Implement (multi-agent loop)

Implement (multi-agent loop) is a skill for Claude Code, Codex from AuraVixStudio/caelo. It costs 24 tokens per session (443 once invoked), scanned A, original, Apache-2.0.

A workflow that splits a substantial coding task among separate AI agents, then has reviewers check and improve the results.

In plain words
What is it for?
Use it to plan, implement, review, and refine non-trivial code changes until reviewers find no blocking issues.
Why use it?
It reduces the chance that one implementation misses problems by adding independent review and fix rounds.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to plan, implement, review, and refine non-trivial code changes until reviewers find no blocking issues.

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Install with agentmods
npx agentmods add skills/auravixstudio/caelo/implement
Install

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.

Any agent
npx skills add AuraVixStudio/caelo --skill implement
Clone the repo
git clone --depth 1 https://github.com/AuraVixStudio/caelo

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for Implement (multi-agent loop)

README.md
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Your own site
<a href="https://agentmods.dev/skills/auravixstudio/caelo/implement"><img src="https://agentmods.dev/badge/skills/auravixstudio/caelo/implement/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.

agentmods 80×15 button for Implement (multi-agent loop)

Your own site · 80×15
<a href="https://agentmods.dev/skills/auravixstudio/caelo/implement"><img src="https://agentmods.dev/badge/skills/auravixstudio/caelo/implement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 443 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00024 $0.00443
Opus 5.5 $0.00010 $0.00177
Sonnet 5.5 $0.00005 $0.00089
Haiku 4.5 $0.00002 $0.00044

Measured 1mo ago against content hash 5f447fad7307, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

caelo_core/skills/builtin/implement/SKILL.md · 38 lines

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

  1. Plan & split. Break the task into independent, self-contained chunks. Note the acceptance criteria you will check against.
  2. Implement. delegate one implementer per chunk (parallel — pass them in a single delegate call). Give each a precise, standalone instruction.
  3. Review. When the implementer summaries return, delegate a reviewer (one, or one per area) to review the changes for correctness, security and quality. Tell the reviewer exactly which files/areas to look at.
  4. Fix. Collect the reviewer findings. If there are any blocking findings, delegate an implementer to fix them (reference the specific findings).
  5. 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.
  6. 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 delegate calls instead of running them in parallel.
Changes

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.

  1. 1mo ago First seen · 38 lines · 24 tokens per session scan A 5f447fad7307

Subscribe to this mod's changes

Implement (multi-agent loop) is a skill published in the GitHub repository AuraVixStudio/caelo (25 stars, last pushed 5d ago), licensed Apache-2.0. It adds 24 tokens to every session and 443 once invoked, about $0.0001 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.

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