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/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/health)<a href="https://agentmods.dev/commands/aurora-neuro/aurora-agent/health"><img src="https://agentmods.dev/badge/commands/aurora-neuro/aurora-agent/health/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/aurora-neuro/aurora-agent/health"><img src="https://agentmods.dev/badge/commands/aurora-neuro/aurora-agent/health.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 | $0.00023 | $0.00359 |
| Opus 5 | $0.00012 | $0.00179 |
| Sonnet 5 | $0.00005 | $0.00072 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
health scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
2. Fetch, with curl: `/v1/operations/snapshot?after=0&limit=15`, What it actually says
Run a one-pass operations health check against the aurora-agent gateway.
- Gateway base:
$ARGUMENTSorhttp://127.0.0.1:8787. Bearer token: theAURORA_GATEWAY_TOKENenvironment variable, or ask the operator. If the gateway is not running, say so and offer the launch line from docs/HTTP_API.md instead of starting services unprompted. - Fetch, with curl:
/v1/operations/snapshot?after=0&limit=15,/v1/operations/gates?after=0&limit=40,/v1/recovery. - Report: mission totals and event metrics; per-group gate states (expect
insufficient_evidenceuntil evaluator evidence exists — that is a posture, not a defect);readiness_claimedverbatim wherever it appears; persistence checkpoint presence and integrity;automatic_resumeand thedoes_not_restorelists; refused-event counts; and the system's ownguarantees/non_claims/operator_actionsarrays verbatim. - Never summarize the posture as "healthy" or "ready" — summarize it as what the evidence shows, name what was not inspected (bounded event pages), and surface the gate policy's decision rule if dispatch questions come up.
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.
- 10d ago First seen · 23 lines · 23 tokens per session scan A 474983dc9c0c
health is a command published in the GitHub repository AURORA-NEURO/aurora-agent (1 stars, last pushed today), licensed Apache-2.0. It adds 23 tokens to every session and 359 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
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.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.