CLI Agent Orchestrator is a tool that coordinates multiple AI coding command-line programs by running them as separate workers in isolated terminal sessions. A supervisor uses it to delegate software tasks to specialist agents in parallel or in sequence while the workers retain their normal command-line capabilities. The catalogue skills operate this orchestration workflow.
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 awslabs/cli-agent-orchestrator --skill cao-workflowgit clone --depth 1 https://github.com/awslabs/cli-agent-orchestratorWrote 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/awslabs/cli-agent-orchestrator/cao-workflow)<a href="https://agentmods.dev/skills/awslabs/cli-agent-orchestrator/cao-workflow"><img src="https://agentmods.dev/badge/skills/awslabs/cli-agent-orchestrator/cao-workflow/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/awslabs/cli-agent-orchestrator/cao-workflow"><img src="https://agentmods.dev/badge/skills/awslabs/cli-agent-orchestrator/cao-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 38 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
- medium Excessive Agency · line 42 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00079 | $0.06185 |
| Opus 5.5 | $0.00032 | $0.02474 |
| Sonnet 5.5 | $0.00016 | $0.01237 |
| Haiku 4.5 | $0.00008 | $0.00619 |
Grade A, and why
cao-workflow 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 yesterday.
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 — 493 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CAO Workflows
A CAO workflow is a Python script you write, validate, and — only after asking the user —
run through cao workflow run. Each script drives one or more agent steps through CAO's
shared substrate, so you can fan work out across agents, collect their results, and resume a
run that was interrupted.
Your job as an author ends at a validated script file on disk. Authoring does NOT run the workflow. Never claim a workflow ran, or will run, when all you did was write it. Running is a separate step the user must approve (see Lifecycle step c).
When to use
Reach for this skill when the user asks to build or run a multi-step or parameterized workflow — for example:
- "Analyze every file in
reports/and summarize the findings." - "Run a review pipeline: implement, then review, then verify."
- "Do the same batch job but with a different input directory each time."
If the work is a single one-off agent call, you don't need a workflow. Workflows earn their keep when there are multiple steps, fan-out, parameterization, or a need to resume.
The script API
Author scripts import from the cao_workflow package. This package runs only in the script
subprocess and imports nothing from cli_agent_orchestrator.* — it talks to CAO over HTTP.
Its public surface:
step(provider, agent, prompt, *, recovery, step_id=None, timeout=None, **opts) -> StepHandle— run one agent step and declare what re-running it would mean.recoveryis keyword-only with no default, so omitting it is aTypeErrorat the call. See "Declaring a recovery policy" below before you pick a value.run_step(provider, agent, prompt, *, step_id=None, timeout=None, **opts) -> StepHandle— the same call, declaring no policy. That is the only difference between the two. Arecovery=passed torun_steplands in**opts; the server validates it, the shim does not — see below.StepHandlehas five fields:.step_id,.terminal_id,.output,.status, and.replayed..replayedqualifies.terminal_id. When it isTruethe server returned a stored result and ran nothing, and.terminal_idis the ORIGINAL id — it names a terminal that no longer exists. That flag is the only thing standing between you and reading, writing to, or waiting on a dead id, so check it before you touch.terminal_id.get_inputs() -> dict— the run's resolved inputs (see Parameterized workflows). Returns{}when nothing was declared; never raises on absence.emit_output(value)— print the run-levelCAO_WORKFLOW_OUTPUT:sentinel (the run's return).ShimError(andShimIdentityError,ShimTransportError,ShimHTTPError) — the failure hierarchystepandrun_stepraise. Failures surface unchanged — the shim never retries. A structured HTTP error with a non-empty stringdetail.kindprints asrun-step returned HTTP <status> (<kind>): <message>(without the message suffix when absent); an unstructured error keeps the originalrun-step returned HTTP <status>text.
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.
- yesterday Changed · +120 lines 09b7c3948787
- 10d ago Changed · +11 lines 824266be5357
- 20d ago Changed · +3 lines 8240508c1f7c
- 1mo ago First seen · 359 lines · 79 tokens per session scan A c10b1b1ab320
cao-workflow is a skill published in the GitHub repository awslabs/cli-agent-orchestrator (1,456 stars, last pushed today), licensed Apache-2.0. It adds 79 tokens to every session and 6,185 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
tool-selection
Internal guidance for choosing between agent-rack's sync and background execution tools, and when to use per-agent shortcuts. Use whenever delegating a task to a claude/codex/opencode/Antigravity/custom sub-agent through agent-rack.
review
Run a structured, read-only code review through agent-rack's agentreview tool.
session-logs
Read the raw stdout/stderr event stream from a background agent-rack session.
session-send
Send follow-up input to a running background agent-rack session.
review-handling
Internal guidance for presenting agent-rack's agentreview output back to the user. Use whenever an agentreview call (foreground or via agentsessionstatus) returns a result.
run
Run a one-shot task synchronously with a named CLI sub-agent via agent-rack.