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 arcasilesgroup/ai-engineering --skill ai-orchestratorgit clone --depth 1 https://github.com/arcasilesgroup/ai-engineeringWrote 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/arcasilesgroup/ai-engineering/ai-orchestrator)<a href="https://agentmods.dev/skills/arcasilesgroup/ai-engineering/ai-orchestrator"><img src="https://agentmods.dev/badge/skills/arcasilesgroup/ai-engineering/ai-orchestrator/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/arcasilesgroup/ai-engineering/ai-orchestrator"><img src="https://agentmods.dev/badge/skills/arcasilesgroup/ai-engineering/ai-orchestrator.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 | $0.00105 | $0.06089 |
| Opus 5.5 | $0.00042 | $0.02436 |
| Sonnet 5.5 | $0.00021 | $0.01218 |
| Haiku 4.5 | $0.00011 | $0.00609 |
Grade A, and why
ai-orchestrator scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Use one `general-purpose` subagent. It runs the checkpoint's `verify` commands **in layer order: unit → API → CLI (lint, build, health `curl`) → UI e2e**, and stops at the first layer that fails. The UI e2e layer runs on How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrator
You coordinate; you don't do the work. Every piece of real work goes to a fresh subagent, so your context stays clean and each worker starts unbiased.
Project specifics (dev/test/lint/build commands, URLs, DB setup, sign-in) come from the Project config section of AGENTS.md. Pass the relevant values into every worker prompt so workers don't have to hunt for them.
Autonomy: the human is asked exactly twice
- Checkpoint review, at the end of Phase 1.
- App review, in Phase 4.
Everywhere else, decide and keep going:
- Ambiguity: choose the option most consistent with
.ai-engineering/PRD.html,.ai-engineering/brainstorm.html,DECISIONS.mdand the existing code, and log it as anassumptionsentry in the checkpoint JSON. The human sees those at the next review. The brainstorm gate, below, refuses a missing, stale, or unrelated interview before any of this runs. - Failures: handle them with the loop's automatic retry and re-plan (see Phase 2).
- Never pause for permission to run tests, start servers, or fix code.
- The only mid-run stop is the final circuit breaker in Phase 2.
Context rules (non-negotiable)
- Never read source code, diffs, test output or screenshots yourself. Subagents read them and report back in a few lines.
- All state lives on disk in
.ai-engineering/workflow/checkpoints/<slug>.json, and that file is the only thing you read to know where you are. Change it only with Edit or Write, never through Bash. The same hook validates every write to it: no gate can be passed out of order, no checkpoint can be marked passed with a gate still open, and no checkpoint can be touched while an earlier one is open. Update it after every step, so/ai-orchestrator resume <slug>can pick up after a crash or/clear. - Every subagent prompt:
- in Phase 2 and later, starts with the gate marker
[checkpoint <slug>#<id> <stage>], where<slug>is the feature slug you were asked to build and<stage>is one oftests,implement,behavior,ui,revieworfix. Thecheckpoint-gatehook (scripts/checkpoint-gate.py, PreToolUse) reads it and blocks the call with exit code 2 if:- an earlier checkpoint hasn't passed;
- this checkpoint has already passed;
- or the gate before this one hasn't passed.
- in Phase 2 and later, starts with the gate marker
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.
- 11d ago Changed · +6 lines 6043aef008c5
- 19d ago First seen · 186 lines · 105 tokens per session scan A 161170786169
ai-orchestrator is a skill published in the GitHub repository arcasilesgroup/ai-engineering (61 stars, last pushed 2d ago), licensed Apache-2.0. It adds 105 tokens to every session and 6,089 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-27.
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