search

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 category
• language:<lang> — filter by programming language
• list categories or categories — 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 |

• Aim for 3–6 keywords. Too few miss items; too many inflate low-quality partial matches.

Step 2 — Search the data files with grep

Data is split into per-category files. Each file is a JSON array with one repo record per line, so you can grep for matches instead of reading whole files — this keeps token use low (a typical query pulls in a few dozen matching lines instead of hundreds of KB). Fields per record:

• u: GitHub URL · n: repository name · d: English description
• c: category · l: language (optional) · t: topics comma-separated (optional)
• sc: quality score 0–8 · st: star count (optional) · ns: normalized star score 0–10 (optional)

File list (all under data/ relative to this plugin; six categories over ~200 entries are split a/b):

| Category | File(s) |

|----------|---------|

| Awesome-lists | repos-awesome-lists.json |

| Prompts | repos-prompts.json |

| Chatbots | repos-chatbots-a.json, repos-chatbots-b.json |

| Browser-extensions | repos-browser-extensions-a.json, repos-browser-extensions-b.json |

| CLIs | repos-clis-a.json, repos-clis-b.json |

| Reimplementations | repos-reimplementations.json |

| Tutorials | repos-tutorials.json |

| NLP | repos-nlp-a.json, repos-nlp-b.json |

| Langchain | repos-langchain.json |

| Unity | repos-unity.json |

| Openai | repos-openai-a.json, repos-openai-b.json |

| Others | repos-others-a.json, repos-others-b.json |

Which files to search — pick the minimum set that covers the query, then grep them (below):

Rule A — category: specified: grep only that category's file(s), skip routing below.

Match the category name case-insensitively and accept common variants:

cli/clis/command-line → CLIs · chatbot/bot/chatbots → Chatbots · browser/extension/browser-extension → Browser-extensions · prompt/prompts → Prompts · tutorial/tutorials → Tutorials · reimpl/reimplementation → Reimplementations · awesome/lists → Awesome-lists · open ai/openai → Openai. If the value matches no category, fall back to keyword routing (Rule C).

Rule B — list categories: skip all file reads, jump to Step 5b.

Rule C — keyword routing for general queries:

Use the English keywords from Step 1 (not the original query text) for routing.

For each row below, check if any English keyword contains or matches the listed terms (case-insensitive substring).

Use that row's file(s) only if there is a match.

If multiple rows match, collect all their files (deduplicated).

If no rows match, use the default: repos-chatbots-a.json, repos-nlp-a.json, repos-openai-a.json, repos-others-a.json.

| If query mentions… | Search these files |

|--------------------|-----------------|

| chatbot, bot, chat, dialog, conversation, assistant, discord, slack | repos-chatbots-a.json, repos-chatbots-b.json |

| RAG, retrieval, vector, embed, semantic, FAISS, Chroma, Pinecone, similarity, index | repos-nlp-a.json, repos-nlp-b.json, repos-langchain.json |

| NLP, text, classify, classification, NER, POS, sentiment, translation, extraction, summariz | repos-nlp-a.json, repos-nlp-b.json |

| agent, agentic, workflow, autonomous, orchestrat, tool use, function call, multi-agent | repos-others-a.json, repos-others-b.json, repos-langchain.json |

| OpenAI, GPT-3, GPT-4, gpt4, gpt3, completion, fine-tun, API key, endpoint | repos-openai-a.json, repos-openai-b.json |

| browser, extension, Chrome, Firefox, sidebar, popup, Tampermonkey | repos-browser-extensions-a.json, repos-browser-extensions-b.json |

| CLI, terminal, shell, command-line, command line | repos-clis-a.json, repos-clis-b.json |

| tutorial, learn, course, beginner, guide, example, cookbook, sample | repos-tutorials.json |

| prompt, prompting, few-shot, chain-of-thought, jailbreak, injection | repos-prompts.json |

| Unity, game engine, 3D, game development | repos-unity.json |

| LangChain, LlamaIndex, Haystack, chain, index, LangGraph | repos-langchain.json |

| lora, peft, qlora, finetun, fine-tuning, quantiz | repos-reimplementations.json, repos-nlp-a.json, repos-openai-a.json |

| evaluat, benchmark, metric, assess, leaderboard | repos-nlp-a.json, repos-nlp-b.json, repos-others-a.json |

| reimplement, from scratch, reproduce, train, training, PyTorch | repos-reimplementations.json |

| awesome list, curated, collection, survey, compilation | repos-awesome-lists.json |

| code, coding, IDE, VS Code, copilot, autocomplete, interpreter | repos-others-a.json, repos-others-b.json, repos-clis-a.json |

| image, vision, multimodal, DALL-E, Stable Diffusion, drawing | repos-others-a.json, repos-nlp-a.json |

| voice, speech, audio, TTS, ASR, Whisper | repos-others-a.json, repos-nlp-b.json |

Then grep those files for the keywords — do NOT open whole files with the Read tool. Locate the data directory once:

DATA="$(find "${HOME}/.claude/plugins" "${PWD}" -type d -name data -path "*awesome-chatgpt-search*" 2>/dev/null | head -1)"

Then grep the selected files for your Step 1 keywords and cap the output. Use -F (literal substring match — same semantics as the scoring step, and safe for keywords like c++ or .net) with one -e per keyword:

grep -ihF -e keyword1 -e keyword2 -e keyword3 "$DATA"/repos-nlp-a.json "$DATA"/repos-nlp-b.json | head -120

Each line of output is one repo record (a JSON object) that matched at least one keyword — score those lines directly in Step 4. This reads only the matching repos, not the whole files. Notes:

• If grep returns fewer than ~8 lines, broaden the keywords (add stems/tool names from Step 1) and re-run.
• If it returns the full head cap, your keywords are good; proceed.
• Only fall back to the Read tool on individual files if grep is unavailable.

Step 3 — Filter by language (if language:<lang> was given)

Append a language filter to the grep pipeline (the l field holds the language, matched case-insensitively):

grep -ihF -e keyword1 -e keyword2 "$DATA"/repos-clis-a.json "$DATA"/repos-clis-b.json | grep -iF '"l":"<lang>"' | head -120

Step 4 — Score candidates

Using the English keywords from Step 1, compute a relevance score for each repo record returned by grep:

Text match score (case-insensitive, per keyword):

• Name (n) exact keyword match: +20 pts
• Name (n) contains keyword: +10 pts
• Description (d) contains keyword: +5 pts
• Topics (t) contains keyword: +3 pts
• Category (c) contains keyword: +2 pts

Popularity bonus (added once per item):

• If ns (normalized star score) is present: min(4, ns * 0.4)
• Otherwise: min(4, sc * 0.5)

Quality bonus (always added): min(2, sc * 0.25)

Combined score = text_match + popularity_bonus + quality_bonus

Exclude items with text_match < 5 (catches only accidental partial hits). Collect top 20 candidates by combined score.

Step 5a — Re-rank with your judgment

Apply semantic judgment to produce the final ordered list of up to 10 results.

Re-rank by evaluating each candidate on:

1. Semantic centrality — how directly does this repo address the query's core intent?
2. Quality signal — higher sc means a richer, better-documented project.
3. Category fit — match the repo type to the implied need:

• "build a chatbot / ボット" → prefer Chatbots, CLIs
• "learn / tutorial / 勉強" → prefer Tutorials
• "prompt engineering" → prefer Prompts
• "use from browser" → prefer Browser-extensions
• "NLP task" → prefer NLP, Langchain
• "OpenAI API" → prefer Openai

1. Specificity — a repo specialized for the exact use-case beats a general one.
2. Language fit — if the user implied a language, prefer repos with matching l.

Step 5b — List categories (only if query was list categories / categories)

Skip scoring. Present:

## Available categories

| Category | Count |
|----------|-------|
| Awesome-lists | 98 |
| Prompts | 190 |
| Chatbots | 383 |
| Browser-extensions | 257 |
| CLIs | 268 |
| Reimplementations | 42 |
| Tutorials | 21 |
| NLP | 425 |
| Langchain | 180 |
| Unity | 17 |
| Openai | 329 |
| Others | 477 |
| **Total** | **2,687** |

Step 6 — Format the output

## Search results for "$ARGUMENTS"

*(Searched for: keyword1, keyword2, ...)*

Found N result(s).

### 1. [repository-name](url)
**Category:** category  ·  **Language:** language  ·  ⭐ {st} stars
Description text here.
*Topics: tag1, tag2, tag3*

### 2. ...

Omit the Language line if l is absent. Omit ⭐ stars if st is absent. Omit the Topics line if t is absent.

If no results found, suggest alternate keywords and link to:

https://github.com/taishi-i/awesome-ChatGPT-repositories

Step 7 — Output use-case selection guide

After the search results list, append a guide table to help users pick the right repo for their specific situation.

Match the section heading and table language to the query language — if the query was in Japanese, use Japanese for the heading and column headers; otherwise use English.

## Use-case Selection Guide

| Use case | Recommended | Score | Why |
|---|---|---|---|
| ... | [name](url) | sc=N | short reason |

Rules:

• List 3–6 distinct use cases derived from the top 10 results. Each row should represent a meaningfully different scenario (e.g., "deploy a self-hosted chatbot" vs. "build a RAG pipeline"), not just a restatement of the query.
• For each row, select the single best repo from the top 10 results.
• Score column: show sc=N using the item's quality score.
• Why: write a 10–15 word reason in the query language explaining the practical benefit. Do not copy the description verbatim.
• If two use cases map to the same repo, merge them into one row or drop the weaker one.
• If there are fewer than 3 meaningfully distinct use cases in the results, output as many rows as make sense (minimum 1).