search

Search the awesome-ChatGPT-repositories database for the user's query. ## Instructions Ground rules — they apply whether this skill was invoked explicitly or picked automatically: - Present only repositories from the bundled data files, even when the list has just a few matches — then say so and suggest other keywords rather than filling the gap from elsewhere. - Don't search the web or open repository pages to add or verify details (setup, license, activity) unless the user explicitly asks for that: the curated list is the source of truth here, and its star counts and descriptions are snapshots. Copy names, URLs, and star counts exactly as they appear in the records. - The output templates in Steps 5b–7 are fixed: keep their Markdown headings (`##`, `###`), field labels, and order, and don't restyle them (for example, turning the result list into a table or a prose summary). - If the data files can't be read (for example, shell commands are blocked), say so and link https://github.com/taishi-i/awesome-ChatGPT-repositories instead of substituting other sources. ### Step 1 — Interpret the query The query is the text the user passed to this skill (the same skill runs in Claude Code and Codex): - **Claude Code:** the arguments of `/awesome-chatgpt-search:search`, appended at the end as `ARGUMENTS: …`. - **Codex:** the user's message that invoked `$awesome-chatgpt-search:search`, minus the `$…` mention itself. If there is no explicit query text, use the user's latest request. 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 the keyword search, 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 read whole files into context (no Read tool, `cat`, or full-file dumps).** Locate the data directory once — it is the plugin's `data/` folder, two levels above this SKILL.md: - **Claude Code:** `${CLAUDE_PLUGIN_ROOT}/data` - **Codex:** `<directory of this SKILL.md>/../../data`, built from the absolute path you loaded this SKILL.md from. If that directory does not exist (unusual install), find it — the data directory is the folder that contains the printed file: ``` find "${CODEX_HOME:-$HOME/.codex}/plugins" "$HOME/.claude/plugins" "$PWD" -type f -name repos-unity.json -path "*awesome-chatgpt-search*" 2>/dev/null | head -1 ``` Shell variables may not persist between commands, so write the resolved absolute path in place of `$DATA` in the commands below. 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 more general single-word stems or tool names from Step 1 — multi-word phrases rarely match) and re-run. - If it returns the full `head` cap, your keywords are good; proceed. - Only fall back to reading 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 **10** results — fewer only when fewer candidates actually fit the query. 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` 4. **Specificity** — a repo specialized for the exact use-case beats a general one. 5. **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. Count the repositories per category from the data (one cheap command, so the numbers always match the bundled data): ``` grep -ho '"c":"[^"]*"' "$DATA"/repos-*.json | sort | uniq -c ``` Present the counts in this order, followed by the total: ``` ## Available categories | Category | Count | |----------|-------| | Awesome-lists | N | | Prompts | N | | Chatbots | N | | Browser-extensions | N | | CLIs | N | | Reimplementations | N | | Tutorials | N | | NLP | N | | Langchain | N | | Unity | N | | Openai | N | | Others | N | | **Total** | **N** | ``` ### Step 6 — Format the output ```