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 mike-coulbourn/claude-vibes --skill graph-engineeringgit clone --depth 1 https://github.com/mike-coulbourn/claude-vibesWrote 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/mike-coulbourn/claude-vibes/graph-engineering)<a href="https://agentmods.dev/skills/mike-coulbourn/claude-vibes/graph-engineering"><img src="https://agentmods.dev/badge/skills/mike-coulbourn/claude-vibes/graph-engineering/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/mike-coulbourn/claude-vibes/graph-engineering"><img src="https://agentmods.dev/badge/skills/mike-coulbourn/claude-vibes/graph-engineering.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 | $0.00070 | $0.03886 |
| Opus 5.5 | $0.00028 | $0.01554 |
| Sonnet 5.5 | $0.00014 | $0.00777 |
| Haiku 4.5 | $0.00007 | $0.00389 |
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 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 — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graph Engineering
Engineer the graph. Do not run it.
Design the work around AI instead of relying on one giant chat.
Design-only boundary
The sole deliverable of this skill is an approval-ready graph. This boundary applies in every mode, including Plan Mode.
Treat verbs such as determine, research, validate, build, or implement as the outcome the future graph must produce—not as permission to perform that work now.
While designing:
- inspect the user's context only enough to understand the objective, constraints, and consequential decisions;
- ask only questions whose answers could materially change the graph;
- identify the evidence and access needed to resolve those decisions;
- turn researchable unknowns into jobs inside the graph;
- place a human gate before any future evidence method that needs new authority;
- do not browse or investigate those unknowns;
- do not launch workers, subagents, skeptics, or mergers;
- do not execute any graph job or produce the graph's terminal deliverable; and
- do not modify the target project or external systems.
Stop after presenting the graph for human approval. The displayed nodes are future jobs. Execution begins only after the user has seen and approved the graph, and it happens outside this skill.
Align before engineering
Begin every invocation with an adaptive alignment interview.
First inspect the conversation, supplied artifacts, current authoritative context, and previously settled decisions. Do not ask the user for information that is already available and current.
Resolve every material ambiguity about:
- the objective and why it matters;
- the expected terminal result;
- what success and failure mean;
- what the result should optimize for;
- priorities, non-negotiables, and unacceptable tradeoffs;
- scope and non-goals;
- authoritative versus stale context;
- constraints involving evidence, access, authority, time, or cost;
- decisions the graph may make; and
- decisions that belong to the human.
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 · 245 lines · 70 tokens per session scan A ed85cf9e5384
graph-engineering is a skill published in the GitHub repository mike-coulbourn/claude-vibes (44 stars, last pushed 1mo ago), licensed MIT. It adds 70 tokens to every session and 3,886 once invoked, about $0.0003 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 2026-09-22.
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