embed

embed is a command for Claude Code from eric861129/SKILLS_All-in-one. It costs 10 tokens per session (598 once invoked), scanned A, original, MIT.

A command that turns text into an embedding, a numerical representation used to compare meaning between pieces of text.

In plain words
What is it for?
Generating embeddings for search, similarity matching, and multilingual text retrieval.
Why use it?
It provides the vector data needed for semantic search, where results are found by meaning rather than exact keywords.

Command for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Install

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.

agentmods
npx agentmods add commands/eric861129/skills_all-in-one/embed
Clone the repo
git clone --depth 1 https://github.com/eric861129/SKILLS_All-in-one

Made for: Claude Code.

Wrote 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.

agentmods badge for embed

README.md
[![agentmods](https://agentmods.dev/badge/commands/eric861129/skills_all-in-one/embed.svg)](https://agentmods.dev/commands/eric861129/skills_all-in-one/embed)
Your own site
<a href="https://agentmods.dev/commands/eric861129/skills_all-in-one/embed"><img src="https://agentmods.dev/badge/commands/eric861129/skills_all-in-one/embed.svg" alt="Measured on agentmods" height="20"></a>
Per session 10 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 598 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00010 $0.00598
Opus 5 $0.00005 $0.00299
Sonnet 5 $0.00002 $0.00120
Haiku 4.5 $0.00001 $0.00060

Measured 2d ago against content hash ad36d302d485, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

embed scanned grade A 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 2d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -s -X POST "https://api.deapi.ai/api/v1/client/txt2embedding" \
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

  • embed — 100% identical, 0 lines differ
public/SKILLS/Media & Content/deapi-ai-media-toolkit/commands/embed.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Text Embeddings via deAPI

Generate embeddings for: $ARGUMENTS

Step 1: Validate input

Verify $ARGUMENTS contains text to embed:

  • Text should not be empty
  • Maximum recommended length: 8192 tokens
  • For longer texts, consider chunking

Step 2: Send request

curl -s -X POST "https://api.deapi.ai/api/v1/client/txt2embedding" \
  -H "Authorization: Bearer $DEAPI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "$ARGUMENTS",
    "model": "Bge_M3_FP16"
  }'

Model info:

Model Dimensions Best for
Bge_M3_FP16 1024 High accuracy, semantic search, multilingual

Step 3: Poll status (feedback loop)

Extract request_id from response, then poll every 10 seconds:

curl -s "https://api.deapi.ai/api/v1/client/request-status/{request_id}" \
  -H "Authorization: Bearer $DEAPI_API_KEY"

Status handling:

  • processing → wait 10s, poll again
  • done → proceed to Step 4
  • failed → report error message to user, STOP

Step 4: Fetch and present result

When status = "done":

  1. Get embedding vector from response
  2. Show vector dimensions and sample values
  3. Offer to save or use the embedding

Output format:

{
  "embedding": [0.123, -0.456, 0.789, ...],
  "dimensions": 1024,
  "model": "Bge_M3_FP16"
}

Step 5: Offer follow-up

Ask user:

  • "Would you like to embed more text for comparison?"
  • "Should I calculate similarity between embeddings?"
  • "Would you like to save these embeddings to a file?"

Use cases

  • Semantic search: Find similar documents
  • Clustering: Group related content
  • RAG: Retrieval-augmented generation
  • Recommendations: Content similarity

Error handling

Error Action
401 Unauthorized Check if $DEAPI_API_KEY is set correctly
429 Rate Limited Wait 60s and retry
500 Server Error Wait 30s and retry once
Empty text Ask user to provide text to embed
Text too long Suggest chunking into smaller segments

Read the full file on GitHub · 88 lines

Changes

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.

  1. 2d ago First seen · 88 lines · 10 tokens per session scan A ad36d302d485

Subscribe to this mod's changes

embed is a command published in the GitHub repository eric861129/SKILLS_All-in-one (52 stars, last pushed 4mo ago), licensed MIT. It adds 10 tokens to every session and 598 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.