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 Mathews-Tom/armory --skill kg-buildergit clone --depth 1 https://github.com/Mathews-Tom/armoryWrote 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/mathews-tom/armory/kg-builder)<a href="https://agentmods.dev/skills/mathews-tom/armory/kg-builder"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/kg-builder/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/mathews-tom/armory/kg-builder"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/kg-builder.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 | $0.00100 | $0.02450 |
| Opus 5.5 | $0.00040 | $0.00980 |
| Sonnet 5.5 | $0.00020 | $0.00490 |
| Haiku 4.5 | $0.00010 | $0.00245 |
Grade A, and why
kg-builder 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.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KG Builder
A knowledge graph is a product with a schema, not a pile of triples. Quality comes from pipeline order: model the domain before extracting, validate during extraction, fuse before storing, and attach provenance to every fact from the first write.
This skill covers the full build — value test, ontology, extraction, quality gate, entity resolution, serving, and maintenance — plus the boundary question that decides whether the result is trustworthy: which stages are deterministic code and which are LLM judgment.
Scope note. This is about knowledge graphs — what an agent remembers. It is not about task graphs, agent orchestration, or multi-agent topology.
Reference Files
| File | Contents | Load When |
|---|---|---|
references/ontology-design.md |
Competency questions, entity/relation types, domain/range, storage choice | Phase 1 |
references/extraction.md |
Source routing, NER/RE/EE prompt patterns, validation, failure modes | Phase 2 |
references/fusion.md |
Blocking, matching layers, merge policy, threshold bands | Phase 3 |
references/serving.md |
GraphRAG retrieval, path queries, community summaries, query layer | Phase 4 |
references/provenance-and-supersession.md |
Claim model, append-only updates, contradiction handling, audit trail | Phase 1 and Phase 4 |
The deterministic / LLM boundary
Decide this before writing code. Code owns control flow, identity, validation, and merges. The model gets contained judgments behind a typed interface, each with a measured baseline.
| Stage | Deterministic (code) | LLM judgment (measure it) |
|---|---|---|
| Source routing | format detection, structured mapping | — |
| Entity extraction | span capture, type validation, dictionary matching | "what entities are in this text" |
| Relation extraction | domain/range enforcement, endpoint checks | "which relation does this sentence assert" |
| Quality gate | sampling, scoring, thresholds | — |
| Blocking | key generation, candidate pairing | — |
| Matching | string/attribute/structure scoring | ambiguous middle band only |
| Merge | canonical selection, edge union, lineage | — (never let a model own a merge) |
| Serving | traversal, subgraph selection, serialization | the agent's own reasoning |
What ships with it
8 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.
- 1mo ago First seen · 172 lines · 100 tokens per session scan A c1f103beceb8
kg-builder is a skill published in the GitHub repository Mathews-Tom/armory (337 stars, last pushed 2d ago), licensed MIT. It adds 100 tokens to every session and 2,450 once invoked, about $0.0004 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 2026-08-30.
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