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/hbkdad/arc/acr-statusgit clone --depth 1 https://github.com/hbkdad/arcWrote 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/hbkdad/arc/acr-status)<a href="https://agentmods.dev/commands/hbkdad/arc/acr-status"><img src="https://agentmods.dev/badge/commands/hbkdad/arc/acr-status.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 | $0.00020 | $0.00215 |
| Opus 5 | $0.00010 | $0.00108 |
| Sonnet 5 | $0.00004 | $0.00043 |
| Haiku 4.5 | $0.00002 | $0.00021 |
Grade A, and why
acr-status 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.
What it actually says
Run these three commands from the repo root and report a concise summary (a few sentences plus any real problems found -- don't just paste raw output):
uv run acr doctor
uv run acr models usage
uv run acr memory calibration
For each:
acr doctor: call out any check that isn'tok, with its detail.acr models usage: state real call counts/cost per provider. If a provider shows zero calls, say so plainly rather than omitting it.acr memory calibration: state the Brier score and whether any bin's predicted confidence diverges meaningfully from its actual success rate. If there's not enough recorded evidence yet, say that plainly instead of treating "no data" as "healthy."
Do not fabricate numbers -- if a command errors, show the real error and stop there rather than guessing at what it would have said.
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 · 20 tokens per session scan A 2fb55f1778dd
acr-status is a command published in the GitHub repository hbkdad/arc (4 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 215 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
ship-and-babysit
Commit, push to origin (fork), open PR to tinyhumansai/openhuman:main, then poll every 5min for CodeRabbit comments and CI failures, resolve them, and exit when clean.
rp-build-cli
Build with rp-cli context builder → chat → implement.
test-live
Execute comprehensive real-world testing of Basic Memory using the installed version. All test results are recorded as notes in a dedicated test project.
changelog
Analyze commits and generate formatted changelog entry for a version.
spec
Manage specifications in our development process.
start-feature
Command "start-feature" from melagiri/code-insights, covering /start-feature — auto-setup feature development team, step 1: create git worktree, example (adapt the slug from $arguments), step 2: create named team and step 3: spawn pm agent.