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 MattArtzAnthro/gephi-ai --skill community-detectiongit clone --depth 1 https://github.com/MattArtzAnthro/gephi-aiWrote 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/mattartzanthro/gephi-ai/community-detection)<a href="https://agentmods.dev/skills/mattartzanthro/gephi-ai/community-detection"><img src="https://agentmods.dev/badge/skills/mattartzanthro/gephi-ai/community-detection/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/mattartzanthro/gephi-ai/community-detection"><img src="https://agentmods.dev/badge/skills/mattartzanthro/gephi-ai/community-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.01556 |
| Opus 5.5 | $0.00017 | $0.00622 |
| Sonnet 5.5 | $0.00009 | $0.00311 |
| Haiku 4.5 | $0.00004 | $0.00156 |
Grade A, and why
community-detection 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 28d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Community Detection Workflow
Run a complete community detection and visualization workflow on the current Gephi graph.
Follow the gephi skill's rules throughout; this workflow repeats only the ones
it is most likely to break.
Read ../gephi/references/statistics-guide.md before
interpreting modularity, and ../gephi/references/layout-guide.md
before the layout step.
Tell the user what you are doing at each step. Narrate briefly before each tool call.
Rules this workflow must keep
- Asking. When a step says to ask, ask once. If the person cannot answer, is away, or asked for a finished product, use the default named in that step and remove or overwrite nothing. At the end, give one short line for each choice that changed the result, under "Choices I made"; leave the section out when every step used its obvious default. If there is no input to work on, stop and say what is needed.
- Session start. Start with
gephi_health_check. Then check which workspace is open (gephi_list_workspaces) and whether a filter is active (filter_activein replies): a filter from an earlier conversation stays on, and exports and checks then see only what it shows. - Stability. Run
gephi_community_stabilitybefore naming, captioning, or colouring by groups, and say how stable they are. - Palette. On a light background, leave
colorsunset: the plugin gives the largest group the first of eight colours validated for readability, the next largest the second, and so on, in an order that keeps the five largest groups distinguishable wherever they touch, even for colour-blind readers; past five groups, label the groups as well. On a dark background, pass the dark-surface palette. Colour must never be the only way to tell groups apart: label the largest nodes or the groups.
Steps
- Session start: Call
gephi_health_check. If it fails, tell the user to start Gephi and stop. Callgephi_list_workspacesandgephi_get_project_info, and tell the user the node and edge counts. Iffilter_activeis true, say that communities will be found in the visible nodes only.
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.
- 28d ago Changed · +3 lines 651030601781
- 1mo ago First seen · 77 lines · 43 tokens per session scan A 2231ef8989ea
community-detection is a skill published in the GitHub repository MattArtzAnthro/gephi-ai (25 stars, last pushed 12d ago), licensed Apache-2.0. It adds 43 tokens to every session and 1,556 once invoked, about $0.0002 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 2026-08-30.
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