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 xjtulyc/awesome-rosetta-skills --skill historical-networkgit clone --depth 1 https://github.com/xjtulyc/awesome-rosetta-skillsWrote 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/xjtulyc/awesome-rosetta-skills/historical-network)<a href="https://agentmods.dev/skills/xjtulyc/awesome-rosetta-skills/historical-network"><img src="https://agentmods.dev/badge/skills/xjtulyc/awesome-rosetta-skills/historical-network/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/xjtulyc/awesome-rosetta-skills/historical-network"><img src="https://agentmods.dev/badge/skills/xjtulyc/awesome-rosetta-skills/historical-network.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 | $0.00038 | $0.05096 |
| Opus 5 | $0.00019 | $0.02548 |
| Sonnet 5 | $0.00008 | $0.01019 |
| Haiku 4.5 | $0.00004 | $0.00510 |
Grade A, and why
historical-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 9d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 9d ago First seen · 646 lines · 38 tokens per session scan A f4225fd9d088
historical-network is a skill published in the GitHub repository xjtulyc/awesome-rosetta-skills (34 stars, last pushed 5mo ago), with no licence file. It adds 38 tokens to every session and 5,096 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 2026-09-03.
Other skills, from other repositories
community-detection
Detect, validate, visualize, and explain communities in the loaded Gephi graph using Louvain or an installed Leiden statistic. Use for community detection, modularity, cluster coloring, or resolution comparisons.
infomap
Use when helping someone USE Infomap as an analysis tool — running the map equation for community detection via the Infomap CLI, Python package, R package, or notebooks; choosing a network representation or flow model (multilayer, memory/state, metadata, bipartite); reproducible analysis; result interpretation; or…
teach-with-gephi
Run narrated, watch-along network analysis in Gephi for teaching, demos, and paired exploration. Use when a person is watching Gephi Desktop and wants every visible change explained and connected to a network-science concept.
build-text-network
Turn transcripts, field notes, survey answers, documents, or social posts into a tuned word co-occurrence network in Gephi. Use for build a text network, concept map this corpus, visualize themes in text, or inspect discourse as a graph.
import-and-explore
Import a GEXF, GraphML, GML, CSV, DOT, or Pajek file into a fresh Gephi project, profile it, style it, lay it out, and present an initial evidence-aware exploration.
verify-claim
Independently verify one plain-language structural claim about the loaded Gephi graph and report confirmed, refuted, or can't-tell with measurements and live-graph receipts. Use for claims about centrality, connectivity, comparison, grouping, or robustness.