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 skills add Evanciel/stellavault --skill vault-reviewgit clone --depth 1 https://github.com/Evanciel/stellavaultWrote 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/evanciel/stellavault/vault-review)<a href="https://agentmods.dev/skills/evanciel/stellavault/vault-review"><img src="https://agentmods.dev/badge/skills/evanciel/stellavault/vault-review/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/evanciel/stellavault/vault-review"><img src="https://agentmods.dev/badge/skills/evanciel/stellavault/vault-review.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 | $0.00068 | $0.00372 |
| Opus 5.5 | $0.00027 | $0.00149 |
| Sonnet 5.5 | $0.00014 | $0.00074 |
| Haiku 4.5 | $0.00007 | $0.00037 |
Grade A, and why
vault-review 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 1mo 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
Knowledge review (decay + gaps)
Turn Stellavault's memory model into a short, actionable review. This is the one thing a plain notes app cannot do: it knows what the user is about to forget.
Steps
- Call
get-learning-path— it returns notes ranked by how close they are to being forgotten (FSRS retrievability). - Call
detect-gaps— weakly-linked or underdeveloped areas of the vault. - Produce a compact review, in the user's language:
- Revisit now (2-3 notes): title + one line on why it matters and what it connects to. Prefer notes that are both decaying AND central (linked from others).
- One gap worth closing: a single concrete suggestion ("[[A]] and [[B]] cover the same system but aren't linked", "topic X has 5 fragments and no summary note").
- End with one question inviting action ("want me to pull up the first one?") — then stop. If the user picks a note, fetch it with
get-documentand summarize what they likely forgot.
Rules
- Keep the whole review under ~15 lines. This is a nudge, not a report.
- Read-only: never write, rename, or "fix" notes in this skill.
- If both tools return nothing actionable, say the vault looks healthy in one line — do not pad.
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.
- 1mo ago First seen · 24 lines · 68 tokens per session scan A 939adce93f00
vault-review is a skill published in the GitHub repository Evanciel/stellavault (5 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 372 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
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sibyl
Persistent memory and task coordination for this project. Invoke for any prompt about past work, project state, in-progress tasks, prior decisions, gotchas, or capturing a non-obvious learning. Also covers semantic search across project knowledge and external docs.
cortex-memory
Persistent cognitive memory for AI agents — query, record, review, and consolidate knowledge across sessions with spreading activation, FSRS scheduling, and NLI contradiction detection.
graft
Persistent graph memory shared across conversations and agents. Graft is a prompter, not a cache: it hands you notes that may be close to the problem in front of you, and a close note is already a win - it gives you a starting point and reminds you what was decided before. Search it before non-trivial work, and write…
memory-audit
Maintenance pass + health check of the graft graph. Runs the graft maintain loop: checks what is due, applies the mechanical cleanup, then adjudicates every maintenance candidate (near duplicates, supersessions, contradictions, memories whose source file changed or disappeared, keyword variants, stale low-value notes)…
memoryze
Distill the current conversation (or a specified excerpt) into N high-quality graft nodes and save them. Triggered by /memoryze, "save this to memory", "ricorda questo", "memorize this", or whenever the user explicitly wants the agent to commit knowledge to the persistent graph. The user can hint at granularity…