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 refine-taskgit 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/refine-task)<a href="https://agentmods.dev/skills/hashgraph-online/awesome-codex-plugins/refine-task"><img src="https://agentmods.dev/badge/skills/hashgraph-online/awesome-codex-plugins/refine-task/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/refine-task"><img src="https://agentmods.dev/badge/skills/hashgraph-online/awesome-codex-plugins/refine-task.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 | $0.00052 | $0.00403 |
| Opus 5.5 | $0.00021 | $0.00161 |
| Sonnet 5.5 | $0.00010 | $0.00081 |
| Haiku 4.5 | $0.00005 | $0.00040 |
Grade A, and why
refine-task 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:
- refine-task — 100% identical, 0 lines differ
What it actually says
Agiflow Refine Task
Make the selected task ready for confident execution while preserving its original intent.
Workflow
- Resolve the task with
get_current_scope,list_projects,list_tasks, orget_taskas needed. - Call
get_taskfor full details andlist_task_commentswhen prior decisions may affect scope. - Call
list_project_statusesandlist_membersonly when status or assignment context is relevant. - Evaluate the task for:
- Clear user or business outcome
- Concrete scope boundaries
- Objective acceptance criteria
- Known dependencies and blockers
- Appropriate priority and assignee
- Enough context to complete without guessing
- Ask focused clarification questions for unresolved decisions. Do not invent requirements.
- Draft the refined title, description, acceptance criteria, scope boundaries, and dependency notes.
- Show the proposed changes and request approval.
- After approval, call
update_taskwith only the fields that need to change. - Call
get_taskagain to verify the saved result.
Quality Test
Acceptance criteria must be specific, measurable, achievable within the task, relevant to its outcome, and directly verifiable. Replace phrases such as "works correctly" or "handles errors" with observable behavior.
Guardrails
- Do not add new product requirements during refinement.
- Do not move the task from Planning to Todo. Backlog grooming owns that transition.
- Do not delete the task.
- Preserve useful existing context and comments.
Response
Summarize what changed, which ambiguities were resolved, and whether the task is ready for backlog-grooming.
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 · 42 lines · 52 tokens per session scan A 5656f367407a
refine-task is a skill published in the GitHub repository hashgraph-online/awesome-codex-plugins (1,326 stars, last pushed today), licensed Apache-2.0. It adds 52 tokens to every session and 403 once invoked, about $0.0002 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
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.
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.
project-dashboard-builder
Generates a self-contained single-file HTML project dashboard (status banner, milestones, risks heat list, decisions log, links) from the tracker files in /Documents/Cowork/projects/, saved as dashboard- -YYYY-MM-DD.html. Use when the user asks for a project dashboard, a shareable or pinnable status page, an HTML…
project-status-tracker
Maintains living project files (project.md, decisions.md, risks.md, links.md) in a per-project OneDrive folder from emails, Teams channels and meeting transcripts, and generates a weekly DRAFT status document in Word. Use when the user asks to set up tracking for a project, update a project's status files, log a…