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 Jamie-BitFlight/claude_skills --skill graph-engineeringgit clone --depth 1 https://github.com/Jamie-BitFlight/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/jamie-bitflight/claude_skills/graph-engineering)<a href="https://agentmods.dev/skills/jamie-bitflight/claude_skills/graph-engineering"><img src="https://agentmods.dev/badge/skills/jamie-bitflight/claude_skills/graph-engineering.svg" alt="Measured on agentmods" 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 | $0.00152 | $0.01842 |
| Opus 5 | $0.00076 | $0.00921 |
| Sonnet 5 | $0.00030 | $0.00368 |
| Haiku 4.5 | $0.00015 | $0.00184 |
Grade A, and why
graph-engineering 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 7d 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.
This is a copy
94% identical to graph-engineering — 26 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graph Engineering
Graph engineering is the discipline of designing the structures agents work through — not the prompts. It has two halves:
- Knowledge graphs — what agents remember. Nodes are entities and facts, edges are relationships with time and provenance. This file's 9-stage pipeline covers it, distilled from Southeast University's graduate KG course (https://github.com/npubird/KnowledgeGraphCourse, Prof. Peng Wang), translated to English and adapted for LLM-era agents.
- Task graphs — how agents work. Nodes are jobs, edges are execution dependencies: parallel fan-out, separate verifier contexts, the stop rule, the human gate. Read references/task-graphs.md when the request is about orchestrating agents rather than building memory. The task graph decides the execution shape once; use teammode only for a ready parallel component whose members must coordinate. Keep serial chains with one agent and perfectly isolated parallel nodes as plain subagents.
Core mental model: a knowledge graph is a product with a schema, not a pile of triples. Quality comes from the pipeline order — model the domain BEFORE extracting, fuse BEFORE storing, evaluate at every stage.
Teaching Mode
When the user wants to LEARN graph engineering (rather than build something), teach it — do not just execute. Rules:
- Anchor every stage in the user's own domain: ask for one real project or dataset, then use it as the running example through all stages.
- Generate visual artifacts as you teach. Concepts in this discipline are shapes; show
them. For each major concept, produce a small diagram the user can keep — mermaid diagrams
(flowchart for the pipeline and task graphs,
graph LRfor example ontologies and subgraphs) or a single self-contained HTML page when interactivity helps. At minimum: the 9-stage pipeline, a 3-type ontology drawn from the user's domain, one extracted subgraph (5-10 nodes) from a real sample, and the diamond pattern with the user's own jobs as nodes. - Teach in the pipeline's order, one stage per exchange, each ending with a small exercise ("write 3 competency questions for your project") before moving on.
- Close by assembling what was built during the lesson into a starter
ontology.yamland a drawn task graph for the user's first real build.
What ships with it
5 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.
- 7d ago First seen · 124 lines · 152 tokens per session scan A 7f1c54ccf561
graph-engineering is a skill published in the GitHub repository Jamie-BitFlight/claude_skills (65 stars, last pushed today), licensed MIT. It adds 152 tokens to every session and 1,842 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to graph-engineering, differing in 26 lines, and is treated as a copy.
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