Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
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.
[](https://agentmods.dev/skills/avenoxai/avenoxskills/codex-fleet)<a href="https://agentmods.dev/skills/avenoxai/avenoxskills/codex-fleet"><img src="https://agentmods.dev/badge/skills/avenoxai/avenoxskills/codex-fleet.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 | $0.00234 | $0.09086 |
| Opus 5 | $0.00117 | $0.04543 |
| Sonnet 5 | $0.00047 | $0.01817 |
| Haiku 4.5 | $0.00023 | $0.00909 |
Grade B, and why
codex-fleet scanned grade B 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 6d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
find ~/.codex/generated_images -type f -name '*.png' -mmin -5 How it starts
The opening of the file, as written. The whole thing — 596 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex Fleet — Standalone Action Runner
A single self-contained skill for driving the Codex CLI from any agent (Claude Code, Cursor, or your own harness). No external control plane required — everything here runs against a plain local
codexinstall. Drop this file into.claude/skills/codex-fleet/SKILL.md(or your agent's skills dir) and go.Built and battle-tested by Avenox. Share freely.
This skill does three things and ALWAYS executes them, never describes them:
- General Codex CLI tasks — code review, refactor, multi-file edits, analysis, diagnosis, anything you'd hand to a peer-AI for parallel processing.
- Image generation — real rendered images via Codex's built-in
gpt-image-2tool: backgrounds, portraits, icons, sigils, banners, UI assets, sprites, mockups, photoreal, infographics. - Fleets — spawning many
codex execdelegates in parallel (one lane or twenty), with worktree isolation for concurrent write lanes.
CRITICAL: This is an ACTION skill, not commentary
When invoked you MUST:
- Actually invoke
codex execvia the Bash tool. Never write instructions for the user to run themselves. - Default to background execution (
run_in_background: true) for any task likely to take >10s. This lets the main agent continue other work in parallel while Codex runs. The harness notifies on completion. - For multiple independent jobs, fire them ALL in parallel. Codex sessions don't contend. Compute is not the bottleneck — throughput is. If the user asks for 4 images or 3 codex investigations, that's 4 or 3 simultaneous Bash calls in one message, all
run_in_background: true. - Summarize results from logs after each background job completes — don't dump raw stdout unless asked.
If the user's request is "use codex to X" or "run codex on X", run codex exec ... "X". Don't wrap, don't paraphrase, don't ask "should I proceed" — just go.
Prerequisites
- Codex CLI 0.128+ installed and authenticated (
codex --version). Reasoning tierslow/medium/high/xhighrequire 0.128+. - For the image-gen CLI fallback and
gpt-image-1.5transparency path only:OPENAI_API_KEY. The built-inimage_gentool uses your Codex subscription and needs no key.
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.
- 6d ago First seen · 596 lines · 0 tokens per session scan B a2dd35693afc
codex-fleet is a skill published in the GitHub repository avenoxai/avenoxskills (48 stars, last pushed 28d ago), licensed MIT. It adds 234 tokens to every session and 9,086 once invoked, about $0.0012 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
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make_plan
For external plan request scenarios, guides the Agent to request a clear, actionable, step-by-step plan from a stronger Agent via listagents and chatwithagent, emphasizing that the plan is executed by the requester, not by the consulted Agent.
hermes-history-ingest
Ingest Hermes agent history into the Obsidian wiki. Use this skill when the user wants to mine their past Hermes sessions for knowledge, import their /.hermes folder, extract insights from previous Hermes conversations, or says things like "process my Hermes history", "add my Hermes memories to the wiki", "ingest…
wiki-context-pack
Produce a token-bounded, citation-ready context slice from an existing Obsidian vault for a downstream agent or task. Use for "/wiki-context-pack", "use my vault as context", "context slice for X", "pack the wiki for my agent", or "bounded context for Y".
security-review
Perform a focused security review of pending git changes to identify high-confidence security vulnerabilities with real exploitation potential. Use this skill when the user asks for a security review, security audit, vulnerability scan, or wants to check pending changes on a branch for security issues before merging.…
huggingface-llm-trainer
Train or fine-tune language models with TRL or Unsloth on Hugging Face Jobs, including SFT, DPO, GRPO, reward models, and GGUF conversion. Use for cloud LLM training; use huggingface-vision-trainer for vision tasks.