Borrowing it
Nothing to install: this file belongs to a777ance/claude-code-homelab. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/a777ance/claude-code-homelab/main/.claude/commands/workout.mdgit 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/workout)<a href="https://agentmods.dev/commands/a777ance/claude-code-homelab/workout"><img src="https://agentmods.dev/badge/commands/a777ance/claude-code-homelab/workout/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/a777ance/claude-code-homelab/workout"><img src="https://agentmods.dev/badge/commands/a777ance/claude-code-homelab/workout.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 | $0.00055 | $0.00652 |
| Opus 5.5 | $0.00022 | $0.00261 |
| Sonnet 5.5 | $0.00011 | $0.00130 |
| Haiku 4.5 | $0.00006 | $0.00065 |
Grade A, and why
workout 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 2mo 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run the in-harness Jury routine on whatever the user handed you. Be forgiving about the input — a math problem, a factual call, a design decision, or open-ended prose are all fair game. Do NOT make the user reformat anything, and do NOT ask clarifying questions unless the prompt is truly unintelligible.
Prompt: $ARGUMENTS
1. Warm-up — size the question (instant)
Read the prompt and decide its shape:
- Discrete answer (a number, a name, a label, yes/no, "which of these") →
votable. Make sure the question ends with a canonical
End with 'ANSWER: <x>'.instruction (append it if it's missing) so draws tally by exact match. - Open-ended prose (explain, design, weigh trade-offs) → self-consistency voting is weaker (answers won't cluster on exact match). Still empanel a panel for the judgment, but report the result as where the panel leans, not a hard verdict — and say so.
2. Empanel the in-harness jury
Empanel 5 concurrent juror subagents (Task tool, subagent_type: "juror")
on the prompt in a single message, each with a different answer-preserving
framing (plain / skeptic / restate / cross-check / avoid-the-trap) so the draws
decorrelate by construction. Collect their ANSWER: lines, normalize, and take
the plurality. On a 3–2 / no-majority split, empanel 4 more (to 9, varying
framings) once, then stop.
For a measurable or repeatable task — where you'd want a measured jury size and a Dirichlet stopping rule rather than a fixed fan-out — the statistical jury tool (
jury_claude.py) lives in the localDNS repo under04-user-services/ai-orchestration/jury-claude/. Use it there; this command is the keyless, in-harness routine.
3. Cool-down — report honestly
Give the verdict, the tally, and a one-line confidence read (unanimous / strong majority / split). If it hit the 9-juror ceiling or split, say so plainly — a jury that won't converge means the question is genuinely contested, and that is the finding; never dress a split as a clean verdict. If the panel agrees strongly but you suspect it's confidently wrong, flag possible systematic bias — a vote can't fix that.
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.
- 2mo ago First seen · 49 lines · 0 tokens per session scan A feb5f0bd96c4
workout is a command published in the GitHub repository a777ance/claude-code-homelab (2 stars, last pushed 2mo ago), licensed MIT. It adds 55 tokens to every session and 652 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.
refactor
Systematic code refactoring with smart scope detection.
init
Install the formatters this repository needs, with every command visible before it runs.
spec-trace-requirements
You are verifying that implementation changes trace back to specification requirements.
merge-conflict-analysis
You are analyzing merge conflicts for PR #${{ pr-number }}.
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).