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

## Search results for "<query>"

*(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. ...

Fill every field from the record: link text n, URL u, category c, language l, stars st as-is, the description d verbatim (you may drop :emoji: shortcodes), and topics from t (trim long lists to about 8). The results stay in English like the data; only the Step 7 guide follows the query language.

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).