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/kengo006/alexandriaWrote 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/kengo006/alexandria/ingest)<a href="https://agentmods.dev/commands/kengo006/alexandria/ingest"><img src="https://agentmods.dev/badge/commands/kengo006/alexandria/ingest.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 | $0.00000 | $0.00201 |
| Opus 5 | $0.00000 | $0.00101 |
| Sonnet 5 | $0.00000 | $0.00040 |
| Haiku 4.5 | $0.00000 | $0.00020 |
Grade A, and why
ingest 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 7d 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
/ingest — enter Librarian mode
A slash-command entry point for the most frequent maintenance session. Install as .claude/commands/ingest.md.
Enter Librarian mode. Read, in order:
0. {your governance layer, if any}
1. governance/role-division.md
2. roles/librarian.md
3. obsidian/note-schema.md, obsidian/vault-structure.md, obsidian/wikilinks-and-mocs.md
4. notes/vault-map.md
5. the errata queue, if one is pending
Then reply exactly: "📚 Librarian ready — inbox, errata, or maintenance?"
and wait for instructions. Remember: gates before notes; serial when content
depends on the source; the three-part completion report before "done".
The fixed ready-line is deliberate: it confirms the reading happened and telegraphs the three standard job types. Adapt the wording; keep the confirmation pattern.
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.
- 7d ago First seen · 20 lines · 0 tokens per session scan A 7d9ee3df3fb5
ingest is a command published in the GitHub repository kengo006/alexandria (5 stars, last pushed 15d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 201 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 2026-08-31.
Other commands, from other repositories
obsidian-recap
Summarize a time period from the vault - today, week, or month.
kb-ingest
Ingest external material into Sources/ inside the bound project KB, then update registry, index, and daily note as needed.
ars-lit-review
ARS academic-paper lit-review mode — annotated bibliography in paper format.
ars-plan
ARS academic-paper plan mode — Socratic chapter-by-chapter planning.
voice-compliance
Voice/telephony compliance check — invokes voice-ai-reviewer to produce TM-voice-{slug}.md with TCPA, STIR/SHAKEN, state recording-consent, EU AI Act Art. 50, and synth-voice deepfake-law gaps.
graphify
Turn your vault into a clustered knowledge graph with HTML and JSON outputs.