Image generation skill using Gemini Web. Generates images from text prompts via Google Gemini. Also supports text generation. Use as the image generation backend for other skills like cover-image, xhs-images, article-illustrator.
中文简介正在准备中。当前显示原文说明。
Generate format-controlled research reports with evidence tracking, citations, and iterative review. This skill should be used when users request a research report, literature review, market or industry analysis, competitive landscape, policy or technical brief, or require a strict report template and section formatting that a single deepresearch pass cannot reliably enforce.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Create high-fidelity research reports with strict format control, evidence mapping, and multi-pass synthesis.
Copy this checklist and track progress:
Deep Research Progress:
- [ ] Step 1: Intake and format contract
- [ ] Step 2: Research plan and query set
- [ ] Step 3: Evidence collection (deepresearch tool)
- [ ] Step 4: Source triage and evidence table
- [ ] Step 5: Outline and section map
- [ ] Step 6: Multi-pass full drafting (parallel subagents)
- [ ] Step 7: UNION merge and format compliance
- [ ] Step 8: Evidence and citation verification
- [ ] Step 9: Present draft for human review and iterate
Establish the report requirements before any research:
Create a concise report spec file:
Report Spec:
- Audience:
- Purpose:
- Scope:
- Time Range:
- Geography:
- Required Sections:
- Section Formatting Rules:
- Citation Style:
- Output Format:
- Length Targets:
- Tone:
- Must-Include Sources:
- Must-Exclude Topics:
If a user provides a template or an example report, treat it as a hard constraint and mirror the structure.
Define the research strategy before calling tools:
Use references/research_plan_checklist.md for guidance.
Use the deepresearch tool to collect evidence and citations.
File structure (recommended):
<output_dir>/research/<topic-name>/
deepresearch_pass1.md
deepresearch_pass2.md
deepresearch_pass3.md
If deepresearch is unavailable, rely on user-provided sources only and state limitations explicitly.
Normalize and score sources before drafting:
Evidence table minimum columns:
Create an outline that enforces the format contract:
Avoid single-pass drafting; generate multiple complete reports, then merge.
Use the Task tool to spawn parallel subagents with isolated context. Each subagent must:
Implementation pattern:
Task(subagent_type="general-purpose", prompt="Draft complete report ...", run_in_background=false) -> version1.md
Task(subagent_type="general-purpose", prompt="Draft complete report ...", run_in_background=false) -> version2.md
Task(subagent_type="general-purpose", prompt="Draft complete report ...", run_in_background=false) -> version3.md
Write drafts to files, not conversation context:
<output_dir>/intermediate/<topic-name>/version1.md
<output_dir>/intermediate/<topic-name>/version2.md
<output_dir>/intermediate/<topic-name>/version3.md
Merge using UNION, never remove content without evidence-based justification:
Verify traceability:
Use references/completeness_review_checklist.md.
Present the draft as a reviewable version:
| File | When to Load |
|---|---|
| research_report_template.md | Build outline and draft structure |
| formatting_rules.md | Enforce section formatting and citation rules |
| source_quality_rubric.md | Score and triage sources |
| research_plan_checklist.md | Build research plan and query set |
| completeness_review_checklist.md | Review for coverage, citations, and compliance |
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Image generation skill using Gemini Web. Generates images from text prompts via Google Gemini. Also supports text generation. Use as the image generation backend for other skills like cover-image, xhs-images, article-illustrator.
中文简介正在准备中。当前显示原文说明。
Transform academic papers into in-depth technical articles with multiple writing style options. Use the MinerU Cloud API for high-precision PDF parsing, automatically extracting images, tables, and formulas. Optional formula explanations and GitHub code analysis, generating Markdown and HTML formats.
中文简介正在准备中。当前显示原文说明。
Generate educational comics from academic papers, using visual storytelling to explain core ideas and innovations. Supports 4 art styles: classic (clean lines), tech (futuristic), warm (friendly), chalk (blackboard). Uses genimg-gemini-web to generate images.
中文简介正在准备中。当前显示原文说明。
Analyzes research papers (PDF/arXiv URL) and converts them into executable code. Automatically activated upon requests for paper replication, algorithm implementation, or research reproduction. Responds to requests like "Implement this paper", "paper2code", "Convert paper to code".
中文简介正在准备中。当前显示原文说明。
Transform research papers into professional visual schemas. Analyzes paper logic, selects optimal layout patterns, and generates detailed prompts for AI image generation.
中文简介正在准备中。当前显示原文说明。