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 raja21068/AutoResearch --skill writing-systems-papersgit clone --depth 1 https://github.com/raja21068/AutoResearchWrote 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/raja21068/autoresearch/writing-systems-papers)<a href="https://agentmods.dev/skills/raja21068/autoresearch/writing-systems-papers"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/writing-systems-papers/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/raja21068/autoresearch/writing-systems-papers"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/writing-systems-papers.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 | $0.00084 | $0.01761 |
| Opus 5 | $0.00042 | $0.00881 |
| Sonnet 5 | $0.00017 | $0.00352 |
| Haiku 4.5 | $0.00008 | $0.00176 |
Grade A, and why
writing-systems-papers 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 8d 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.
This is a copy
98% identical to writing-systems-papers — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Systems Papers: Paragraph-Level Blueprint
Structural guidance for $ARGUMENTS
Relationship to Other ARIS Skills
- paper-write: General paper generation workflow with citation verification. This skill complements it with systems-specific structural blueprints.
- paper-slides: Conference presentation generation (Beamer+PPTX). Already covers talks — no overlap.
- paper-plan: Research outline creation. Use paper-plan first, then this skill for paragraph-level structure.
Boundary: paper-write handles the generation workflow (LaTeX output, DBLP verification, section-by-section drafting). This skill provides the structural skeleton — page budgets, paragraph roles, and writing patterns specific to systems venues.
Page Allocation: 12-Page Systems Paper
| Section | Pages | Key Content |
|---|---|---|
| Abstract | ~0.25 | 150–250 words, 5 sentences |
| S1 Introduction | 1.5–2 | Problem → Gap → Insight → Contributions |
| S2 Background & Motivation | 1–1.5 | Terms + Production observations |
| S3 Design | 3–4 | Architecture + Modules + Alternatives |
| S4 Implementation | 0.5–1 | Prototype, LOC, engineering |
| S5 Evaluation | 3–4 | Setup + E2E + Ablation + Scalability |
| S6 Related Work | 1 | By methodology, explicit comparison |
| S7 Conclusion | 0.5 | 3-sentence summary |
Section Blueprints
Abstract (5 sentences)
S1: Problem context and importance
S2: Gap in existing approaches
S3: Thesis — "X is better for Y in environment Z" (Irene Zhang formula)
S4: Approach summary + headline results
S5: Impact or availability
Sources: Levin & Redell — "Can you state the new idea concisely?"; Irene Zhang — "abstract cannot use terms introduced in the paper."
S1 Introduction (1.5–2 pages)
- Problem (~0.5p) — Domain + concrete numbers + why it matters
- Gap analysis (~0.5p) — G1–Gn: specific shortcomings with evidence
- Key insight (1 para) — Thesis: "X is better for Y in Z"
- Contributions (~0.5p) — 3–5 numbered, testable claims with §N references
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.
- 8d ago First seen · 185 lines · 84 tokens per session scan A 8e9d53e493ee
writing-systems-papers is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 84 tokens to every session and 1,761 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to writing-systems-papers, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…
paper-autoraters
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…
paper-writing-bench
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a…