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 glebis/claude-skills --skill temple-generatorgit clone --depth 1 https://github.com/glebis/claude-skillsWrote 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/glebis/claude-skills/temple-generator)<a href="https://agentmods.dev/skills/glebis/claude-skills/temple-generator"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/temple-generator/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/glebis/claude-skills/temple-generator"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/temple-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.01487 |
| Opus 5 | $0.00021 | $0.00744 |
| Sonnet 5 | $0.00008 | $0.00297 |
| Haiku 4.5 | $0.00004 | $0.00149 |
Grade A, and why
temple-generator 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 6d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Temple Generator
Generate a 3D interactive knowledge visualization from any Obsidian vault. The output is a single HTML file (Three.js) with concentric entity rings, audio, discovery mechanics, and multi-scale semantic zoom.
When to Use
- User wants to visualize any Obsidian vault as a 3D knowledge map
- User wants to compare two vaults/document sets visually
- User wants to regenerate the temple from scratch with fresh vault analysis
Architecture
Two-part system:
- Generation pipeline (this skill): discovers structure, names it, scores confidence, exports a scene package
- Runtime renderer (template): handles navigation, transitions, audio, discovery
Pre-generate meaning. Runtime-render experience.
Workflow
Step 1: Scan the Vault
Run python3 ~/.claude/skills/temple-generator/scripts/extract_entities.py <vault_path>.
This produces vault-scan.json with:
- Files: path, title, tags, outgoing links, backlink counts, word count, folder, frontmatter
- Graph: adjacency list with bidirectional link counts
- Centrality: degree centrality per node
- Clusters: detected groups of tightly linked notes
Step 2: Read the Scan + Sample Notes
- Read
vault-scan.json - Read the top ~20 nodes by centrality (first 100 lines each)
- Read
references/classification-guide.mdfor entity type heuristics - Read 3-5 representative notes to calibrate the vault's "voice" (formal/informal, domain jargon, language)
Step 3: Classify Entities
Using references/classification-guide.md, assign each significant node to an entity type. Maintain two vocabularies:
- canonical: neutral labels for portability (
anxiety-management,fermentation-process) - poetic: mythic/art labels for the installation (
The Ferment Gate,The Cortisol Throne)
Target counts per type (adjust for vault size):
| Type | Small vault (< 100) | Medium (100-500) | Large (500+) |
|---|---|---|---|
| Gods | 2-3 | 3-5 | 5-7 |
| Demigods | 3-7 | 5-12 | 8-15 |
| Tensions | 2-4 | 3-7 | 5-9 |
| Narratives | 2-5 | 5-10 | 8-12 |
| Blind spots | 1-3 | 3-5 | 4-7 |
| Spirits | 1-3 | 3-5 | 3-5 |
| Research | 5-15 | 10-25 | 15-30 |
| Values | 2-5 | 3-8 | 5-10 |
| Trails | 2-5 | 3-8 | 5-10 |
| Questions | 3-6 | 5-10 | 8-12 |
| Depths | 2-5 | 5-10 | 8-15 |
| Crystals | 1-3 | 2-5 | 3-6 |
What ships with it
7 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.
- 6d ago First seen · 150 lines · 42 tokens per session scan A 749db1b7a143
temple-generator is a skill published in the GitHub repository glebis/claude-skills (374 stars, last pushed 8d ago), licensed MIT. It adds 42 tokens to every session and 1,487 once invoked, about $0.0002 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 2026-09-03.
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