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/emergent-wisdom/understanding-graph/graph-workflownpx skills add emergent-wisdom/understanding-graph --skill graph-workflowgit clone --depth 1 https://github.com/emergent-wisdom/understanding-graphWrote 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/emergent-wisdom/understanding-graph/graph-workflow)<a href="https://agentmods.dev/skills/emergent-wisdom/understanding-graph/graph-workflow"><img src="https://agentmods.dev/badge/skills/emergent-wisdom/understanding-graph/graph-workflow.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 | $0.00044 | $0.01380 |
| Opus 5 | $0.00022 | $0.00690 |
| Sonnet 5 | $0.00009 | $0.00276 |
| Haiku 4.5 | $0.00004 | $0.00138 |
Grade A, and why
graph-workflow 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.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graph Mutation Reference
The live MCP instructions and understanding-work define the fluid medium.
This skill only explains how to preserve a chosen update accurately. It does
not require orienting, searching, re-entering, or touching every tool in a
fixed order.
At a genuine choice point, graph_suggest_next({ task, workflow }) can propose
concrete weighted routes. The model may choose, combine, change, reject, or
replace them. When a selected route calls graph_understand, keep its proposed
stance; workflow and stance are independent.
Mutate atomically
Substantive graph changes go through graph_batch. A batch is one committed
encounter: either every operation succeeds or none does. It accepts at most
100 operations; split independent work into separate commits.
graph_batch({
commit_message: "Connected the new failure model to the observed retry burst",
agent_name: "current-agent",
operations: [
{ tool: "graph_add_concept", params: {
title: "Fixed retries synchronize failure",
trigger: "surprise",
understanding: "A recovery rule that protects one client can align the population and intensify overload.",
why: "Changes the failure model from independent retries to common-mode behavior"
}},
{ tool: "graph_connect", params: {
from: "$0.id",
to: "n_retry_observation",
type: "learned_from",
why: "The burst trace is the evidence that occasioned this update"
}}
]
})
$N.id references the result of operation N, counting every operation from
zero. Use the exact field names in the live schema. Common ones are:
graph_add_concept { title, trigger, understanding, why }
graph_note { about, testimony, title?, trigger?, why?, status?, relations? }
graph_connect { from, to, type, why }
graph_revise { node, understanding, before, after, pivot, why }
doc_create { title, content?, fileType?, isDocRoot?, parentId?, level?, afterId? }
doc_revise { nodeId, content, why }
doc_move { nodeId, parentId?, afterId? }
doc_split { nodeId, mode, lineNumbers?, childLevel? }
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 · 145 lines · 44 tokens per session scan A 7351ddf14386
graph-workflow is a skill published in the GitHub repository emergent-wisdom/understanding-graph (1 stars, last pushed 9d ago), licensed MIT. It adds 44 tokens to every session and 1,380 once invoked, about $0.0002 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.
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