Borrowing it
Nothing to install: this file belongs to velmighty/youtube-to-knowledge. 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/velmighty/youtube-to-knowledge/main/.claude/commands/kg_navigator.mdgit clone --depth 1 https://github.com/velmighty/youtube-to-knowledgeWrote 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/commands/velmighty/youtube-to-knowledge/kg_navigator)<a href="https://agentmods.dev/commands/velmighty/youtube-to-knowledge/kg_navigator"><img src="https://agentmods.dev/badge/commands/velmighty/youtube-to-knowledge/kg_navigator/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/commands/velmighty/youtube-to-knowledge/kg_navigator"><img src="https://agentmods.dev/badge/commands/velmighty/youtube-to-knowledge/kg_navigator.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 | $0.00000 | $0.00322 |
| Opus 5 | $0.00000 | $0.00161 |
| Sonnet 5 | $0.00000 | $0.00064 |
| Haiku 4.5 | $0.00000 | $0.00032 |
Grade A, and why
kg_navigator 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
Knowledge graph specialist for entity relationships across videos.
Entity resolution
- Use canonical full names: "Michał Sadowski" not "Mike". Check metadata.json for channel/speaker names.
- Before adding a node, search existing graph.json for equivalent nodes (e.g., "AI" = "Artificial Intelligence").
- Normalize: strip whitespace, consistent capitalization.
Triplet quality rules
- Predicates MUST be specific verbs: "founded", "acquired", "recommends", "competes_with", "increased_by".
- NEVER use vague predicates: "is related to", "is associated with", "is connected to", "involves".
- Each triplet must be verifiable from the transcript. No inferences.
Depth tiers
- light (8-12 triplets): Primary entities and direct relationships only.
- standard (15-20 triplets): People, companies, tools, concepts.
- deep (25-35 triplets): Add causal chains, temporal relations, attributed claims, quantities.
Graph operations
- Update:
python src/graph_extractor.py <dir> <dir>/triplets.json - Always load existing graph.json first — merge, don't replace.
- Find hubs: nodes with highest degree are key entities.
- Cross-video: check all
vault/content/*/graph.jsonfor shared nodes before extracting.
Data sources
- Per-video:
vault/content/<channel>/graph.json,graph.html - Triplets:
vault/content/<channel>/triplets.json
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 · 27 lines · 0 tokens per session scan A b050eacb20db
kg_navigator is a command published in the GitHub repository velmighty/youtube-to-knowledge (59 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 322 tokens. 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.
Other commands, from other repositories
voxpip
Watch any video and get a transcript — even without an API key. Extends /watch with a local speech-to-text fallback.
kg-ingest
Ingest a source document into the Knowledge Graph - extract entities, concepts, create wiki pages.
kg-init
Initialize a new Knowledge Graph with raw/ and wiki/ structure.
laravel-ai-sdk
Build AI features with the first-party Laravel AI SDK (Laravel 13+); use the laravel:ai-sdk skill exactly as written.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.
prompt-create
Create a new prompt following ground rules.