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 skills add desplega-ai/agent-swarm --skill work-on-taskgit 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/skills/desplega-ai/agent-swarm/work-on-task)<a href="https://agentmods.dev/skills/desplega-ai/agent-swarm/work-on-task"><img src="https://agentmods.dev/badge/skills/desplega-ai/agent-swarm/work-on-task/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/skills/desplega-ai/agent-swarm/work-on-task"><img src="https://agentmods.dev/badge/skills/desplega-ai/agent-swarm/work-on-task.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00016 | $0.00369 |
| Opus 5 | $0.00008 | $0.00185 |
| Sonnet 5 | $0.00003 | $0.00074 |
| Haiku 4.5 | $0.00002 | $0.00037 |
Grade A, and why
work-on-task 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 9d 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
Working on a Task
If no taskId is provided, call poll-task to get a new task.
Workflow
-
Get task details: Call
get-task-detailswith the taskId. -
Recall relevant memories: Use
memory-searchwith the task description before starting any work. Past learnings, solutions, and gotchas are indexed here. -
Choose your approach based on the task type:
- Research task → use
/researching - Development task → use
/planningfirst, then/implementing - Simple/direct task (no plan needed) → implement directly
- Research task → use
-
Work on it, calling
store-progressat each meaningful milestone (not just start and end — the lead monitors this). -
Complete the task — see Completion below.
Completion
Call store-progress with:
- Success:
status: "completed"+output: "<what you did and the result>". Output should be specific enough for the lead to assess without re-reading your work. - Failure:
status: "failed"+failureReason: "<what went wrong and what you tried>".
Then reply "DONE" to end the session.
Interruptions
If interrupted by the user, adapt to their instructions. When resuming, call /skill:work-on-task <taskId> again to pick up where you left off.
When to escalate
If you're stuck after genuine effort (not just first failure), use /skill:swarm-chat to ask the lead for help or clarification. Don't spin — escalate.
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.
- 9d ago First seen · 40 lines · 16 tokens per session scan A 4a9642f91552
work-on-task is a skill published in the GitHub repository desplega-ai/agent-swarm (754 stars, last pushed today), licensed MIT. It adds 16 tokens to every session and 369 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 2026-08-30.
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