Borrowing it
Nothing to install: this file belongs to ystreibel/logseq-wiki. 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/ystreibel/logseq-wiki/main/.skills/vault-skill-factory/SKILL.mdgit clone --depth 1 https://github.com/ystreibel/logseq-wikiWrote 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/ystreibel/logseq-wiki/vault-skill-factory)<a href="https://agentmods.dev/skills/ystreibel/logseq-wiki/vault-skill-factory"><img src="https://agentmods.dev/badge/skills/ystreibel/logseq-wiki/vault-skill-factory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ystreibel/logseq-wiki/vault-skill-factory"><img src="https://agentmods.dev/badge/skills/ystreibel/logseq-wiki/vault-skill-factory.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00084 | $0.00905 |
| Opus 5 | $0.00042 | $0.00452 |
| Sonnet 5 | $0.00017 | $0.00181 |
| Haiku 4.5 | $0.00008 | $0.00090 |
Grade A, and why
vault-skill-factory 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vault Skill Factory — Notes → Portable Skill
REQUIRED: Invoke llm-wiki first for Logseq syntax; this skill also builds on skill-creator.
Compile a dense cluster of wiki pages into a distributable skill: the accumulated knowledge becomes an "expert" other agents can load, instead of re-querying the vault each time (the "compile once, keep current" principle applied to expertise).
Before You Start
- Read
~/.logseq-wiki/config(preferred) or.envto getLOGSEQ_VAULT_PATH - Read
wiki/_master-index.mdto see themes and page density - Identify the target cluster: a theme, or a hub page + its neighbors (use
wiki-statushubs or ask the user)
Step 1: Assess Maturity
A cluster is skill-ready only if it is substantial and stable:
- ≥ 4–5 interconnected pages on the topic (check density with the graph, excluding
_index) - Pages carry real distilled knowledge (facts, decisions, patterns), not mostly
TODO/stubs - Content is stable (not actively churning). If the cluster is thin or volatile, say so and stop — suggest more ingestion first rather than packaging premature knowledge.
Step 2: Extract the Expertise
From the cluster, distill into skill material:
- Scope — what the expert knows and when it applies
- Core facts & patterns — the reusable knowledge (not vault-specific navigation)
- Decision rules / heuristics the pages encode
- Pointers back to the source pages
[[wiki/theme/page]]for provenance - Preserve
^[inferred]/^[ambiguous]— an expert states its confidence
Strip anything vault-private that should not travel (personal notes, internal identifiers) unless the user wants an internal-only skill.
Step 3: Generate the Skill
Invoke the skill-creator skill to produce the new skill (it owns the authoring workflow, evals
and validation — do not hand-roll the SKILL.md structure). Pass it:
name: a clear, hyphenated expert name (e.g.gcp-architecture-expert)description: third-person "Use when…" triggering conditions only (no workflow summary)- Body: the distilled expertise, with a "Sources" section linking the originating wiki pages
- Keep under ~500 lines; move heavy reference to supporting files
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.
- 10d ago First seen · 72 lines · 84 tokens per session scan A 2b9f3d5be8b9
vault-skill-factory is a skill published in the GitHub repository ystreibel/logseq-wiki (4 stars, last pushed 15d ago), licensed MIT. It adds 84 tokens to every session and 905 once invoked, about $0.0004 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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