Awesome Claude Code Toolkit is a curated collection of extensions and configuration for Claude Code, including agents, skills, commands, plugins, hooks, rules, templates, MCP configurations, and companion apps. It is for Claude Code users who want ready-made workflows and integrations for different development tasks. The catalogue add-ons are selected components from this toolkit.
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.
git clone --depth 1 https://github.com/rohitg00/awesome-claude-code-toolkitWrote 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/commands/rohitg00/awesome-claude-code-toolkit/generate-embeddings)<a href="https://agentmods.dev/commands/rohitg00/awesome-claude-code-toolkit/generate-embeddings"><img src="https://agentmods.dev/badge/commands/rohitg00/awesome-claude-code-toolkit/generate-embeddings/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/commands/rohitg00/awesome-claude-code-toolkit/generate-embeddings"><img src="https://agentmods.dev/badge/commands/rohitg00/awesome-claude-code-toolkit/generate-embeddings.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 | $0.00000 | $0.00299 |
| Opus 5.5 | $0.00000 | $0.00120 |
| Sonnet 5.5 | $0.00000 | $0.00060 |
| Haiku 4.5 | $0.00000 | $0.00030 |
Grade A, and why
generate-embeddings scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
/generate-embeddings - Generate Vector Embeddings
Generate vector embeddings for text data using embedding models.
Steps
- Ask the user for the input data: text file, database table, or API responses
- Select the embedding model: OpenAI text-embedding-3, Cohere embed, Sentence-BERT, or local model
- Preprocess the input text: clean, normalize, truncate to model's max token length
- Batch the inputs for efficient API calls (batch size based on model limits)
- Generate embeddings with retry logic for API rate limits and transient errors
- Validate embedding dimensions match the expected model output
- Normalize embeddings to unit length for cosine similarity searches
- Store embeddings with their source text and metadata in the vector database
- Create an index for efficient nearest-neighbor search
- Verify embedding quality by checking similarity of known-similar items
- Report: total items embedded, dimensions, storage size, API cost estimate
- Save the embedding configuration for future regeneration
Rules
- Batch API calls to stay within rate limits and reduce costs
- Implement exponential backoff retry for API failures
- Truncate text to the model's maximum token length before embedding
- Normalize embeddings for consistent similarity calculations
- Store the model name and version with embeddings for reproducibility
- Cache embeddings to avoid regenerating unchanged content
- Monitor API costs and set spending alerts for large datasets
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 · 29 lines · 0 tokens per session scan A b8c61ee66a45
generate-embeddings is a command published in the GitHub repository rohitg00/awesome-claude-code-toolkit (2,727 stars, last pushed 5mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 299 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
t00-ai-dev
A set of instructions for building applications that use artificial intelligence, such as chatbots, document search, or text-generation tools. It covers Claude, retrieval-augmented generation (RAG), embeddings, and vector databases, which help find relevant documents for an AI answer.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.