Auto Sci Research

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.

| Task | Use | |---|---| | Full workflow from topic selection to final manuscript | Start here, then read references/08_full_research_to_manuscript_pipeline.md | | Large task that should be split into role-specific agents | Read references/09_subagent_composition_matrix.md | | Reusing academic prompt libraries without copying copyrighted text | Read references/10_prompt_workflow_from_academic_pdf.md | | Integrating Sportpark writing/figure memories or deduplicating local skills | Read references/11_sportpark_skill_integration_notes.md | | Build or update the overall research-agent workflow | agent-auto-sci-automation | | Machine learning, AI model design, SHAP, model evaluation | agent-auto-sci-ai-ml | | Upstream K-Dense ML/AI technical playbooks: scikit-learn, PyTorch Lightning, Transformers, SHAP, time-series ML, GNN, UMAP | kdense-ml-ai-selected | | EDA, statistics, publication figures, tables, dashboards | agent-auto-sci-data-viz | | Upstream K-Dense EDA/statistics/visualization playbooks: matplotlib, seaborn, scientific visualization, NetworkX, Polars, Dask | kdense-data-viz-selected | | Data figure advisor, chart selection, journal-ready plotting, CJK font setup, visual QA | scipilot-figure-skill | | GIS, spatial accessibility, remote sensing, spatial ML | agent-auto-sci-geospatial | | Upstream K-Dense geospatial and remote-sensing playbooks: geomaster, GeoPandas | kdense-geospatial-rs-selected | | Manuscript argument, slides, posters, peer review, journal strategy, rebuttal | agent-auto-sci-scicomm | | Academic polishing, translation, de-AI/humanizing, cover letter, response-to-reviewers, writing lint | scipilot-writing-skill | | Geography and remote-sensing SCI writing: inversion, spatiotemporal change, exposure/accessibility IMRAD sections | geors-sci-writing-adapter | | Upstream K-Dense scientific communication playbooks: writing, peer review, citations, literature review, slides, posters, schematics | kdense-scicomm-selected | | Hypothesis, research design, critical appraisal, evidence grading | agent-auto-sci-methodology | | Urban exposure review workflow, systematic/scoping review, bibliometric + critical review, remote-sensing frontier radar, CV-to-RS idea generation, public health database linkage | urban-exposure-review-radar-workflow | | Sport geography empirical SCI manuscript writing | sport-geography-sci-writing | | Sport geography review, systematic review, bibliometrics | sport-geography-review-bibliometric | | Web-based autonomous deep research | gpt-researcher | | Full academic research suite and ARS workflows | academic-research-suite | | Long-running improve/verify loops | codex-autoresearch | | Local PDF library reading and HTML notes | codex-paper-reader | | Persona or thinking-framework skill distillation | huashu-nuwa | | Academic-writing prompt reuse | awesome-ai-research-writing | | External academic skill selection | codex-academic-skills-index |

Read references/02_subskill_registry.md when choosing among skills.

Operating Contract

1. Keep the user's research project persistent. Do not treat a task as a one-off answer if it creates reusable knowledge.
2. Convert useful external repositories into local workflows, not blind copies.
3. Separate memory, workflow, scripts, data, manuscripts, figures, and review feedback.
4. Record failed attempts, rejected ideas, weak evidence, and reviewer objections as first-class knowledge.
5. Never store secrets in skill files, HTML, markdown logs, or committed examples.
6. Every major skill change must update the evolution archive HTML.

Workflow

1. Scope the task

• Identify whether this is literature, idea, method, data, spatial, ML, writing, review, or skill-evolution work.
• Decide which subskill owns the task and which existing sport-geography skill is relevant.
• Read references/00_system_architecture.md.

1. Load project memory

• Check local project notes, previous skill references, and the evolution archive.
• For sport geography, reuse the established narrative: public problem -> spatial inequity -> measurement gap -> evidence -> mechanism -> planning implication.

1. Run the smallest reliable workflow

• Prefer reproducible scripts and structured tables when the task repeats.
• Prefer clear markdown references when the task is judgment-heavy.
• Use external APIs only when their value justifies setup and security cost.
• For full manuscript projects, progress phase by phase and keep handoffs explicit.
• For large tasks, compose subagents instead of creating new standalone skills by default.

1. Gate quality

• Verify files, scripts, outputs, citations, figures, and assumptions.
• For manuscripts, run claim-evidence checks and reviewer-risk scans.
• For data or models, run EDA, leakage checks, baselines, sensitivity, and reproducibility checks.

1. Update evolution records

• Update /agent_auto_sci/evolution_archive/evolution_data.json.
• Run /agent_auto_sci/evolution_archive/update_evolution_html.py.
• Mention the generated HTML path in the final response.
• Read references/04_evolution_archive_protocol.md.

Reference Map

• references/00_system_architecture.md: total architecture and how AutoSci/OmegaWiki is adapted.
• references/01_source_learning_notes.md: what was learned from AutoSci and K-Dense Scientific Agent Skills.
• references/02_subskill_registry.md: current local subskill inventory and routing.
• references/03_api_config_and_security.md: API configuration, provider roles, and secret-handling rules.
• references/04_evolution_archive_protocol.md: how to maintain the HTML evolution archive.
• references/05_project_routes.md: current sport-geography review and empirical-manuscript routes.
• references/06_external_skill_coverage_audit.md: coverage matrix for AutoSci and K-Dense skills, including what is preserved, adapted, and intentionally excluded.
• references/07_external_skills_deployment_20260601.md: 2026-06-01 deployment record for gpt-researcher, nuwa-skill, ARS, autoresearch, paper reader, Codex Academic Skills, and AI writing prompts.
• references/08_full_research_to_manuscript_pipeline.md: end-to-end research pipeline from topic selection, literature, data/code, analysis, figures, drafting, review, formatting, and rebuttal.
• references/09_subagent_composition_matrix.md: role-specific subagent combinations and handoff schema.
• references/10_prompt_workflow_from_academic_pdf.md: private PDF-derived prompt workflow structure, adapted without copying long text.
• references/11_sportpark_skill_integration_notes.md: Sportpark writing/figure lessons, duplicate handling, and Darwin-style evaluation boundary.
• ../urban-exposure-review-radar-workflow/references/workflow_playbook.md: route-specific workflow for reviews, bibliometrics, urban exposure, remote sensing radar, and health database linkage.
• ../kdense-ml-ai-selected/SKILL.md: selected K-Dense ML/AI package and upstream subskills.
• ../kdense-data-viz-selected/SKILL.md: selected K-Dense data analysis and visualization package.
• ../kdense-geospatial-rs-selected/SKILL.md: selected K-Dense geospatial and remote-sensing package.
• ../kdense-scicomm-selected/SKILL.md: selected K-Dense scientific communication package.
• ../scipilot-figure-skill/SKILL.md: SciPilot scientific figure advisor and visual QA workflow.
• ../scipilot-writing-skill/SKILL.md: SciPilot writing, polishing, de-AI/humanizing, cover letter, and rebuttal workflow.
• ../geors-sci-writing-adapter/SKILL.md: original geography/remote-sensing SCI writing adapter for inversion, spatiotemporal change, exposure, accessibility, and urban-health manuscripts.

Local Project Files

• /agent_auto_sci/00_AGENT_AUTO_SCI_总览与变更记录.md
• /agent_auto_sci/evolution_archive/agent-auto-sci-evolution.html
• /agent_auto_sci/evolution_archive/evolution_data.json
• /external_repos/latest/AutoSci
• /external_repos/latest/scientific-agent-skills
• /external_repos/latest/gpt-researcher
• /external_repos/latest/nuwa-skill
• /external_repos/latest/academic-research-skills-codex
• /external_repos/latest/codex-autoresearch
• /external_repos/latest/Codex-Academic-Skills
• /external_repos/latest/codex-paper-reader
• /external_repos/latest/awesome-ai-research-writing-STRYXTN

Must Not Do

• Do not install all third-party skills blindly.
• Do not copy external workflows that require unavailable infrastructure without marking them as optional.
• Do not expose API keys, tokens, private PDFs, unpublished data, or reviewer-sensitive material in HTML.
• Do not copy long text from private prompt PDFs into public skill files.
• Do not create a new subskill when an existing subskill can be extended cleanly.
• Do not skip the HTML evolution archive after a meaningful skill change.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Lzy599775
• Source: Lzy599775/agent-auto-sci-skills
• License: MIT
• Homepage: https://lzy599775.github.io/agent-auto-sci-skills/

Install and usage instructions live in the source repository linked above.