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 outline-agentgit 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/outline-agent)<a href="https://agentmods.dev/skills/raja21068/autoresearch/outline-agent"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/outline-agent/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/outline-agent"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/outline-agent.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 | $0.00099 | $0.01364 |
| Opus 5 | $0.00049 | $0.00682 |
| Sonnet 5 | $0.00020 | $0.00273 |
| Haiku 4.5 | $0.00010 | $0.00136 |
Grade A, and why
outline-agent 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 6d 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
100% identical to outline-agent — 0 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outline Agent (Step 1)
Faithful implementation of the Outline Agent from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, App. F.1, pp. 40–44).
Cost: 1 LLM call.
Your task
Read four input files from the workspace and produce a single JSON object at
workspace/outline.json with three top-level keys:
plotting_plan— array of figure objectsintro_related_work_plan— object withintroduction_strategyandrelated_work_strategysection_plan— array of section objects, each withsection_titleandsubsections[]
How to do it
- Read the verbatim prompt at
references/prompt.md. This is the exact Outline Agent system prompt from the paper. Use it as your system message. - Prepend the Anti-Leakage Prompt from
../paper-orchestra/references/anti-leakage-prompt.md. - Read the four input files:
workspace/inputs/idea.mdworkspace/inputs/experimental_log.mdworkspace/inputs/template.texworkspace/inputs/conference_guidelines.md
- Synthesize across all four — the global instruction in the prompt is "Do not analyze inputs in isolation. You must synthesize information across all provided documents for every step."
- Emit a single JSON object following the schema in
references/outline-schema.md. Cross-check againstreferences/outline_schema.json(machine-readable). - Save to
workspace/outline.json. - Validate:
If validation fails, fix the JSON and re-validate. Do not proceed to Step 2 or Step 3 with an invalid outline — every downstream agent depends on this schema.python skills/outline-agent/scripts/validate_outline.py workspace/outline.json
Hard rules from the prompt (do not violate)
These are excerpted from references/prompt.md. The validator enforces them.
Plotting plan (Directive 1)
plot_typeMUST be exactly one of"plot"or"diagram".data_sourceMUST be exactly one of"idea.md","experimental_log.md", or"both".aspect_ratioMUST be exactly one of:"1:1","1:4","2:3","3:2","3:4","4:1","4:3","4:5","5:4","9:16","16:9","21:9".figure_idMUST be a semantically meaningful snake_case identifier (e.g.,fig_framework_overview,fig_ablation_study_parameter_sensitivity).figure_idMUST NOT contain the word"Figure".
What ships with it
6 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.
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.
- 6d ago First seen · 114 lines · 99 tokens per session scan A 685512f9d7d6
outline-agent is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 99 tokens to every session and 1,364 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to outline-agent, differing in 0 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…
literature-review-agent
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to…
agent-research-aggregator
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimentallog.md). TRIGGER when the user says…
paper-orchestra
Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper…
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…
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…