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 burakolgun/vault-anything --skill vaultgit clone --depth 1 https://github.com/burakolgun/vault-anythingWrote 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/burakolgun/vault-anything/vault)<a href="https://agentmods.dev/skills/burakolgun/vault-anything/vault"><img src="https://agentmods.dev/badge/skills/burakolgun/vault-anything/vault/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/burakolgun/vault-anything/vault"><img src="https://agentmods.dev/badge/skills/burakolgun/vault-anything/vault.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 | $0.00023 | $0.03483 |
| Opus 5 | $0.00012 | $0.01741 |
| Sonnet 5 | $0.00005 | $0.00697 |
| Haiku 4.5 | $0.00002 | $0.00348 |
Grade A, and why
vault scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/vault-anything:vault
Analyzes a project's source code and any static documents the user has placed under raw/, then generates a Karpathy-style Obsidian wiki vault where every page is self-contained and useful as either human documentation or LLM context.
Arguments
| Argument | Default | Description |
|---|---|---|
project-dir |
current directory | Source code to analyze |
--workspace |
<project>/vault-anything/ |
Where raw/ and vault/ are written |
--lang |
en |
Vault page output language (must match a file in locales/) |
Phase 0 — Pre-flight
Run the resolvers to get plugin and workspace paths. Save the outputs into shell variables you'll use throughout:
set -e
PLUGIN_ROOT=$(node "$CLAUDE_SKILL_DIR/resolve-plugin-root.mjs")
WORKSPACE=$(node "$PLUGIN_ROOT/skills/vault/resolve-workspace.mjs" "$@")
Parse $WORKSPACE (it's a JSON object) into shell variables with jq:
PROJECT_DIR=$(echo "$WORKSPACE" | jq -r '.projectDir')
PROJECT_NAME=$(echo "$WORKSPACE" | jq -r '.projectName')
WORKSPACE_DIR=$(echo "$WORKSPACE" | jq -r '.workspaceDir')
RAW_DIR=$(echo "$WORKSPACE" | jq -r '.rawDir')
VAULT_DIR=$(echo "$WORKSPACE" | jq -r '.vaultDir')
EXISTS=$(echo "$WORKSPACE" | jq -r '.exists')
VAULT_LANG="en" # parse from --lang argument if user provided one
Decision:
- If
$EXISTSis"true", this project has been analyzed before. Ask the user which path to take:- (a) Full re-analysis (all phases run again)
- (b) Just update what changed → tell the user to run
/vault-anything:vault-updateinstead, then STOP. - (c) Skip scan + analyze, regenerate vault from existing
raw/(jump to Phase 3).
- If
$EXISTSis"false", proceed with full analysis.
Set up progress tracking
Before running the phases, use the TaskCreate tool to create a top-level task list the user can watch. The tasks are:
- Phase 1: Scan project files
- Phase 2: Analyze files
- Phase 3: Cluster concepts + write skeleton
- Phase 4: Write pages
- Phase 5: Quality review
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 357 lines · 23 tokens per session scan A 6a114b4d26af
vault is a skill published in the GitHub repository burakolgun/vault-anything (4 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 3,483 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.
Other skills, from other repositories
ingest
Populate the gnosis-mcp knowledge base — from local files, git history, or a crawled website. Handles the full matrix of flags (--force, --prune, --wipe, --embed, --include-crawled) in one place.
manage
CRUD operations on the knowledge base — add, delete, update metadata. For bulk ingest / re-ingest / prune, use /gnosis:ingest instead. Requires GNOSISMCPWRITABLE=true.
setup
First-time setup wizard for Gnosis MCP. Install, init the database, ingest a docs folder, wire your editor — in that order.
status
Verify gnosis-mcp server connectivity, schema integrity, and corpus health. Use when MCP calls fail, return empty, or return unexpected data.
tune
Find the chunk-size and retrieval config that maximizes quality on YOUR corpus. Sweeps chunk sizes, runs a golden-query set, reports nDCG / MRR / Hit@5. Use after first ingest or whenever your corpus changes shape significantly.
eval
Measure retrieval quality on your corpus — Hit@5, MRR, nDCG@10, Precision@5. Thin wrapper around gnosis-mcp eval with regression tracking against a saved baseline, plain-English interpretation, and tuning pointers when numbers look off. Use after every ingest or config change.