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 CamusGIT/EvoQuant --skill paper-graphgit clone --depth 1 https://github.com/CamusGIT/EvoQuantWrote 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/camusgit/evoquant/paper-graph)<a href="https://agentmods.dev/skills/camusgit/evoquant/paper-graph"><img src="https://agentmods.dev/badge/skills/camusgit/evoquant/paper-graph/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/camusgit/evoquant/paper-graph"><img src="https://agentmods.dev/badge/skills/camusgit/evoquant/paper-graph.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 | $0.00197 | $0.06049 |
| Opus 5.5 | $0.00079 | $0.02420 |
| Sonnet 5.5 | $0.00039 | $0.01210 |
| Haiku 4.5 | $0.00020 | $0.00605 |
Grade A, and why
paper-graph 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 1mo 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 — 398 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Graph
Build a Markdown report with embedded Mermaid diagrams showing how research on a user-specified topic (or paper) evolved — clustered into challenges → solutions and traced as per-solution evolution paths.
The skill has no outbound LLM dependency. The host agent provides all LLM calls; the skill provides deterministic data fetchers (S2 / DeepXiv), prompt templates, markdown parsers, and Mermaid renderers. Run the runbook below step-by-step.
When to Use This Skill
Trigger when the user asks something like:
- "Show me the history of " / "How did evolve?"
- "Where does stem from?" / "What did build on?"
- "What are significant improvements / follow-ups to ?"
- "Trace the lineage of ideas in " / "Give me a literature taxonomy of "
- "Citation tree of " / "Idea trace of "
Skip when:
- The user just wants a one-paper summary or single search hit (no relational/evolutionary aspect).
- The request is for non-academic citation work.
- The user explicitly wants a plain bibliography rather than a graph.
Inputs and Output
Inputs:
- A research query: a topic, a seed paper title/citation, or a hybrid. Free-form text.
- Output path (required): path for the final Markdown report. If not given, ask before running.
- (Optional) number of papers to fetch (
--nflag onfetch_papers). Default 10. - (Optional) Mermaid theme
lightordark(--themeon render steps, orMERMAID_THEMEenv). Default light.
Output: a single Markdown file at the user-specified path with these sections:
- Research goal (extracted from the query)
- High-level taxonomy — one Mermaid graph: root → challenges → solutions → paper references
- Per-solution evolution paths — one Mermaid graph per solution, showing paper-to-paper "evolution from" edges, evolution points, open challenges
- Paper appendix — the numbered list of papers with title / year / authors / abstract / conclusion excerpt
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/audit_edge.md 1.6 KB
- references/classify.md 1.7 KB
- references/detail.md 4.4 KB
- references/outline.md 2.0 KB
- references/parse_query.md 1.9 KB
- references/seed_paper_block.md 87 B
- scripts/cli.py 48 KB runs code
- scripts/config.py 2.6 KB runs code
- scripts/deepxiv_client.py 4.9 KB runs code
- scripts/logger.py 5.4 KB runs code
- scripts/mermaid.py 29 KB runs code
- scripts/paper_md.py 6.8 KB runs code
- scripts/pipeline.py 23 KB runs code
- scripts/prompts.py 950 B runs code
- scripts/web_api.py 9.0 KB runs code
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.
- 1mo ago First seen · 398 lines · 197 tokens per session scan A b41f3f0c737c
paper-graph is a skill published in the GitHub repository CamusGIT/EvoQuant (141 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 197 tokens to every session and 6,049 once invoked, about $0.0008 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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