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/mattartzanthro/gephi-ai/text-networkgit 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/commands/mattartzanthro/gephi-ai/text-network)<a href="https://agentmods.dev/commands/mattartzanthro/gephi-ai/text-network"><img src="https://agentmods.dev/badge/commands/mattartzanthro/gephi-ai/text-network.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 | $0.00023 | $0.00319 |
| Opus 5 | $0.00012 | $0.00160 |
| Sonnet 5 | $0.00005 | $0.00064 |
| Haiku 4.5 | $0.00002 | $0.00032 |
Grade A, and why
text-network 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 4d 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.
What it actually says
Build a text network
Dispatch the text-network-builder agent to turn the text in $ARGUMENTS into a
word co-occurrence network in Gephi — recurring concepts as nodes, co-occurrence as
edges, themes as communities.
- If
$ARGUMENTSis a file path (or several), pass it to the agent; if it is a folder or a naturally segmented corpus, tell the agent so it builds from a list (one transcript turn / note / answer per item) rather than one blob — the co-occurrence window should reset per segment. - If
$ARGUMENTSis empty, ask the user for the text or a path, then dispatch.
The agent builds, inspects the vocabulary, rebuilds with better stopwords/POS/frequency
settings if the hubs are noise, then colors, sizes, and lays out the graph. It runs in
its own context so the tuning iterations stay out of this conversation. When it returns,
show the export/caption and relay its notes on what the construction choices did (a
co-occurrence edge is proximity in text, not a claim of meaning). Reading the map is a
separate step — offer /analyze-network or the network-analyst agent for that.
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.
- 4d ago First seen · 25 lines · 23 tokens per session scan A 4aca550c04d2
text-network is a command published in the GitHub repository MattArtzAnthro/gephi-ai (21 stars, last pushed 3d ago), licensed Apache-2.0. It adds 23 tokens to every session and 319 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 2026-08-30.
Other commands, from other repositories
master_analysis
Run comprehensive 5-phase analysis across labs, genetics, and protocols.
diff
Quantitative volume comparison between a CadQuery model and a reference STEP file.
arg-diagram
ARG academic-paper diagram mode — standalone structural & conceptual diagram generation.
simulation-calibrator
Test and refine simulation accuracy with validation loops, bias detection, and continuous improvement frameworks.
graphite-morphology-classify
Classify graphite in a cast-iron micrograph per ASTM A247 / ISO 945-1, quantify nodularity, and read the matrix — the single most diagnostic observation in a cast-iron case.
plot
Publication Polish. Runs after results-analysis (Phase 7). Audits all tables and figures produced in Phases 4–7, upgrades them to top-journal standards by calling the table and figure skills.