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 product-researchgit 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/product-research)<a href="https://agentmods.dev/skills/hashgraph-online/awesome-codex-plugins/product-research"><img src="https://agentmods.dev/badge/skills/hashgraph-online/awesome-codex-plugins/product-research/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/product-research"><img src="https://agentmods.dev/badge/skills/hashgraph-online/awesome-codex-plugins/product-research.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 | $0.00045 | $0.00666 |
| Opus 5.5 | $0.00018 | $0.00266 |
| Sonnet 5.5 | $0.00009 | $0.00133 |
| Haiku 4.5 | $0.00005 | $0.00067 |
Grade A, and why
product-research 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.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon FBA Product Research
This skill is the deeper, criteria-driven version of launchfast-product-research.
Core criteria
Evaluate every market against these baselines:
| Criteria | Threshold |
|---|---|
| Total niche revenue | > $200,000/month |
| Average price | >= $25, ideally >= $40 |
| Average reviews | <= 500 |
| Revenue per seller | >= $5,000/month |
| Top-seller dominance | top 2-3 sellers < 50% of revenue |
| Search volume | must exist |
| Estimated margin | >= 30% before ad costs |
Large-market exception:
- If niche revenue is above $1M, higher review counts can still be acceptable when multiple sellers under 200 reviews are doing strong revenue.
Workflow
1. Initial scan
Run:
research_products(keyword="<keyword>", focus="balanced", product_limit=20)
Extract:
- search volume
- average price
- average reviews
- opportunity score
- market grade
- brand concentration
- dominant brand
- total niche revenue
- average revenue per seller
- top-seller share
2. Financial trend check
Run:
research_products(keyword="<keyword>", focus="financial", product_limit=20)
Look for:
- growing vs stable vs declining products
- average MoM growth
- short-term momentum using 7d trend fields
3. Listing quality check
Run:
research_products(keyword="<keyword>", focus="titles", product_limit=10)
Look for:
- low listing quality scores with high revenue
- listing quality gaps
- weak copy or obvious differentiation openings
4. Keyword validation
Pick 2-3 relevant ASINs and run:
amazon_keyword_research(asins=["ASIN1", "ASIN2", "ASIN3"], limit=20)
Evaluate:
- keyword diversity
- CPC and sponsored density
- purchase rate
- obvious ranking gaps
5. Profitability estimate
Present a conservative estimate:
Selling Price
- Amazon Fees (~15%)
- Manufacturing
- Shipping
= Estimated Profit per Unit
= Estimated Margin %
If manufacturing cost is unknown, say so and state the assumption used.
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 · 138 lines · 45 tokens per session scan A eeaef583c912
product-research is a skill published in the GitHub repository hashgraph-online/awesome-codex-plugins (1,419 stars, last pushed yesterday), licensed Apache-2.0. It adds 45 tokens to every session and 666 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 2026-09-05.
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.
ecommerce-product-manager
Expert E-commerce Product Manager with deep knowledge of online retail strategy, conversion optimization, marketplace operations, and platform-specific tactics for Amazon, Shopify, and Alibaba. Use when: ecommerce-strategy, product-launch, conversion-optimization, marketplace-management, pricing-strategy.
ecommerce-livestream-trainer
Expert-level E-commerce Livestream Trainer with deep knowledge of live selling techniques, platform operations (TikTok Shop, Taobao Live, JD Live), audience engagement, and sales conversion.
sprr
Single PR reviewer for awesome-quant. Use when the user asks to review, validate, comment on, label, close, or merge one specific pull request that adds README.md entries. Triggers include "sprr", "review PR", "check PR", and "validate contribution".
bprr
Bulk PR reviewer for awesome-quant. Use when the user asks to review all open PRs, review unreviewed PRs, bulk review, or mentions "bprr". Reviews open PRs lacking the reviewed label and presents a summary before any merge/comment/label action.