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 | $0.02446 |
| Opus 5 | $0.00076 | $0.01223 |
| Sonnet 5 | $0.00030 | $0.00489 |
| Haiku 4.5 | $0.00015 | $0.00245 |
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 9d 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 — 138 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.
| 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 |
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 9.8 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 7.9 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.
- 9d 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 3d ago), licensed MIT. It adds 151 tokens to every session and 2,446 once invoked, about $0.0008 per session on Opus 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.
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