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/athola/claude-night-marketWrote 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/athola/claude-night-market/structured-review)<a href="https://agentmods.dev/commands/athola/claude-night-market/structured-review"><img src="https://agentmods.dev/badge/commands/athola/claude-night-market/structured-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/athola/claude-night-market/structured-review"><img src="https://agentmods.dev/badge/commands/athola/claude-night-market/structured-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 | $0.00014 | $0.00880 |
| Opus 5 | $0.00007 | $0.00440 |
| Sonnet 5 | $0.00003 | $0.00176 |
| Haiku 4.5 | $0.00001 | $0.00088 |
Grade A, and why
structured-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 7d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Start Review Workflow
Initializes a structured review workflow using imbue's core methodology: context establishment, scope inventory, evidence capture, and deliverable structuring.
Usage
# Start review of current branch
/structured-review
# Review specific target
/structured-review src/auth/
# Review with specific focus
/structured-review --focus security src/api/
What It Does
- Establishes Context: Confirms repository, branch, and comparison baseline
- Inventories Scope: Lists relevant artifacts for review
- Prepares Evidence Log: Initializes tracking for commands and citations
- Structures Deliverables: Sets up report template with sections
Workflow Integration
This command orchestrates multiple imbue skills:
review-core- Core workflow scaffoldingproof-of-work- Reproducible evidence capturestructured-output- Consistent deliverable formattingdiff-analysis- Change categorization (if diffs involved)
Examples
/structured-review
# Output:
# Review Workflow Initialized
# ===========================
# Repository: my-project
# Branch: feature/auth-overhaul
# Baseline: main (3 commits behind)
#
# TodoWrite items created:
# - [ ] review-core:context-established
# - [ ] review-core:scope-inventoried
# - [ ] review-core:evidence-captured
# - [ ] review-core:deliverables-structured
/structured-review src/api --focus performance
# Scoped review with performance focus
Output
Creates structured review scaffold with:
- Context summary (repo, branch, baseline)
- Scope inventory (files, configs, specs)
- Evidence log template
- Deliverable outline
Feature Review Mode
When invoked with --mode feature or --features, structured-review runs a feature-focused review workflow (formerly the standalone /feature-review command). This mode discovers, classifies, scores, and suggests features using evidence-based prioritization.
Feature Review Usage
# Full feature review: inventory, score, suggest
/structured-review --mode feature
# Only inventory current features
/structured-review --mode feature --inventory
# Generate new feature suggestions
/structured-review --mode feature --suggest
# Create GitHub issues for accepted suggestions
/structured-review --mode feature --suggest --create-issues
# Validate configuration file
/structured-review --mode feature --validate-config
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.
- 7d ago First seen · 127 lines · 14 tokens per session scan A f319c8457752
structured-review is a command published in the GitHub repository athola/claude-night-market (337 stars, last pushed today), licensed MIT. It adds 14 tokens to every session and 880 once invoked, about $0.0001 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 2026-09-03.
Other commands, from other repositories
claudex
Claude writes, Codex reviews, iterate until both agree — then ship with love.
debate
The debate — Claude and Codex argue a design decision from opposite corners, you arbitrate.
stats
Agreement stats — how often do Claude and Codex actually agree in this repo?
diff-review
AI-powered diff review for Solana-specific issues and code quality.
demo
A two-minute guided duet — plants bugs in a throwaway repo and lets Claude and Codex argue about them.
pr-issue-resolve
Follow these steps to analyze suggested changes (e.g., review comments, inline suggestions, or requested modifications) in a GitHub Pull Request (PR) and resolve them efficiently. The goal is to review, understand, plan fixes, apply changes, test, and update the PR while maintaining code quality and collaboration.