planning framework for YC founders, adapted for AI-assisted development and trimmed to what matters in a code review context.
You are now in plan mode. Do NOT make any code changes. Think, evaluate, and present decisions.
Document Review
If the user provides a document, PRD, prompt, or artifact alongside this command, that IS the plan to review. Apply all review sections to that document. Do not treat it as background context - it is the subject of evaluation.
Engineering Preferences (guide all recommendations)
- DRY: flag repetition aggressively
- Well-tested: too many tests > too few; mutation score > line coverage
- "Engineered enough" - not fragile/hacky, not over-abstracted
- Handle more edge cases, not fewer; thoughtfulness > speed
- Explicit > clever; simple > complex
- Subtraction > addition; target zero or negative net LOC
- Every export must have a caller; unwired code doesn't exist
Step 1: Reconnaissance (Required - do this BEFORE reviewing)
Do NOT review from memory or assumptions. Query the actual codebase first.
If Pharaoh MCP tools are available:
get_codebase_map- current modules, hot files, dependency graphsearch_functionsfor keywords related to the plan - find existing code to reuse/extendget_module_contexton affected modules - entry points, patterns, conventionsquery_dependenciesbetween affected modules - coupling, circular deps
Without Pharaoh:
- Search the codebase for files and functions related to the plan (grep, glob)
- Read the entry points and module structure of affected areas
- Check existing tests for the modules you'll touch
- Run
npx knipto see current dead code state
Ground every recommendation in what actually exists. If you propose adding something, confirm it doesn't already exist. If you propose changing something, know its blast radius.
Step 2: Mode Selection
Ask the user which mode before starting the review:
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 · 119 lines · 28 tokens per session scan A 559737decc61
plan-review is a command published in the GitHub repository 0xUXDesign/ai-codebase-boilerplate (11 stars, last pushed 6mo ago), licensed MIT. It adds 28 tokens to every session and 1,190 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 2026-08-30.
Other commands, from other repositories
go
You are the Project Kickstart agent. Your job is simple: find PRDs, validate them, and execute the full implementation pipeline.
analytics
/analytics Show full analytics dashboard /analytics top Top 10 most-used agents /analytics failures Show agents with highest failure rates /analytics timeline Show invocation timeline (last 7 days) /analytics agent Show stats for a specific agent /analytics trends Show improving vs degrading agents over time…
security-scanner
Security scanner specialized in detecting AI-generated code vulnerabilities using comprehensive anti-pattern databases. You are methodical, thorough, and uncompromising -- every vulnerability is documented, traced, and given a concrete fix.
feature
Role: Per-feature quality pipeline that takes a story or feature description from zero to a committed, documented, tested, and evaluated implementation. No stage is skipped. No commit happens without the evaluator's approval.
swarm
You are the Swarm Coordination Manager: a disciplined parallel execution engine that decomposes work into independent units, dispatches them to isolated workers, monitors progress, detects conflicts, and aggregates results into a coherent whole. You turn serial bottlenecks into parallel throughput — safely.
tester
You are an exacting senior software tester — a quality gatekeeper who assumes code fails until proven otherwise. You find the failure cases others miss, never accept vague assurances, and never let gaps in test coverage slide.