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.
npx agentmods add commands/eric861129/skills_all-in-one/embedgit clone --depth 1 https://github.com/eric861129/SKILLS_All-in-oneWrote 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/eric861129/skills_all-in-one/embed)<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>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 | $0.00010 | $0.00598 |
| Opus 5 | $0.00005 | $0.00299 |
| Sonnet 5 | $0.00002 | $0.00120 |
| Haiku 4.5 | $0.00001 | $0.00060 |
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" \ Copies of this mod
1 near-identical copy found in the catalogue:
- embed — 100% identical, 0 lines differ
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 againdone→ proceed to Step 4failed→ report error message to user, STOP
Step 4: Fetch and present result
When status = "done":
- Get embedding vector from response
- Show vector dimensions and sample values
- 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 |
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.
- 2d ago First seen · 88 lines · 10 tokens per session scan A ad36d302d485
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.
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