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/dinglebear-ai/axon/axon-deploygit clone --depth 1 https://github.com/dinglebear-ai/axonWrote 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/dinglebear-ai/axon/axon-deploy)<a href="https://agentmods.dev/commands/dinglebear-ai/axon/axon-deploy"><img src="https://agentmods.dev/badge/commands/dinglebear-ai/axon/axon-deploy.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 | $0.00024 | $0.00205 |
| Opus 5 | $0.00012 | $0.00102 |
| Sonnet 5 | $0.00005 | $0.00041 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
axon-deploy 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 4d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 4d ago First seen · 25 lines · 24 tokens per session scan A 7b18ca842b4c
axon-deploy is a command published in the GitHub repository dinglebear-ai/axon (4 stars, last pushed yesterday), licensed AGPL-3.0. It adds 24 tokens to every session and 205 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
whatneedsdoing
One honest "what needs doing" across the two work substrates — repo dev work (docs/backlog/ + open gripes + open GitHub PRs + Dependabot alerts) and the prod factory queue (open/doable todos) — plus a repo-hygiene scan (migration-number collisions · backlog lint · memory-index lint), a prod system-health read…
go
Implement the agreed spec, ship to main, and deploy to the cluster — the dark-factory one-keystroke. Run from inside a feature worktree.
land
End-of-session wrap-up — commit WIP, sync onto main, gate against the worktree, then squash-merge to main. Runs the deterministic scripts/ship. Run from inside a feature worktree.
next
Summarize the next logical steps and emit both a /compact retention argument and a copy-paste recovery prompt to resume after compacting. Use before compacting, or any time you want a clean-context restart point.
rebase
Fast-forward or rebase the current worktree's branch onto main. Deterministic script for the clean path; LLM steps in only to resolve real conflicts or ask you.
qland
Quick-land — commit WIP, sync onto main, squash-merge WITHOUT running the gate. For burst-landing many in-flight worktrees under gate congestion; finish the burst with one /go (full gate + deploy). Run from inside a feature worktree.