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/alebgl77/claude-incWrote 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/alebgl77/claude-inc/standup)<a href="https://agentmods.dev/commands/alebgl77/claude-inc/standup"><img src="https://agentmods.dev/badge/commands/alebgl77/claude-inc/standup/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/alebgl77/claude-inc/standup"><img src="https://agentmods.dev/badge/commands/alebgl77/claude-inc/standup.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 | $0.00000 | $0.00185 |
| Opus 5.5 | $0.00000 | $0.00074 |
| Sonnet 5.5 | $0.00000 | $0.00037 |
| Haiku 4.5 | $0.00000 | $0.00018 |
Grade A, and why
standup 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 23d 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 the Monday-morning standup of Claude, Inc. on the current working directory. Optional focus: $ARGUMENTS
- Skim the project first: README, file structure, anything recently changed.
- Launch all 9 department agents, including Growth, in parallel via the Task tool. Each VP returns at most 4 bullets: 2 observations, 1 opportunity, 1 recommended next action, strictly within its domain.
- Compile the Standup Board: one row per department (Department | Status | Opportunity | Next action).
- Close with CEO's pick: the single highest-leverage action for today, and why.
Keep it tight. A standup that takes longer than the work is a fired standup.
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.
- 23d ago Changed 979744dd1de4
- 1mo ago First seen · 14 lines · 0 tokens per session scan A 40452e43e936
standup is a command published in the GitHub repository alebgl77/claude-inc (19 stars, last pushed 24d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 185 tokens. 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 2026-08-30.
Other commands, from other repositories
run-daily
Trigger the daily digest now (background via OS scheduler) and monitor until completion.
replay
Summarize one Agent Monitor session by id — header plus a concise transcript recap.
firm
Convene your standing AI staff — memos on every beat, a board session without you, minutes with dissent preserved.
promote
The promote loop — scan your transcripts for what you keep asking, pick a pattern, draft the skill, test its triggers, publish the release package.
ship-a-feature
Workflow recipe — take a feature idea from fuzzy brief to launch plan by chaining 5 skills.
setup-context
Set up your Skill Memory — a pm-context.md every skill reads so outputs come back tailored to you.