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 docxology/template --skill knowledge_graphgit clone --depth 1 https://github.com/docxology/templateWrote 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/docxology/template/knowledge_graph)<a href="https://agentmods.dev/skills/docxology/template/knowledge_graph"><img src="https://agentmods.dev/badge/skills/docxology/template/knowledge_graph/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/docxology/template/knowledge_graph"><img src="https://agentmods.dev/badge/skills/docxology/template/knowledge_graph.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 | $0.00204 |
| Opus 5 | $0.00012 | $0.00102 |
| Sonnet 5 | $0.00005 | $0.00041 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
LLM Extraction & KG Construction 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 5d 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
Instructions
You are interacting with the src/knowledge_graph/ module. This handles the transition from natural language text to structured assertions via Large Language Models.
Agentic Interface (MCP Strategy)
- Config-Driven Orchestration: Never hardcode LLM parameters (models, temperature, minimum confidence thresholds) within Python scripts. Bind them tightly to
LLMConfigstructures derived securely fromconfig.yaml. - Robust Categorization: Respect strict boundaries on confidence gating. When crafting or modifying prompt templates, ensure the extraction schema firmly permits "irrelevant", "neutral", and "don't know" states to neutralize base-model hallucination tendencies.
- Streaming Persistence: Assertions must emit safely to disk via incremental JSON Lines or valid TriG records. High-volume document processing should not accumulate global lists in RAM.
What ships with it
15 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.
- __init__.py 929 B runs code
- AGENTS.md 4.1 KB
- calibration.py 3.1 KB runs code
- extraction.py 2.0 KB runs code
- graph_builder.py 10 KB runs code
- hypothesis.py 11 KB runs code
- kg_runner.py 8.7 KB runs code
- llm_client.py 3.5 KB runs code
- llm_config.py 551 B runs code
- llm_extraction.py 7.4 KB runs code
- llm_prompts.py 1.6 KB runs code
- nanopublication.py 14 KB runs code
- query.py 3.5 KB runs code
- README.md 3.9 KB
- schema.py 3.4 KB runs code
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.
- 5d ago First seen · 15 lines · 23 tokens per session scan A e39c7f58c01d
LLM Extraction & KG Construction is a skill published in the GitHub repository docxology/template (19 stars, last pushed today), licensed Apache-2.0. It adds 23 tokens to every session and 204 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 2026-09-03.
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