Awesome Codex Plugins is a curated catalogue and marketplace of installable plugins, skills, and related resources for OpenAI Codex and ChatGPT. It helps people find, create, validate, and submit extensions for Codex workflows across its app, command-line interface, and IDE extensions. The catalogue add-ons are the plugins, skills, agents, and hooks listed or supported by this project.
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 hashgraph-online/awesome-codex-plugins --skill orchestrategit clone --depth 1 https://github.com/hashgraph-online/awesome-codex-pluginsWrote 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/hashgraph-online/awesome-codex-plugins/orchestrate)<a href="https://agentmods.dev/skills/hashgraph-online/awesome-codex-plugins/orchestrate"><img src="https://agentmods.dev/badge/skills/hashgraph-online/awesome-codex-plugins/orchestrate/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/hashgraph-online/awesome-codex-plugins/orchestrate"><img src="https://agentmods.dev/badge/skills/hashgraph-online/awesome-codex-plugins/orchestrate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00064 | $0.01004 |
| Opus 5.5 | $0.00026 | $0.00402 |
| Sonnet 5.5 | $0.00013 | $0.00201 |
| Haiku 4.5 | $0.00006 | $0.00100 |
Grade A, and why
orchestrate 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- orchestrate — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Invoked as
/agiflow:orchestrate. In hosts without slash-prompts, this skill is triggered by matching intent and drives AgiFlow via its MCP tools.
Usage:
/agiflow:orchestrate- Check pipeline state and dispatch the next highest-priority task
Guardrails
- This is a read-assess-dispatch loop, not an implementation prompt.
- Do NOT implement tasks here — use
/agiflow:run-taskfor that. - Keep capacity checks honest: do not dispatch if at capacity.
- Report pipeline state even when no dispatch is needed.
AgiFlow Project Management Guidelines
Follow the shared AgiFlow project-management guidelines in references/agiflow-agents.md — agent assignment, the task status workflow and transitions, work-unit best practices, and the tags strategy apply to this workflow.
Steps Track these steps as TODOs and complete them one by one.
1. Assess Current Pipeline State
-
Use
list_tasksto count tasks in each active status column:status: "In Progress"— tasks currently being coded by agentsstatus: "Testing"— tasks running test suitesstatus: "Review"— tasks awaiting human reviewstatus: "Blocked"— tasks requiring human interventionstatus: "Todo"— tasks ready for pickup (sorted by priority automatically)
-
Report the pipeline state in a concise table:
Pipeline State: ┌──────────────┬───────┐ │ Status │ Count │ ├──────────────┼───────┤ │ Todo │ N │ │ In Progress │ N │ │ Testing │ N │ │ Review │ N │ │ Blocked │ N │ └──────────────┴───────┘
2. Check Capacity
- Determine active task count:
In Progress+Testingcombined. - Check capacity limit (default: 3 concurrent active tasks unless specified).
- If at or above capacity:
- Report: "At capacity (N active tasks). No dispatch needed."
- List any Blocked tasks that need human attention.
- Stop here.
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.
- 1mo ago First seen · 110 lines · 64 tokens per session scan A 2cc32b064197
orchestrate is a skill published in the GitHub repository hashgraph-online/awesome-codex-plugins (1,346 stars, last pushed yesterday), licensed Apache-2.0. It adds 64 tokens to every session and 1,004 once invoked, about $0.0003 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.
Other skills, from other repositories
orchestrate
Pipeline orchestration: dispatch the highest-priority ready tasks/work units to agents, manage capacity, and coordinate the Todo to Done flow. Invoked as /agiflow:orchestrate. Uses listtasks, listactivetasksbyorg, listmembers, updatetask, getworkunitprogress.
awesome-chatgpt
Search this repository's awesome-ChatGPT-repositories list for open-source ChatGPT and LLM projects such as RAG frameworks, agents, chatbots, CLIs, prompts, and browser extensions. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, or AI development; not for editing or adding list…
awesome-chatgpt-search
Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.
create-entry
Use when the user wants to suggest, draft, create, or add an awesome-quant README entry from a project URL, GitHub issue, or Linear issue, including choosing tags and category placement.
galley
Authors and validates Galley task YAML, acceptance criteria, profiles, daemon handoff, and queueing. Use whenever the request mentions Galley or touches a Galley task, profile, daemon, or run.
jira-cli
Interact with Jira from the command line to create, list, view, edit, and transition issues, manage sprints and epics, and perform common Jira workflows. Use when the user asks about Jira tasks, tickets, issues, sprints, or needs to manage project work items.