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 agentmods add skills/arcasilesgroup/ai-engineering/ai-cyclenpx skills add arcasilesgroup/ai-engineering --skill ai-cyclegit 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-cycle)<a href="https://agentmods.dev/skills/arcasilesgroup/ai-engineering/ai-cycle"><img src="https://agentmods.dev/badge/skills/arcasilesgroup/ai-engineering/ai-cycle.svg" alt="Measured on agentmods" 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 | $0.00121 | $0.01057 |
| Opus 5 | $0.00060 | $0.00528 |
| Sonnet 5 | $0.00024 | $0.00211 |
| Haiku 4.5 | $0.00012 | $0.00106 |
Grade A, and why
ai-cycle 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Two halves, and a person between them
Not an engine. Each stage is a skill with its own file; load that file and follow it, the way the generated slash command already says. There is no state file, no new verb and nothing here that decides anything a stage would not decide on its own.
What it produces
Half one ends with the brief page a person decides on — docs/solution-intent.html,
regenerated by ai-eng report intent --html. Half two ends with the green gate output,
shown in the conversation, or with a page that says exactly why it is not green.
First half — up to the person
/ai-cycle <what you want>
The order of the halves is data, not prose here: read policy/skill-sequence.toml and
follow it. First half — research, then spec, then challenge, then council, then a brief
published as a page: what is proposed, what was refuted, what is missing, and what it
costs. Then stop.
Stopping is the whole of this half. ai-eng report blocked already lists a drafted
specification nobody has approved, so the halt exists and fires; this hands the person the
thing they need to decide instead of a status line. The approval is a record with the
specification's exact digest in it, and until that record exists the second half has
nothing to run against — the task envelope refuses outright when the bytes moved.
Second half — after them
/ai-cycle build <NNN>
The same map's second half, in order, once an approval record carrying the
specification's exact digest exists — the map's [gate] section is the line between the
halves. Inside ai-build, run one task at a time through ai-eng spec show <NNN> --task <n>: two kilobytes, not the whole plan. Where the plan's order allows two tasks
at once and the host can start two agents, start two; where it cannot, do them in turn.
The critics — review, verify, security — run each in its own context, then ai-eng audit verify, then ai-ship.
When the gate goes red
It does not stop at the first red and it does not get to cheat.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 84 lines · 121 tokens per session scan A f32c45c0e808
ai-cycle is a skill published in the GitHub repository arcasilesgroup/ai-engineering (54 stars, last pushed 4d ago), licensed Apache-2.0. It adds 121 tokens to every session and 1,057 once invoked, about $0.0006 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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