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/Omar-Obando/qwen-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/commands/omar-obando/qwen-orchestrator/deploy)<a href="https://agentmods.dev/commands/omar-obando/qwen-orchestrator/deploy"><img src="https://agentmods.dev/badge/commands/omar-obando/qwen-orchestrator/deploy/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/omar-obando/qwen-orchestrator/deploy"><img src="https://agentmods.dev/badge/commands/omar-obando/qwen-orchestrator/deploy.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 | $0.00222 |
| Opus 5.5 | $0.00000 | $0.00089 |
| Sonnet 5.5 | $0.00000 | $0.00044 |
| Haiku 4.5 | $0.00000 | $0.00022 |
Grade A, and why
deploy 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 1mo 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
Deploy the following target to the specified environment:
Target: {{args}}
Deployment Protocol
-
Pre-Flight Checks:
- All tests pass
- Build succeeds
- No security vulnerabilities
- Environment variables documented
-
Build:
- Generate production artifacts
- Verify build output
-
Deploy:
- Deploy to target environment
- Run health checks
- Verify deployment
-
Post-Deploy:
- Smoke test critical paths
- Monitor error rates
- Update release notes
Report Format
## Deployment Report
### Pre-Flight
- Tests: PASS/FAIL
- Build: PASS/FAIL
- Security: PASS/FAIL
### Deployment
- Environment: [target]
- Version: [version]
- Status: SUCCESS/ROLLBACK
### Post-Deploy Verification
- Health check: PASS/FAIL
- Smoke tests: PASS/FAIL
- Error rate: [baseline vs current]
Begin by running the pre-flight checks.
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.
- 1mo ago First seen · 52 lines · 0 tokens per session scan A 56bae16708ea
deploy is a command published in the GitHub repository Omar-Obando/qwen-orchestrator (51 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 222 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.
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export
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install
Install or upgrade the m3-memory CLI + system payload.
tts:uninstall
Stop and remove the claude-tts OS service and daemon, and optionally remove its config. Confirms before deleting.
toefl-grade
A TOEFL answer-scoring command that uses a local language model, and speech-to-text software for recordings, to assess reading, listening, writing, and speaking practice. TOEFL is an English test with separate reading, listening, writing, and speaking sections.