Borrowing it
Nothing to install: this file belongs to pass-agent/loomkin. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pass-agent/loomkin/main/.agents/skills/vault-ingest/SKILL.mdgit clone --depth 1 https://github.com/pass-agent/loomkinWrote 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/skills/pass-agent/loomkin/vault-ingest)<a href="https://agentmods.dev/skills/pass-agent/loomkin/vault-ingest"><img src="https://agentmods.dev/badge/skills/pass-agent/loomkin/vault-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 | $0.00017 | $0.00336 |
| Opus 5 | $0.00009 | $0.00168 |
| Sonnet 5 | $0.00003 | $0.00067 |
| Haiku 4.5 | $0.00002 | $0.00034 |
Grade A, and why
vault-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 9d 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
Analyze raw input and route to the right location in the knowledge base.
Classify Content
Determine what this is:
- Strategy, process, or how-to: note (link to parent topic)
- Hub or organizing content: topic
- Product or brand information: project
- Person information: person
- A decision or commitment: decision (also log to decision graph)
- External reference: source
- A feature or product idea: idea
If the input contains multiple distinct concepts, extract each as a separate atomic entry.
Check for Duplicates
vault_search(query: "{key phrases from input}") — if a similar entry exists, suggest updating it instead of creating a new one. Use ask_user to confirm.
Create Entries
vault_create_entry(entry_type: "{classified_type}", ...) for each extracted concept.
For decisions, also create a decision graph node:
decision_log(node_type: "decision", title: "...", confidence: ...)
Link
- Link new notes to parent topics via
vault_link(link_type: "parent") - Link related entries via
vault_link(link_type: "related") - Extract action items to
vault_kanban(action: "add", ...)
Report: files created with paths, topics linked, action items added.
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.
- 9d ago First seen · 46 lines · 17 tokens per session scan A 235db466bd35
vault-ingest is a skill published in the GitHub repository pass-agent/loomkin (179 stars, last pushed 3mo ago), licensed MIT. It adds 17 tokens to every session and 336 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 2026-08-30.
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