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 Bilal140202/the-lord-of-the-skills --skill taishi-i__awesome-chatgpt-repositoriesgit clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-skillsWrote 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/bilal140202/the-lord-of-the-skills/taishi-i__awesome-chatgpt-repositories)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/taishi-i__awesome-chatgpt-repositories"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/taishi-i__awesome-chatgpt-repositories/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/bilal140202/the-lord-of-the-skills/taishi-i__awesome-chatgpt-repositories"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/taishi-i__awesome-chatgpt-repositories.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.5 | $0.00023 | $0.01372 |
| Sonnet 5.5 | $0.00011 | $0.00686 |
| Haiku 4.5 | $0.00006 | $0.00343 |
Grade B, and why
taishi-i__awesome-ChatGPT-repositories scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
DATA="$(find "${HOME}/.claude/plugins" "${PWD}" -type d -name data -path "*awesome-chatgpt-search*" 2>/dev/null | head -1)" This is a copy
100% identical to search — 69 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 1mo ago First seen · 234 lines · 57 tokens per session scan B ba562504cfa6
taishi-i__awesome-ChatGPT-repositories is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 3,431 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). It is 100% identical to search, differing in 69 lines, and is treated as a copy.
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