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/auravixstudio/caelo/implementnpx skills add AuraVixStudio/caelo --skill implementgit clone --depth 1 https://github.com/AuraVixStudio/caeloWhat 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 | $0.00024 | $0.00443 |
| Opus 5 | $0.00012 | $0.00221 |
| Sonnet 5 | $0.00005 | $0.00089 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
Implement (multi-agent loop) 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 2d 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
Implement (multi-agent loop)
Use this when you (the orchestrator) should implement a non-trivial change by fanning
work out to subagents and iterating until the change is clean. You drive the loop with
the delegate tool; subagents work in isolated copies and their changes await the
user's merge review.
Loop
- Plan & split. Break the task into independent, self-contained chunks. Note the acceptance criteria you will check against.
- Implement.
delegateoneimplementerper chunk (parallel — pass them in a singledelegatecall). Give each a precise, standalone instruction. - Review. When the implementer summaries return,
delegateareviewer(one, or one per area) to review the changes for correctness, security and quality. Tell the reviewer exactly which files/areas to look at. - Fix. Collect the reviewer findings. If there are any blocking findings,
delegateanimplementerto fix them (reference the specific findings). - Repeat steps 3–4 until reviewers report no blocking findings, or you reach the round limit. Default to 3 rounds; if the user asked for an "effort N" (1–5), use N rounds.
- Summarize. Report what changed, the final review verdict, and that the changes are staged for the user's merge review.
Notes
- Keep your own context clean: integrate the returned summaries, not transcripts.
- Do not exceed the team limits (parallelism / subagent count / turn budget) — they are enforced; design your fan-out to fit.
- If a chunk turns out to depend on another, sequence those
delegatecalls instead of running them in parallel.
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.
- 2d ago First seen · 38 lines · 24 tokens per session scan A 5f447fad7307
Implement (multi-agent loop) is a skill published in the GitHub repository AuraVixStudio/caelo (22 stars, last pushed 20d ago), licensed Apache-2.0. It adds 24 tokens to every session and 443 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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