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/ramonclaudio/skills/embedgit clone --depth 1 https://github.com/ramonclaudio/skillsWhat 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 | $0.00012 | $0.00347 |
| Opus 5 | $0.00006 | $0.00173 |
| Sonnet 5 | $0.00002 | $0.00069 |
| Haiku 4.5 | $0.00001 | $0.00035 |
Grade A, and why
embed 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 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.
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
Run qmd embed $ARGUMENTS. Defaults to incremental (only embeds hashes without vectors).
Useful flags:
-f/--force: clear all vectors and re-embed everything (after model change, dimension mismatch, or strategy switch).-c <name>: scope to one collection (embeds only its pending hashes).-c <name> --forceclears and rebuilds only that collection's vectors and preserves hashes shared with sibling collections. Added in qmd 2.5.0.--chunk-strategy auto: AST-aware chunking for code files (.ts/.tsx/.js/.jsx/.mts/.cts/.mjs/.cjs/.py/.go/.rs). Recommended for code-heavy collections so chunks land on function/class boundaries instead of mid-statement.--max-docs-per-batch N/--max-batch-mb N: bound peak memory on huge collections (defaults: 64 docs / 64 MB, whichever is hit first).
After embedding, run qmd status and confirm zero pending embeds. First run downloads ~300 MB embedding model; pre-download all 3 models (~2 GB) with /qmd:pull.
Embeddings are safe to interrupt and resume — the next run picks up from where it stopped.
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 · 20 lines · 0 tokens per session scan A 8c422487893f
embed is a command published in the GitHub repository ramonclaudio/skills (4 stars, last pushed 3mo ago), licensed MIT. It adds 12 tokens to every session and 347 once invoked, about $0.0001 per session on Opus 5. 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 2026-08-31.
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