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/t1djani/war-roomWrote 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/t1djani/war-room/war-room-roster)<a href="https://agentmods.dev/commands/t1djani/war-room/war-room-roster"><img src="https://agentmods.dev/badge/commands/t1djani/war-room/war-room-roster/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/t1djani/war-room/war-room-roster"><img src="https://agentmods.dev/badge/commands/t1djani/war-room/war-room-roster.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 | $0.00017 | $0.00146 |
| Opus 5 | $0.00009 | $0.00073 |
| Sonnet 5 | $0.00003 | $0.00029 |
| Haiku 4.5 | $0.00002 | $0.00015 |
Grade A, and why
war-room-roster 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 12d 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
Build a project-specific roster for the war-room.
Run the discover-roster skill: reuse .servo/manifest.yaml if it exists, otherwise scan the project shallowly to infer 3–5 domains, then propose a roster of domain officers (persona + real slice + signature question) and show it to me. Ask before writing — only create .war-room/roster.yaml once I accept or adjust it.
This is optional. The default /war-room already works with five generic officers and no config; this just seats officers that read real slices of this codebase.
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.
- 12d ago First seen · 10 lines · 17 tokens per session scan A f23dc6fd585d
war-room-roster is a command published in the GitHub repository t1djani/war-room (1 stars, last pushed 2mo ago), licensed MIT. It adds 17 tokens to every session and 146 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
mega-approve
Approve the mega-plan and start feature execution. Creates worktrees and generates PRDs for each feature. Usage: /plan-cascade:mega-approve [--flow ] [--tdd ] [--confirm] [--no-confirm] [--spec ] [--first-principles] [--max-questions N] [--auto-prd] [--agent ] [--prd-agent ] [--impl-agent ].
auto
AI auto strategy executor. Analyzes task and automatically selects and executes the best strategy: direct execution, hybrid-auto PRD generation, hybrid-worktree isolated development, or mega-plan multi-feature orchestration.
worktree
Start a new task in an isolated Git worktree for parallel multi-task development. Creates a task branch, worktree directory with planning files, and leaves the main directory untouched. Usage: /plan-cascade:worktree [task-name] [target-branch]. Example: /plan-cascade:worktree feature-login main.
mega-status
Show detailed status of mega-plan execution including feature progress and story completion. Usage: /plan-cascade:mega-status.
resume
Auto-detect and resume any interrupted Plan Cascade task. Detects mega-plan, hybrid-worktree, or hybrid-auto context and routes to the appropriate resume command.
mega-edit
Edit the mega-plan interactively. Add, remove, or modify features. Usage: /plan-cascade:mega-edit.