awesome-ChatGPT-repositories is a curated, searchable directory of open-source repositories related to ChatGPT, the OpenAI API, and Codex. It is for people looking for tools such as retrieval systems, agent libraries, chatbot interfaces, and command-line utilities, with catalogue entries that support searching the collection through coding-agent extensions.
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 taishi-i/awesome-ChatGPT-repositories --skill searchgit clone --depth 1 https://github.com/taishi-i/awesome-ChatGPT-repositoriesWrote 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/taishi-i/awesome-chatgpt-repositories/search)<a href="https://agentmods.dev/skills/taishi-i/awesome-chatgpt-repositories/search"><img src="https://agentmods.dev/badge/skills/taishi-i/awesome-chatgpt-repositories/search/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/taishi-i/awesome-chatgpt-repositories/search"><img src="https://agentmods.dev/badge/skills/taishi-i/awesome-chatgpt-repositories/search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Agent Snooping · line 111 Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
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 | $0.00057 | $0.03431 |
| Opus 5 | $0.00028 | $0.01716 |
| Sonnet 5 | $0.00011 | $0.00686 |
| Haiku 4.5 | $0.00006 | $0.00343 |
Grade B, and why
search scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
DATA="$(find "${HOME}/.claude/plugins" "${PWD}" -type d -name data -path "*awesome-chatgpt-search*" 2>/dev/null | head -1)" Copies of this mod
1 near-identical copy found in the catalogue:
- taishi-i__awesome-ChatGPT-repositories — 100% identical, 20 lines differ
How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search the awesome-ChatGPT-repositories database for: "$ARGUMENTS"
Instructions
Step 1 — Interpret the query
The user's query is: "$ARGUMENTS"
Supported query modifiers:
category:<name>— filter to one categorylanguage:<lang>— filter by programming languagelist categoriesorcategories— skip to Step 5b- Plain text — keyword search across all categories
The descriptions are in English, so convert non-English queries to English keywords before searching.
Examples:
| User query | English keywords to search |
|---|---|
| RAGを使ったチャットボット | RAG, retrieval, chatbot, vector |
| 코드 생성 도구 (Korean) | code generation, copilot, autocomplete |
| 中文问答系统 | chinese, QA, question answering |
| outil de résumé (French) | summarization, summary, text |
| LLMを使ったエージェント | agent, autonomous, LLM, tool use |
Keyword tips:
- Use stems, not full words. Substring match catches variants:
embed→ embedding/embeddings,retriev→ retrieval/retrieve,classif→ classification/classifier,generat→ generation/generative,fine-tun→ fine-tune/fine-tuning,summari→ summarize/summarization,orchestrat→ orchestrate/orchestration. - Add domain-specific names. For common LLM/AI domains, include well-known tool or framework names present in the database:
| Domain (query hint) | Stem keywords | Tool/library names to add |
|---|---|---|
| RAG / 検索拡張生成 | retriev, rag, embed, vector |
langchain, llamaindex, haystack, faiss, chroma, pinecone |
| Agent / エージェント | agent, autonom, orchestrat |
autogpt, langchain, langgraph, crewai |
| Fine-tuning / ファインチューニング | fine-tun, lora, peft, finetun |
lora, peft, qlora |
| Code generation / コード生成 | code, coding, copilot, autocomplet |
copilot, codex, interpreter |
| Chatbot / チャットボット | chat, bot, dialog, convers |
discord, telegram, slack |
| Prompt engineering | prompt, few-shot, chain-of-thought, jailbreak |
promptflow, dspy |
| Evaluation / 評価 | evaluat, benchmark, metric |
evals, lm-eval, deepeval |
| Image / 画像生成 | image, vision, multimodal |
dall-e, stable-diffusion, midjourney |
| Voice / 音声 | voice, speech, audio, tts, asr |
whisper, eleven |
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.
- 13d ago First seen · 234 lines · 57 tokens per session scan B 991cd006e942
search is a skill published in the GitHub repository taishi-i/awesome-ChatGPT-repositories (3,242 stars, last pushed 5d ago), licensed CC0-1.0. It adds 57 tokens to every session and 3,431 once invoked, about $0.0003 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
claude-api
Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.
iterative-retrieval
Pattern for progressively refining context retrieval to solve the subagent context problem.
cost-aware-llm-pipeline
A planning guide for choosing language models and managing the amount of conversation context used by an AI coding workflow. It groups tasks by complexity and gives rules for avoiding context overflow during long sessions.
paper-daily
Discover daily arXiv papers for LLM/Agent topics, rank candidates with keyword and institution filters, and prepare a small selected paper list for llm-paper-daily style workflows.
fetch-llm-papers
Workflow for updating the LLM landscape paper pool (section/xllmpapers.md) using fetchllmpapers.py. Covers full re-fetch, resume from checkpoint, and adding new topics. USE FOR: Refreshing citation counts, expanding topic coverage. DO NOT USE FOR: Adding hand-curated entries to section files (use…
fetch-llm-apps
Workflow for updating the popular LLM applications pool (section/xllmapps.md) using fetchllmapps.py. Covers full refresh, alternate exports, topic tuning, and common pitfalls. USE FOR: Refreshing the ranked GitHub applications list linked from applications.md. DO NOT USE FOR: Hand-curating application entries inside…