Big-AGI is an open-source workspace for using multiple AI models through chat and other AI functions. It is intended for engineers, founders, researchers, and other users who want to work with AI personas, model comparisons, image generation, voice, documents, and code-related features. The catalogue entries provide commands, instructions, and a skill for working with Big-AGI.
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/enricoros/big-AGIWrote 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/enricoros/big-agi/review-inflight)<a href="https://agentmods.dev/commands/enricoros/big-agi/review-inflight"><img src="https://agentmods.dev/badge/commands/enricoros/big-agi/review-inflight/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/enricoros/big-agi/review-inflight"><img src="https://agentmods.dev/badge/commands/enricoros/big-agi/review-inflight.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 | $0.00011 | $0.00372 |
| Opus 5 | $0.00005 | $0.00186 |
| Sonnet 5 | $0.00002 | $0.00074 |
| Haiku 4.5 | $0.00001 | $0.00037 |
Grade A, and why
review-inflight 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 11d 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
Review the current in-flight changes in the big-agi-private repository (dev branch, continuously rebased ~1800 commits on top of main).
Step 1: Scope and read
git diff --stat + git status for breadth. Then full git diff (if empty: git diff --cached, then git diff HEAD~1).
For every file in the diff, read surrounding context in the actual source file - the diff alone hides bugs in adjacent untouched code.
Step 2: Reverse-engineer the intent
From the diff, determine the what, how, and why. Present this concisely so the author can confirm or correct, but don't stop here, continue to the full review in the same response.
Step 3: Validate
Run tsc --noEmit --pretty and npm run lint (in parallel). Report any errors with the review.
If the diff removes/renames identifiers, grep the codebase for stale references to the OLD names. This catches broken guards, stale imports, and incomplete migrations.
Step 4: Deep review
Evaluate every file in the diff. Leave no rocks unturned - correctness, coherence, completeness, excess, generalization, maintenance burden, codebase consistency, etc.
Step 5: Prioritized next steps
Think about what happens when the next developer touches this code. Rank findings by severity (bug > correctness > cleanup > cosmetic). Be specific about what to change and where.
Remember: design values for this codebase: orthogonal features, features that generalize well, modularized and reusable code, type-discriminated data, optimized code, zero maintenance burden. Minimize future pain, etc.
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.
- 11d ago First seen · 35 lines · 11 tokens per session scan A 1d5abac4dd8b
review-inflight is a command published in the GitHub repository enricoros/big-AGI (7,119 stars, last pushed yesterday), licensed MIT. It adds 11 tokens to every session and 372 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 2026-08-30.
Other commands, from other repositories
concilium-brainstorm
Run a Concilium BRAINSTORMING campaign — open-book cross-model idea brainstorminging with a live read-only copy of the project's database per seat. Findings replayed, ideas flow to human-reviewed dossiers, never orchestrator-curated.
concilium-forge
Run a Concilium FORGE round — cross-model idea generation against an open question, with a shared register. Nothing is judged or voted on.
concilium-review
Route a claim, or the uncommitted diff, to the cross-model Concilium reviewer.
lens-reviewer
Use the @lens-reviewer agent to review the following code: $ARGUMENTS.
raven-critic
Use the @raven-critic agent to critique the following plan, spec, or design: $ARGUMENTS.
zen-simplifier
Use the @zen-simplifier agent to simplify the following code without changing behavior: $ARGUMENTS.