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 batch-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/batch-product-research)<a href="https://agentmods.dev/skills/hashgraph-online/awesome-codex-plugins/batch-product-research"><img src="https://agentmods.dev/badge/skills/hashgraph-online/awesome-codex-plugins/batch-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/batch-product-research"><img src="https://agentmods.dev/badge/skills/hashgraph-online/awesome-codex-plugins/batch-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 | $0.00041 | $0.00461 |
| Opus 5.5 | $0.00016 | $0.00184 |
| Sonnet 5.5 | $0.00008 | $0.00092 |
| Haiku 4.5 | $0.00004 | $0.00046 |
Grade A, and why
batch-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.
What it actually says
Batch Product Research
Use this skill for 1-20 keywords.
Inputs
Accept:
- comma-separated keywords
- numbered lists
- a local file path containing keywords
Defaults:
- maximum 20 keywords per run
- report path:
./artifacts/launchfast/batch-research/report-[YYYY-MM-DD].html - csv path:
./artifacts/launchfast/batch-research/report-[YYYY-MM-DD].csv
Workflow
1. Normalize input
- trim whitespace
- deduplicate case-insensitively
- if there are more than 20 keywords, split into chunks of 20 and process chunk-by-chunk
2. Run balanced product research
- run
research_productsfor every keyword - prefer parallel tool calls where practical
- do not require delegation
3. Score each keyword
For each keyword compute:
- search volume
- total niche revenue
- average price
- average reviews
- average revenue per seller
- top-seller dominance
- estimated margin using conservative assumptions
- opportunity score and verdict
Use verdicts:
- VIABLE
- MARGINAL
- NOT RECOMMENDED
- ERROR
4. Optional deeper passes
For VIABLE or MARGINAL keywords only:
- run
research_products(... focus="financial") - optionally run
amazon_keyword_researchon the top 2-3 ASINs if keyword depth matters for the user’s goal
5. Present ranked results
Always include a comparison table first.
Then provide concise cards or sections for the strongest keywords.
6. Write artifacts when useful
If the user asked for files, or a file materially improves the result:
- write an HTML report
- write a CSV export
Keep the file generation deterministic. Prefer Python for CSV writing.
Output
At minimum return:
- number of keywords processed
- ranking table
- top recommendations
- artifact paths when files were written
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 · 91 lines · 41 tokens per session scan A 9f885cf1f780
batch-product-research is a skill published in the GitHub repository hashgraph-online/awesome-codex-plugins (1,423 stars, last pushed today), licensed Apache-2.0. It adds 41 tokens to every session and 461 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.
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