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/desplega-ai/agent-swarmWrote 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/desplega-ai/agent-swarm/todos)<a href="https://agentmods.dev/commands/desplega-ai/agent-swarm/todos"><img src="https://agentmods.dev/badge/commands/desplega-ai/agent-swarm/todos/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/desplega-ai/agent-swarm/todos"><img src="https://agentmods.dev/badge/commands/desplega-ai/agent-swarm/todos.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 | $0.00007 | $0.00195 |
| Opus 5 | $0.00003 | $0.00097 |
| Sonnet 5 | $0.00001 | $0.00039 |
| Haiku 4.5 | $0.00001 | $0.00019 |
Grade A, and why
todos 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 10d 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
Agent Personal Todos
File location: /workspace/personal/todos.md
Format
# My TODOs
## Current
- [ ] Task 1
- [x] Task 2
- [ ] Task 3
## <YYYY-MM-DD>
- [ ] Task 4
- [ ] Task 5
## Current contains ongoing tasks related to your active work — what you should be focusing on right now. Once done, move completed tasks to a section with the completion date (create it if it doesn't exist).
Keep it tidy
Regularly review and clean up: remove completed tasks from ## Current and archive them under the appropriate date section.
Fallback
If this command is used without a clear action, provide a summary of how to use the todos.md file, including the format above.
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.
- 10d ago First seen · 36 lines · 7 tokens per session scan A b6ac348d416d
todos is a command published in the GitHub repository desplega-ai/agent-swarm (754 stars, last pushed yesterday), licensed MIT. It adds 7 tokens to every session and 195 once invoked, about $0.0000 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 2026-08-30.
Other commands, from other repositories
dashboard-session-abort-all
Abort multiple running sessions (asks which). Usage /dashboard:session-abort-all.
dashboard-session-rename
Rename a session. Usage /dashboard:session-rename.
dashboard-session-tell
Send a prompt to another session. Usage /dashboard:session-tell.
dashboard-session-unhide
Unhide a previously hidden session. Usage /dashboard:session-unhide.
daily-okr
Run a daily knowledge compound loop (7 KR). Invoke with /daily-okr or "start my daily review".
distill
Distill repository files into the RLM Summary Ledger using agentic intelligence (fast) or Swarm Workers (offline batch).