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 Lzy599775/agent-auto-sci-skills --skill auto-sci-researchgit clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-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/lzy599775/agent-auto-sci-skills/auto-sci-research)<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/auto-sci-research"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/auto-sci-research/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/lzy599775/agent-auto-sci-skills/auto-sci-research"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/auto-sci-research.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 | $0.00151 | $0.03624 |
| Opus 5.5 | $0.00060 | $0.01450 |
| Sonnet 5.5 | $0.00030 | $0.00725 |
| Haiku 4.5 | $0.00015 | $0.00362 |
Grade A, and why
auto-sci-research 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 10d 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto-sci-research
This is the umbrella skill for the user's local scientific-agent system. It replaces the earlier public label agent-auto-sci while keeping that phrase as a legacy trigger. It does not replace domain skills. It routes work to the right subskill, preserves reusable knowledge, and requires every major improvement to leave a visible evolution record.
Core idea:
sources -> structured memory -> idea and method design -> data/geo/ML analysis -> manuscript or review -> critique/rebuttal -> evolution archive
For full paper projects, use the expanded route:
intake -> topic -> SMART question -> literature search -> literature matrix -> data plan -> processing/code -> analysis -> figures/tables -> draft -> internal review -> formatting/submission -> rebuttal -> evolution record
Routing
Use this skill first when the task spans multiple research stages or asks to improve the user's local skills.
Deliverable-first precedence
Route by the current deliverable, not by topic keywords alone. Green exposure, heat exposure, urban health, spatial equity, accessibility, and SHAP can occur in either a literature review or an empirical manuscript, so they do not select the writing owner by themselves.
- External literature synthesis, review corpus construction, paper-level extraction, Evidence Trace, cross-paper synthesis, conflicting evidence, heterogeneity, literature gaps, review coding, or review drafting routes to
sport-geography-review-bibliometric. If the review type or domain route is still undecided, placeurban-exposure-review-radar-workflowbefore it. - The user's own data/method/results manuscript drafting or revision routes through
agent-auto-sci-scicommas the argument owner, withsport-geography-sci-writingadded as the domain writing helper when applicable. Add geospatial, ML, data-viz, or methodology helpers only when the immediate deliverable requires them. - For mixed tasks, choose the owner for the current handoff. A frozen empirical Results/Discussion remains scientifically bounded by
sport-geography-sci-writingwhen applicable, whileagent-auto-sci-scicommcoordinates the argument handoff; review skills may be helpers and must not take over. Do not load both domain owners merely because the topic contains literature and empirical terms; make a later handoff explicit when the deliverable changes. - If the immediate deliverable is only review evidence structure and explicitly says not to write prose, do not add
sport-geography-sci-writing,scipilot-writing-skill, oragent-auto-sci-scicommas primary owners.
What ships with it
13 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.
- agents/openai.yaml 350 B
- references/00_system_architecture.md 3.7 KB
- references/01_source_learning_notes.md 4.0 KB
- references/02_subskill_registry.md 11 KB
- references/03_api_config_and_security.md 2.1 KB
- references/04_evolution_archive_protocol.md 1.6 KB
- references/05_project_routes.md 2.7 KB
- references/06_external_skill_coverage_audit.md 5.2 KB
- references/07_external_skills_deployment_20260601.md 4.3 KB
- references/08_full_research_to_manuscript_pipeline.md 10 KB
- references/09_subagent_composition_matrix.md 4.7 KB
- references/10_prompt_workflow_from_academic_pdf.md 7.1 KB
- references/11_sportpark_skill_integration_notes.md 3.9 KB
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.
- 10d ago Changed · +30 lines 15e62f4ca85a
- 27d ago Changed · +21 lines a83820089aaf
- 1mo ago First seen · 138 lines · 151 tokens per session scan A 717df9140184
auto-sci-research is a skill published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 9d ago), licensed MIT. It adds 151 tokens to every session and 3,624 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
papers-reading-skill
Evidence-grounded AI research workflow for turning supplied economics, finance, management, and social-science papers or structured records into versioned PaperReading artifacts. Use when Codex must ingest text, Markdown, or a text-based PDF; separate source-grounded claims from researcher analysis; bind findings to…
paper-research-mentor
A research-paper reading guide for understanding one or more academic papers in depth. It provides explanations linked to the source, discusses equations and experiments, and connects methods to new projects.
scopus-researcher
Expert academic researcher using the Scopus MCP. Finds papers, retrieves full abstracts, builds author profiles, analyzes citation impact, and constructs advanced Boolean queries across the Elsevier Scopus database. Activate when asked to search for academic papers, analyze research trends, find citations, profile…
write-literature-review
Iterative literature-review workflow for a research topic. Use this skill to draft up to 10 search keywords, build a seed set from OpenAlex title-and-abstract matches, expand by backward and forward citations, screen candidates by title and abstract, repeat until no new relevant papers remain, rank the final set…
nature-paper-trans
A workflow for translating selected pages or a complete English research-paper PDF into a simplified-Chinese, image-based PDF. bioRxiv is not mentioned in the provided details; the workflow applies to research-paper PDFs generally.
nature-experiment-log
A workflow for turning experiment notes, images, audio, or text into structured Markdown laboratory logs with YAML metadata. It can also organize raw attachments and optionally connect the logs to Feishu or Obsidian.