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 commands/sciencemj/graph-engineering/graph-newgit clone --depth 1 https://github.com/sciencemj/graph-engineeringWhat 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 | $0.00019 | $0.00434 |
| Opus 5 | $0.00010 | $0.00217 |
| Sonnet 5 | $0.00004 | $0.00087 |
| Haiku 4.5 | $0.00002 | $0.00043 |
Grade A, and why
graph-new 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 yesterday.
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
Read the "Building a graph" and "Four ways to build a graph that blows up" sections of the
graph-engineering skill first.
Requirement: $ARGUMENTS
Do this:
- Decompose the requirement into nodes. Hard rules while decomposing:
- Reproducible work — tests, builds, lints, formatters, migrations — must be a
commandnode. Do not wrap it in anai_task. - Every point that needs a human decision (spec choice, naming policy, deploy approval) is a
user_inputor acheckpoint. - Always put a
checkpointin front of anything irreversible (deploy, migration, force push). - A node whose failure should still flow downstream needs an explicit
on_failure: "continue". With the defaulthalt, a failing test kills the run before it can reach the node that fixes it. - Express a backwards path as a
kind: "loop"edge. A cycle made of forward edges is rejected at save.
- Reproducible work — tests, builds, lints, formatters, migrations — must be a
- Check yourself before registering:
- Does the path that decides success or failure have at least one
commandorcondition? Branching on anai_task's report about itself produces well-organized nonsense. - Did you create branches that all merge back into the same node?
- If there is a loop, does something inside it change state, and can the exit condition ever become true?
- Does the path that decides success or failure have at least one
- Register it with
graph_create. If validation rejects it, fix it and register again. - Summarize the nodes and edges in a table, and point out where a human will be asked to step in.
Do not run it. End with "review the graph, edit it if needed, lock it, then run
/graph-engineering:graph-run <name>".
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.
- yesterday First seen · 33 lines · 19 tokens per session scan A 2c686a76396a
graph-new is a command published in the GitHub repository sciencemj/graph-engineering (0 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 434 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 2026-08-31.
Other commands, from other repositories
fix-issues
Diagnose, reproduce, then fix reproducible open GitHub issues in parallel: one clean worktree/issue; symlink build artifacts to avoid rebuilds.
review-prs
Parallel PR triage: decide merge-worthiness, prepare rebased worktrees, fix blockers, return them for human merge.
triage
Classify/label newly opened GitHub issues missing labels.
release
Release all packages at specified version.
cleanup
Autonomous cleanup-loop iteration: discover ONE target → complete execution → verify → report. Runs stateless: derive from current tree; assume prior runs left it consistent.
hello
Say hello.