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 agentmods add skills/mark393295827/graph-engineering-architectures/graph-engineeringnpx skills add Mark393295827/graph-engineering-architectures --skill graph-engineeringgit clone --depth 1 https://github.com/Mark393295827/graph-engineering-architecturesWrote 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/mark393295827/graph-engineering-architectures/graph-engineering)<a href="https://agentmods.dev/skills/mark393295827/graph-engineering-architectures/graph-engineering"><img src="https://agentmods.dev/badge/skills/mark393295827/graph-engineering-architectures/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 | $0.00033 | $0.01589 |
| Opus 5 | $0.00016 | $0.00794 |
| Sonnet 5 | $0.00007 | $0.00318 |
| Haiku 4.5 | $0.00003 | $0.00159 |
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 3d 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
92% identical to graph-engineering — 8 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graph Engineering
<skill_contract> A dependency-heavy objective with candidate nodes, data schemas, owners, effects, verifiers, joins, budgets, and durable state paths. A validated static DAG contract with typed edges, explicit joins, node-local recovery, and graph-level receipts. Static invariants and terminal acceptance checks pass with fresh node, join, budget, permission, and state evidence. <non_goals>Temporal loop design, worker-team command, runtime-kernel implementation, dynamic graphs, or universal parallelism.</non_goals>
Use Graph Engineering for dependency width. Use loop-engineering for repeated
execution through time, agent-teams-command for process ownership and IPC,
and harness-engineering for scheduler, permission, lease, and observability
infrastructure. A graph node may contain a bounded Loop or Agent Team.
Usage Template
Provide: objective/non-goals, candidate nodes, real data dependencies, payload
schemas, owners and write territories, join semantics, node/terminal verifiers,
effects and permissions, artifact/state paths, budgets, stop conditions, and
recovery. Load references/graph-contract.md for the full schema and boundary;
start from references/diamond-graph-example.json.
Workflow
Run the admission gate before drawing a graph:
- Identify which steps actually consume another step's output.
- Estimate independent width, critical path, scheduler overhead, and review load. Require measurable payback or stronger independent evaluation.
- Keep one-shot or Loop execution when work is mainly sequential, small, or cheaper to review serially.
- Limit V7.1 to a static DAG: sequence, pipeline, diamond, maker-checker, or
bounded subgraph. Put repetition inside a
loopnode; reject graph cycles and dynamic expansion.
<unknowns_gate>
Return NEEDS_INPUT when objective, graph owner, dependency direction, payload
schema, writer, verifier, permission boundary, budget, join, or recovery is
missing and cannot be discovered safely. Probe candidate independence with a
small dry run. Do not invent an edge merely because two steps are adjacent.
What ships with it
4 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.
- 3d ago First seen · 161 lines · 33 tokens per session scan A b35b14814cf6
graph-engineering is a skill published in the GitHub repository Mark393295827/graph-engineering-architectures (2 stars, last pushed 13d ago), licensed MIT. It adds 33 tokens to every session and 1,589 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to graph-engineering, differing in 8 lines, and is treated as a copy.
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