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/aurora-neuro/aurora-agent/preflightgit clone --depth 1 https://github.com/AURORA-NEURO/aurora-agentWrote 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/aurora-neuro/aurora-agent/preflight)<a href="https://agentmods.dev/commands/aurora-neuro/aurora-agent/preflight"><img src="https://agentmods.dev/badge/commands/aurora-neuro/aurora-agent/preflight.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 5.1 | $0.00020 | $0.00296 |
| Opus 5 | $0.00010 | $0.00148 |
| Sonnet 5 | $0.00004 | $0.00059 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
preflight 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
Preflight a mission against the aurora-agent backend without executing it.
- If
$ARGUMENTSnames a JSON file, read it as the mission request. Otherwise use the demo mission from the mission-lifecycle skill (fiber_compile on the reference fixtures; remember every step needsdomainandcapability). - Preferred transport: the
agent_missionMCP tool is EXECUTION — do NOT use it here. Preflight goes through the HTTP gateway:POST /v1/missions/preflighton a runningbioprism-api(launch line is in the aurora-agent docs/HTTP_API.md). If no gateway is running, say so and offer the launch command rather than executing anything. - Report verbatim:
dispatch(expect"not_started"), the plan digest, the wave decomposition,operations_evidence.decision,acceptance_required/present/valid,dispatch_prerequisite, each group'sgate_stateand missing gates, andreadiness_claimed. A complete evidence set still requires human review — never present preflight success as authorization to dispatch.
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 · 20 tokens per session scan A 9d6835699ed6
preflight is a command published in the GitHub repository AURORA-NEURO/aurora-agent (1 stars, last pushed 2d ago), licensed Apache-2.0. It adds 20 tokens to every session and 296 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 2026-08-31.
Other commands, from other repositories
OPSX: Propose
Propose a new change - create it and generate all artifacts in one step.
10-optimization-finalization
Comprehensive Optimization: Multi-agent optimization, PRD improvement, documentation consolidation, and production-ready finalization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.