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/a777ance/claude-code-homelab/cardiogit clone --depth 1 https://github.com/a777ance/claude-code-homelabWrote 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/a777ance/claude-code-homelab/cardio)<a href="https://agentmods.dev/commands/a777ance/claude-code-homelab/cardio"><img src="https://agentmods.dev/badge/commands/a777ance/claude-code-homelab/cardio.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 | $0.00064 | $0.00926 |
| Opus 5 | $0.00032 | $0.00463 |
| Sonnet 5 | $0.00013 | $0.00185 |
| Haiku 4.5 | $0.00006 | $0.00093 |
Grade A, and why
cardio 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.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run the self-consistency sampling doctrine inside this harness: don't consume a single warm draw where a verdict matters — empanel a jury, then let a plurality vote outvote the idiosyncratic draws.
Question to deliberate: $ARGUMENTS
First: is this the right tool?
- One-off judgment call (a design decision, "which of these is the bug", a factual call you need now) → empanel the in-harness jury below.
- Measurable or repeatable task (you have or can build a labelled set, or
you'll ask this shape of question often) → the statistical, calibrated jury tool
(
jury_claude.py, withcalibrate/form/diet/strength) lives in the localDNS repo under04-user-services/ai-orchestration/jury-claude/. Its Dirichlet stopping rule and measured jury size beat the fixed fan-out below. Come back here only for the un-repeatable calls.
Empanel the jury (concurrent vote)
-
Empanel 5 jurors at once, each with a different framing. In a single message, spawn 5 concurrent
jurorsubagents (Task tool,subagent_type: "juror"), each handed the same question ($ARGUMENTS) but a different answer-preserving framing directive, so the draws decorrelate by construction rather than by luck (identical prompts to a locked-decoding model collapse into one reasoning path). Juror 1 gets no directive (plain); jurors 2–5 get one each:- skeptic — work one step at a time; before accepting each step, ask how it could be wrong.
- restate — restate the question and list what's given vs. asked; derive from those facts only.
- cross-check — solve, then reach the answer a second way (estimate / work backwards) and reconcile.
- avoid-the-trap — name the most tempting wrong approach first, then deliberately avoid it.
The framing changes the approach, never the question or the answer. Ask each to end with a canonical
ANSWER:line. -
Tally. Collect each juror's
ANSWER:line, normalize (lowercase, strip surrounding punctuation/whitespace), and count. The plurality is the working verdict.
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 · 63 lines · 64 tokens per session scan A 530ee0f570f4
cardio is a command published in the GitHub repository a777ance/claude-code-homelab (2 stars, last pushed 25d ago), licensed MIT. It adds 64 tokens to every session and 926 once invoked, about $0.0003 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
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.
constitution
Create or update the project constitution from interactive or provided principle inputs.
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.