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/sairam0424/MindForgeWrote 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/sairam0424/mindforge/cross-review)<a href="https://agentmods.dev/commands/sairam0424/mindforge/cross-review"><img src="https://agentmods.dev/badge/commands/sairam0424/mindforge/cross-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/sairam0424/mindforge/cross-review"><img src="https://agentmods.dev/badge/commands/sairam0424/mindforge/cross-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 | $0.00012 | $0.00146 |
| Opus 5 | $0.00006 | $0.00073 |
| Sonnet 5 | $0.00002 | $0.00029 |
| Haiku 4.5 | $0.00001 | $0.00015 |
Grade A, and why
cross-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.
What it actually says
MindForge v2 — Cross-Review Command
Usage: /mindforge:cross-review [--phase N] [--models list] [--focus area]
Purpose
Get the same code diff reviewed by multiple AI models simultaneously. Claude finds what Claude finds. GPT-4o finds what GPT-4o finds. Consensus findings = high confidence issues.
Round 1: Primary (Claude)
Senior architect review.
Round 2: Adversarial (GPT-4o)
Critical security and edge case review.
Synthesis
Consensus detector filters findings.
Verdict is gating for /mindforge:ship.
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 · 22 lines · 12 tokens per session scan A 4f737944a1ea
cross-review is a command published in the GitHub repository sairam0424/MindForge (0 stars, last pushed 5d ago), licensed MIT. It adds 12 tokens to every session and 146 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
review
Send the current git diff to Antigravity (agy) for code review. Optional focus text to steer the review.
p5-review-sprint
Runs ONE final code review over the entire sprint at sprint end, with a stronger model, to catch issues that single-story reviews structurally cannot see: cross-story interactions, the schema/data model as a whole, conformance to ADRs and Constitution Inviolables, and consistency with prior lessons/instincts.
clean-check
Analyze code for cleanliness issues (unused code, comment quality, formatting, naming, complexity). Delegates to the code-cleanliness agent.
pr
Open a Pull Request on GitHub following team conventions. Draft by default.
review
Adversarial staff-level review of a System Design Doc. Interrogates the 10 staff questions, finds cost and failure risks, returns a verdict. Sensors and evals gate.
review
Verification review of a named agent's claim. Ends in acquittal or replacement.