[upstream-sync] skills/treatment-plans/SKILL.md from K-Dense-AI/scientific-agent-skills@831d49eb [prompt]

upstream/K-Dense-AI-scientific-agent-skills/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Scientific Agent Skills"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/fc0b9f69/README.md
-upstream_sha: fc0b9f69
-imported_at: 2026-07-14
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/README.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -26,7 +26,13 @@ validated: false
 
 ## Star History
 
-[![Star History Chart](https://api.star-history.com/svg?repos=K-Dense-AI/scientific-agent-skills&type=date&legend=top-left)](https://www.star-history.com/#K-Dense-AI/scientific-agent-skills&type=date&legend=top-left)
+<a href="https://www.star-history.com/?repos=K-Dense-AI%2Fscientific-agent-skills&type=date&legend=top-left">
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/chart?repos=K-Dense-AI/scientific-agent-skills&type=date&theme=dark&legend=top-left&sealed_token=rL_5GLS9f4Fbyr1_VYZLGMF-8Rr6ZlWNaYNecajc52QSQq6KL7HrzSea_tGQGy1mBMXgVvAUMSIYAc0w39si9v5Up1RIw74-UDGZg_9HvH_chiyS0Njf-5tebtPh1LJjXTG6mH5Iv2pMJNivgfPsyB-oOgbaIV3uSc7DzSeZFCTE4WOcHX4y2BR76k5g" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/chart?repos=K-Dense-AI/scientific-agent-skills&type=date&legend=top-left&sealed_token=rL_5GLS9f4Fbyr1_VYZLGMF-8Rr6ZlWNaYNecajc52QSQq6KL7HrzSea_tGQGy1mBMXgVvAUMSIYAc0w39si9v5Up1RIw74-UDGZg_9HvH_chiyS0Njf-5tebtPh1LJjXTG6mH5Iv2pMJNivgfPsyB-oOgbaIV3uSc7DzSeZFCTE4WOcHX4y2BR76k5g" />
+   <img alt="Star History Chart" src="https://api.star-history.com/chart?repos=K-Dense-AI/scientific-agent-skills&type=date&legend=top-left&sealed_token=rL_5GLS9f4Fbyr1_VYZLGMF-8Rr6ZlWNaYNecajc52QSQq6KL7HrzSea_tGQGy1mBMXgVvAUMSIYAc0w39si9v5Up1RIw74-UDGZg_9HvH_chiyS0Njf-5tebtPh1LJjXTG6mH5Iv2pMJNivgfPsyB-oOgbaIV3uSc7DzSeZFCTE4WOcHX4y2BR76k5g" />
+ </picture>
+</a>
 
 > **🔔 Claude Scientific Skills is now Scientific Agent Skills.** Same skills, broader compatibility — now works with any AI agent that supports the open [Agent Skills](https://agentskills.io/) standard, not just Claude.
 
@@ -529,7 +535,7 @@ This repository contains **148 scientific and research skills** organized across
 - Diagrams: Scientific Schematics, Markdown & Mermaid Writing
 - Infographics: Infographics (10 types, 8 styles, colorblind-safe palettes)
 - Citations: Citation Management, pyzotero
-- Illustration: Generate Image (AI image generation with FLUX.2 Pro and Gemini 3 Pro (Nano Banana Pro))
+- Illustration: Generate Image (AI image generation with FLUX.2 Pro and Gemini 3.6 Flash (Nano Banana Pro))
 
 #### 🔬 **Scientific Databases & Data Access** (6 skills → 100+ databases total)
 > A unified database-lookup skill provides deterministic REST API access to 78 public databases across all domains, with retrieval contracts, pagination/count reconciliation, and endpoint provenance. Dedicated skills cover specialized data platforms. Multi-database packages like BioServices (~40 bioinformatics services), BioPython (38 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage.

upstream/K-Dense-AI-scientific-agent-skills/catalogue/docs/skills.md

@@ -2,9 +2,9 @@
 title: "Scientific Skills"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/fc0b9f69/docs/skills.md
-upstream_sha: fc0b9f69
-imported_at: 2026-07-14
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/docs/skills.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -175,12 +175,12 @@ validated: false
 - **BGPT Paper Search** - Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone
 - **pyzotero** - Python client for the Zotero Web API v3. Programmatically manage Zotero reference libraries: retrieve, create, update, and delete items, collections, tags, and attachments. Export citations as BibTeX, CSL-JSON, and formatted bibliography HTML. Supports user and group libraries, local mode for offline access, paginated retrieval with `everything()`, full-text content indexing, saved search management, and file upload/download. Optional CLI and built-in MCP server (pyzotero 1.12+) for searching local Zotero 7 libraries including full-text PDF search and Semantic Scholar integration. Use cases: building research automation pipelines that integrate with Zotero, bulk importing references, exporting bibliographies programmatically, managing large reference collections, syncing library metadata, enriching bibliographic data, and connecting LLM agents to a local Zotero library.
 - **Citation Management** - Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata from multiple sources (CrossRef, PubMed, arXiv), validate citations, and generate properly formatted BibTeX entries. Features include converting DOIs, PMIDs, or arXiv IDs to BibTeX, cleaning and formatting bibliography files, finding highly cited papers, checking for duplicates, and ensuring consistent citation formatting. Use cases: building bibliographies for manuscripts, verifying citation accuracy, citation deduplication, and maintaining reference databases
-- **Generate Image** - AI-powered image generation and editing for scientific illustrations, schematics, and visualizations using OpenRouter's image generation models. Supports multiple models including google/gemini-3-pro-image-preview (high quality, recommended default) and black-forest-labs/flux.2-pro (fast, high quality). Key features include: text-to-image generation from detailed prompts, image editing capabilities (modify existing images with natural language instructions), automatic base64 encoding/decoding, PNG output with configurable paths, and comprehensive error handling. Requires OpenRouter API key (via .env file or environment variable). Use cases: generating scientific diagrams and illustrations, creating publication-quality figures, editing existing images (changing colors, adding elements, removing backgrounds), producing schematics for papers and presentations, visualizing experimental setups, creating graphical abstracts, and generating conceptual illustrations for scientific communication
-- **Infographics** - Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes
+- **Generate Image** - AI-powered image generation and editing for scientific illustrations, schematics, and visualizations using OpenRouter's image generation models. Supports multiple models including google/gemini-3.6-flash (high quality, recommended default) and black-forest-labs/flux.2-pro (fast, high quality). Key features include: text-to-image generation from detailed prompts, image editing capabilities (modify existing images with natural language instructions), automatic base64 encoding/decoding, PNG output with configurable paths, and comprehensive error handling. Requires OpenRouter API key (via .env file or environment variable). Use cases: generating scientific diagrams and illustrations, creating publication-quality figures, editing existing images (changing colors, adding elements, removing backgrounds), producing schematics for papers and presentations, visualizing experimental setups, creating graphical abstracts, and generating conceptual illustrations for scientific communication
+- **Infographics** - Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes
 - **LaTeX Posters** - Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientific communication with layout design, color schemes, multi-column formats, figure integration, and poster-specific best practices. Features compliance with conference size requirements (A0, A1, 36×48"), complex multi-column layouts, and integration of figures, tables, equations, and citations. Use cases: conference poster sessions, thesis defenses, symposia presentations, and research group templates
 - **Market Research Reports** - Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation, deep integration with research-lookup for data gathering, and multi-framework strategic analysis including Porter's Five Forces, PESTLE, SWOT, TAM/SAM/SOM, and BCG Matrix. Use cases: investment decisions, strategic planning, competitive landscape analysis, market sizing, and market entry evaluation
 - **PPTX Posters** - Create professional research posters using PowerPoint/HTML formats for researchers who prefer WYSIWYG tools over LaTeX. Features design principles, layout templates, quality checklists, and export guidance for poster sessions. Use cases: conference posters when LaTeX is not preferred, quick poster creation, and collaborative poster design
-- **Scientific Schematics** - Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review with document-type-specific thresholds (journal: 8.5/10, conference: 8.0/10, poster: 7.0/10). Specializes in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations. Features natural language input, automatic quality assessment, and publication-ready output. Use cases: creating figures for papers, generating workflow diagrams, visualizing experimental designs, and producing graphical abstracts
+- **Scientific Schematics** - Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review with document-type-specific thresholds (journal: 8.5/10, conference: 8.0/10, poster: 7.0/10). Specializes in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations. Features natural language input, automatic quality assessment, and publication-ready output. Use cases: creating figures for papers, generating workflow diagrams, visualizing experimental designs, and producing graphical abstracts
 - **Scientific Slides** - Build slide decks and presentations for research talks using PowerPoint and LaTeX Beamer. Features slide structure, design templates, timing guidance, and visual validation. Emphasizes visual engagement with minimal text, research-backed content with proper citations, and story-driven narrative. Use cases: conference presentations, academic seminars, thesis defenses, grant pitches, and professional talks
 - **Venue Templates** - Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). Provides ready-to-use templates and detailed specifications for successful academic submissions. Use cases: manuscript preparation, conference papers, research posters, and grant proposals with venue-specific formatting
 

upstream/K-Dense-AI-scientific-agent-skills/catalogue/skills/clinical-reports/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/clinical-reports/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-26
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/clinical-reports/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: unknown
 upstream_changes: accepted
 name: clinical-reports
@@ -10,7 +10,7 @@ description: Write comprehensive clinical reports including case reports (CARE g
 allowed-tools: Read Write Edit Bash
 license: MIT License
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # Clinical Report Writing

upstream/K-Dense-AI-scientific-agent-skills/catalogue/skills/literature-review/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/literature-review/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/literature-review/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: catalogue
 upstream_changes: accepted
 name: literature-review
@@ -10,7 +10,7 @@ description: Conduct comprehensive, systematic literature reviews using multiple
 allowed-tools: Read Write Edit Bash
 license: MIT license
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.3", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # Literature Review

upstream/K-Dense-AI-scientific-agent-skills/catalogue/skills/peer-review/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/peer-review/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/peer-review/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: unknown
 upstream_changes: accepted
 name: peer-review
@@ -10,7 +10,7 @@ description: Structured manuscript/grant review with checklist-based evaluation.
 allowed-tools: Read Write Edit Bash
 license: MIT license
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.3", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # Scientific Critical Evaluation and Peer Review

upstream/K-Dense-AI-scientific-agent-skills/catalogue/skills/pptx-posters/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/pptx-posters/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/pptx-posters/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: catalogue
 upstream_changes: accepted
 name: pptx-posters
@@ -10,7 +10,7 @@ description: Create research posters using HTML/CSS that can be exported to PDF
 allowed-tools: Read Write Edit Bash
 license: MIT license
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # PPTX Research Posters (HTML-Based)

upstream/K-Dense-AI-scientific-agent-skills/catalogue/skills/scholar-evaluation/SKILL.md

@@ -1,15 +1,15 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/scholar-evaluation/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/scholar-evaluation/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: unknown
 upstream_changes: accepted
 name: scholar-evaluation
 description: Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
 license: MIT license
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # Scholar Evaluation

upstream/K-Dense-AI-scientific-agent-skills/catalogue/skills/scientific-schematics/SKILL.md

@@ -1,28 +1,28 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/scientific-schematics/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/scientific-schematics/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: catalogue
 upstream_changes: accepted
 name: scientific-schematics
-description: Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
+description: Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
 allowed-tools: Read Write Edit Bash
 license: MIT license
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # Scientific Schematics and Diagrams
 
 ## Overview
 
-Scientific schematics and diagrams transform complex concepts into clear visual representations for publication. **This skill uses Nano Banana 2 AI for diagram generation with Gemini 3.1 Pro Preview quality review.**
+Scientific schematics and diagrams transform complex concepts into clear visual representations for publication. **This skill uses Nano Banana 2 AI for diagram generation with Gemini 3.6 Flash quality review.**
 
 **How it works:**
 - Describe your diagram in natural language
 - Nano Banana 2 generates publication-quality images automatically
-- **Gemini 3.1 Pro Preview reviews quality** against document-type thresholds
+- **Gemini 3.6 Flash reviews quality** against document-type thresholds
 - **Smart iteration**: Only regenerates if quality is below threshold
 - Publication-ready output in minutes
 - No coding, templates, or manual drawing required
@@ -62,7 +62,7 @@ python scripts/generate_schematic.py "Complex circuit diagram with op-amp, resis
 
 **What happens behind the scenes:**
 1. **Generation 1**: Nano Banana 2 creates initial image following scientific diagram best practices
-2. **Review 1**: **Gemini 3.1 Pro Preview** evaluates quality against document-type threshold
+2. **Review 1**: **Gemini 3.6 Flash** evaluates quality against document-type threshold
 3. **Decision**: If quality >= threshold → **DONE** (no more iterations needed!)
 4. **If below threshold**: Improved prompt based on critique, regenerate
 5. **Repeat**: Until quality meets threshold OR max iterations reached
@@ -156,7 +156,7 @@ python scripts/generate_schematic.py "your diagram description" -o output.png
 
 ---
 
-# AI Generation Mode (Nano Banana 2 + Gemini 3.1 Pro Preview Review)
+# AI Generation Mode (Nano Banana 2 + Gemini 3.6 Flash Review)
 
 ## Smart Iterative Refinement Workflow
 
@@ -168,7 +168,7 @@ The AI generation system uses **smart iteration** - it only regenerates if quali
 ┌─────────────────────────────────────────────────────┐
 │  1. Generate image with Nano Banana 2             │
 │                    ↓                                │
-│  2. Review quality with Gemini 3.1 Pro Preview                │
+│  2. Review quality with Gemini 3.6 Flash            │
 │                    ↓                                │
 │  3. Score >= threshold?                             │
 │       YES → DONE! (early stop)                      │
@@ -186,9 +186,9 @@ Scientific diagram guidelines + User request
 
 **Output:** `diagram_v1.png`
 
-### Quality Review by Gemini 3.1 Pro Preview
+### Quality Review by Gemini 3.6 Flash
 
-Gemini 3.1 Pro Preview evaluates the diagram on:
+Gemini 3.6 Flash evaluates the diagram on:
 1. **Scientific Accuracy** (0-2 points) - Correct concepts, notation, relationships
 2. **Clarity and Readability** (0-2 points) - Easy to understand, clear hierarchy
 3. **Label Quality** (0-2 points) - Complete, readable, consistent labels
@@ -225,10 +225,10 @@ VERDICT: ACCEPTABLE (for poster, threshold 7.0)
 ### Subsequent Iterations (Only If Needed)
 
 If quality is below threshold, the system:
-1. Extracts specific issues from Gemini 3.1 Pro Preview's review
+1. Extracts specific issues from Gemini 3.6 Flash's review
 2. Enhances the prompt with improvement instructions
 3. Regenerates with Nano Banana 2
-4. Reviews again with Gemini 3.1 Pro Preview
+4. Reviews again with Gemini 3.6 Flash
 5. Repeats until threshold met or max iterations reached
 
 ### Review Log

upstream/K-Dense-AI-scientific-agent-skills/skills/citation-management/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/citation-management/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-26
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/citation-management/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: prompt
 upstream_changes: accepted
 name: citation-management
@@ -10,7 +10,7 @@ description: Comprehensive citation management for academic research. Search Goo
 allowed-tools: Read Write Edit Bash
 license: MIT License
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for LLM-powered citation steps.", "required_for": "optional features"}, {"name": "NCBI_EMAIL", "prompt": "Email for NCBI Entrez identification.", "required_for": "optional features"}, {"name": "NCBI_API_KEY", "prompt": "NCBI API key to raise Entrez rate limits.", "required_for": "optional features"}]
-metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for LLM-powered citation steps."}, {"name": "NCBI_EMAIL", "required": false, "description": "Email for NCBI Entrez identification."}, {"name": "NCBI_API_KEY", "required": false, "description": "NCBI API key to raise Entrez rate limits."}]}}
+metadata: {"version": "1.3", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for LLM-powered citation steps."}, {"name": "NCBI_EMAIL", "required": false, "description": "Email for NCBI Entrez identification."}, {"name": "NCBI_API_KEY", "required": false, "description": "NCBI API key to raise Entrez rate limits."}]}}
 ---
 
 # Citation Management

upstream/K-Dense-AI-scientific-agent-skills/skills/clinical-decision-support/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/clinical-decision-support/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-26
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/clinical-decision-support/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: prompt
 upstream_changes: accepted
 name: clinical-decision-support
@@ -10,7 +10,7 @@ description: Generate professional clinical decision support (CDS) documents for
 allowed-tools: Read Write Edit Bash
 license: MIT License
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # Clinical Decision Support Documents

upstream/K-Dense-AI-scientific-agent-skills/skills/hypothesis-generation/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/hypothesis-generation/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/hypothesis-generation/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: prompt
 upstream_changes: accepted
 name: hypothesis-generation
@@ -10,7 +10,7 @@ description: Structured hypothesis formulation from observations. Use when you h
 allowed-tools: Read Write Edit Bash
 license: MIT license
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # Scientific Hypothesis Generation

upstream/K-Dense-AI-scientific-agent-skills/skills/infographics/SKILL.md

@@ -1,28 +1,28 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/infographics/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/infographics/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: prompt
 upstream_changes: accepted
 name: infographics
-description: "Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes."
+description: "Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes."
 allowed-tools: Read Write Edit Bash
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # Infographics
 
 ## Overview
 
-Infographics are visual representations of information, data, or knowledge designed to present complex content quickly and clearly. **This skill uses Nano Banana Pro AI for infographic generation with Gemini 3 Pro quality review and Perplexity Sonar for research.**
+Infographics are visual representations of information, data, or knowledge designed to present complex content quickly and clearly. **This skill uses Nano Banana Pro AI for infographic generation with Gemini 3.6 Flash quality review and Perplexity Sonar for research.**
 
 **How it works:**
 - (Optional) **Research phase**: Gather accurate facts and statistics using Perplexity Sonar
 - Describe your infographic in natural language
 - Nano Banana Pro generates publication-quality infographics automatically
-- **Gemini 3 Pro reviews quality** against document-type thresholds
+- **Gemini 3.6 Flash reviews quality** against document-type thresholds
 - **Smart iteration**: Only regenerates if quality is below threshold
 - Professional-ready output in minutes
 - No design skills required
@@ -74,7 +74,7 @@ python skills/infographics/scripts/generate_infographic.py \
 **What happens behind the scenes:**
 1. **(Optional) Research**: Perplexity Sonar gathers accurate facts, statistics, and data
 2. **Generation 1**: Nano Banana Pro creates initial infographic following design best practices
-3. **Review 1**: **Gemini 3 Pro** evaluates quality against document-type threshold
+3. **Review 1**: **Gemini 3.6 Flash** evaluates quality against document-type threshold
 4. **Decision**: If quality >= threshold → **DONE** (no more iterations needed!)
 5. **If below threshold**: Improved prompt based on critique, regenerate
 6. **Repeat**: Until quality meets threshold OR max iterations reached
@@ -369,7 +369,7 @@ python skills/infographics/scripts/generate_infographic.py \
 ┌─────────────────────────────────────────────────────┐
 │  1. Generate infographic with Nano Banana Pro       │
 │                    ↓                                │
-│  2. Review quality with Gemini 3 Pro                │
+│  2. Review quality with Gemini 3.6 Flash            │
 │                    ↓                                │
 │  3. Score >= threshold?                             │
 │       YES → DONE! (early stop)                      │
@@ -381,7 +381,7 @@ python skills/infographics/scripts/generate_infographic.py \
 
 ### Quality Review Criteria
 
-Gemini 3 Pro evaluates each infographic on:
+Gemini 3.6 Flash evaluates each infographic on:
 
 1. **Visual Hierarchy & Layout** (0-2 points)
    - Clear visual hierarchy

upstream/K-Dense-AI-scientific-agent-skills/skills/latex-posters/SKILL.md

@@ -1,15 +1,15 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/latex-posters/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/latex-posters/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: prompt
 upstream_changes: accepted
 name: latex-posters
 description: "Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientific communication. Includes layout design, color schemes, multi-column formats, figure integration, and poster-specific best practices for visual communication."
 allowed-tools: Read Write Edit Bash
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # LaTeX Research Posters

upstream/K-Dense-AI-scientific-agent-skills/skills/markitdown/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/markitdown/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/markitdown/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: prompt
 upstream_changes: accepted
 name: markitdown
@@ -10,7 +10,7 @@ description: Convert files and office documents to Markdown. Supports PDF, DOCX,
 allowed-tools: Read Write Edit Bash
 license: MIT license
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # MarkItDown - File to Markdown Conversion

upstream/K-Dense-AI-scientific-agent-skills/skills/markitdown/references/api_reference.md

@@ -2,9 +2,9 @@
 title: "MarkItDown API Reference"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/markitdown/references/api_reference.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/markitdown/references/api_reference.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: prompt
 upstream_changes: accepted
 author: upstream
@@ -260,7 +260,7 @@ result = md.convert("presentation.pptx")
 
 Popular models with vision support:
 - `anthropic/claude-opus-4.5` - **Recommended for scientific vision**
-- `google/gemini-3-pro-preview` - Gemini Pro Vision
+- `google/gemini-3.6-flash` - Gemini Flash Vision
 
 See https://openrouter.ai/models for the complete list.
 

upstream/K-Dense-AI-scientific-agent-skills/skills/scientific-slides/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/scientific-slides/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/scientific-slides/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: prompt
 upstream_changes: accepted
 name: scientific-slides
@@ -10,7 +10,7 @@ description: Build slide decks and presentations for research talks. Use this fo
 allowed-tools: Read Write Edit Bash
 license: MIT license
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # Scientific Slides

upstream/K-Dense-AI-scientific-agent-skills/skills/scientific-writing/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/scientific-writing/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/scientific-writing/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: prompt
 upstream_changes: accepted
 name: scientific-writing
@@ -10,7 +10,7 @@ description: Core skill for the deep research and writing tool. Write scientific
 allowed-tools: Read Write Edit Bash
 license: MIT license
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # Scientific Writing

upstream/K-Dense-AI-scientific-agent-skills/skills/treatment-plans/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/treatment-plans/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/831d49eb/skills/treatment-plans/SKILL.md
+upstream_sha: 831d49eb
+imported_at: 2026-07-21
 prompt_class: prompt
 upstream_changes: accepted
 name: treatment-plans
@@ -10,7 +10,7 @@ description: Generate concise (3-4 page), focused medical treatment plans in LaT
 allowed-tools: Read Write Edit Bash
 license: MIT license
 required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
-metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
+metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}
 ---
 
 # Treatment Plan Writing

upstream/yboulaamane-awesome-drug-discovery/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Awesome Drug Discovery [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/yboulaamane/awesome-drug-discovery/blob/1b8ee074/README.md
-upstream_sha: 1b8ee074
-imported_at: 2026-07-16
+upstream_source: https://github.com/yboulaamane/awesome-drug-discovery/blob/475719b9/README.md
+upstream_sha: 475719b9
+imported_at: 2026-06-27
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -82,7 +82,7 @@ A meticulously curated resource list focused on computational methods for drug d
 - [ANPDB](https://phabidb.vm.uni-freiburg.de/anpdb/) - 27k+ African medicinal plant compounds.  
 - [SANCDB](https://sancdb.rubi.ru.ac.za/) - Natural compounds from the plant and marine life in and around South Africa.  
 - [CMNPD](https://www.cmnpd.org/) - 31k+ marine natural products.  
-- [The Natural Products Atlas](https://www.npatlas.org/) - An open-access database for microbial natural products structures and metadata.  
+- [SistematX](https://sistematx.ufpb.br/) - 8k+ secondary metabolites.  
 - [CoumarinDB](https://yboulaamane.github.io/CoumarinDB/) - A manually curated database on coumarins from plants.  
 - [ArtemisiaDB](https://yboulaamane.github.io/ArtemisiaDB/) - Artemisia genus compounds.  
 - [BIAdb](https://webs.iiitd.edu.in/raghava/biadb/type.php?tp=natural) - A database for benzylisoquinoline alkaloids.  
@@ -109,7 +109,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [canSAR](https://cansar.ai/) - Integrative cancer knowledgebase aggregating molecular, genetic, and structural data for drug target identification.
 - [CDD Vault](https://www.collaborativedrug.com/public-access-cdd-vault) - Hosted informatics platform providing public access to aggregated drug discovery data.
 - [ClinicalTrials.gov](https://clinicaltrials.gov/) - Comprehensive registry and results database for clinical studies involving human participants.
-- [CovalentInDB (CIDB)](https://cadd.zju.edu.cn/cidb/) - A comprehensive database dedicated to covalent inhibitors, targets, and experimental data.
 - [HSADab](https://github.com/proszxppp/HSADab) - Database of binding thermodynamics, structures, and docking data for human serum albumin.
 
 ---
@@ -124,7 +123,7 @@ A meticulously curated resource list focused on computational methods for drug d
 - [InterPro](https://www.ebi.ac.uk/interpro/) - Protein classification and domain prediction.
 - [AlphaFold DB](https://alphafold.ebi.ac.uk/) - Predicted structures from AlphaFold.
 - [Proteopedia](https://proteopedia.org/wiki/index.php/Main_Page) - Interactive protein visualizations.
-- [Pfam](https://www.ebi.ac.uk/interpro/entry/pfam/) - Collection of protein families represented by multiple sequence alignments and hidden Markov models.
+- [Pfam](https://pfam.xfam.org/) - Collection of protein families represented by multiple sequence alignments and hidden Markov models.
 - [Human Protein Atlas](https://www.proteinatlas.org/) - Spatial mapping of all human proteins across tissues and cells.
 
 ### Binding Site and Pocket Detection
@@ -142,7 +141,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [MODELLER](https://salilab.org/modeller/) - A software for homology or comparative modeling of protein structures.
 - [PDBFixer](https://github.com/openmm/pdbfixer) - Repairs PDB files by adding missing atoms, residues, and hydrogens for MD simulations.
 - [OpenFold Portal](https://portal.openfold.omsf.io/) - Cloud portal for predicting 3D protein structures using the open-source OpenFold model.
-- [RFdiffusion](https://github.com/RosettaCommons/RFdiffusion) - Open-source method for de novo protein design using structure-guided diffusion models.
 - [Melodia](https://github.com/rwmontalvao/Melodia_py) - Python library for analyzing and comparing protein structure shapes via differential geometry.
 
 ---
@@ -170,7 +168,9 @@ A meticulously curated resource list focused on computational methods for drug d
 ## Ligand Design and Optimization
 
 ### Pharmacophore Modeling
+- [ZINCPharmer](http://zincpharmer.csb.pitt.edu/) - Pharmacophore screening.
 - [Pharmit](https://pharmit.csb.pitt.edu/) - Interactive pharmacophore modeling.
+- [AnchorQuery](http://anchorquery.csb.pitt.edu/) - Pharmacophore-based search engine specialized in protein–protein interaction sites.
 
 ### QSAR and Descriptor Tools
 - [QSAR Toolbox](https://qsartoolbox.org/) - Hazard assessment and QSAR.
@@ -178,7 +178,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [ChemMaster](https://crescent-silico.com/chemmaster/) - QSAR and cheminformatics suite.
 - [3D-QSAR](https://www.3d-qsar.com/) - Web resources for 3D QSAR modeling.
 - [QSAR-Co](https://sites.google.com/view/qsar-co/) - Robust multitarget QSAR modeling.
-- [QSPRpred](https://github.com/CDDLeiden/QSPRpred) - Open-source Python toolkit for building, reproducing, and deploying QSAR/QSPR models.
 - [DataWarrior](https://openmolecules.org/datawarrior/) - Free software for chemical analysis, QSAR, and visualization.
 - [KNIME](https://www.knime.com/) - Workflow platform for cheminformatics and ML integration.
 - [pyADA](https://github.com/jeffrichardchemistry/pyADA) - Assesses the applicability domain of molecular fingerprints via similarity-based thresholds for QSAR validation.
@@ -271,15 +270,15 @@ A meticulously curated resource list focused on computational methods for drug d
 - [Desmond](https://www.deshawresearch.com/resources.html) - GPU-accelerated MD engine for high-performance simulations.
 
 ### Topology and Force Field Tools
-- [CGenFF](https://cgenff.silcsbio.com/) - CHARMM force field parametrization of drug-like molecules.
+- [CGenFF](https://cgenff.umaryland.edu/) - CHARMM force field parametrization of drug-like molecules.
 - [SwissParam](https://www.swissparam.ch/) - Rapid generation of CHARMM-compatible parameters for small organic molecules.
 - [ATB](https://atb.uq.edu.au/) - Automated topology builder and repository for classical force field parameters.
 - [CHARMM-GUI](https://www.charmm-gui.org/) - Web-based interface for building complex biomolecular systems and generating MD input files.
-- [LigParGen](https://traken.chem.yale.edu/ligpargen/) - Automated OPLS-AA parameter generator for organic ligands.
+- [LigParGen](https://zarbi.chem.yale.edu/ligpargen/) - Automated OPLS-AA parameter generator for organic ligands.
 
 ### Analysis Tools
 - [MD DaVis](https://md-davis.readthedocs.io/en/latest/index.html) - Interactive visualization and analysis of MD trajectories.
-- [iMODS](https://imods.chaconlab.org/) - Normal Mode Analysis toolkit using internal coordinates.
+- [iMod](https://imods.iqfr.csic.es/) - Normal Mode Analysis toolkit using internal coordinates.
 - [MolAiCal](https://molaical.github.io/) - Web-based platform for binding free energy calculations using MM/PBSA and MM/GBSA methods.
 - [gmx_MMPBSA](https://valdes-tresanco-ms.github.io/gmx_MMPBSA/dev/) - Port of AMBER MMPBSA.py for GROMACS.
 - [VMD](https://www.ks.uiuc.edu/Research/vmd/) - Large biomolecular systems visualization and analysis using 3D graphics and scripting.
@@ -288,7 +287,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [MDAnalysis](https://www.mdanalysis.org/) - Open-source Python library for analyzing MD simulations.
 - [CABS-flex 3.0](https://lcbio.pl/cabsflex3/) - Web server for rapid simulation of protein and peptide structural flexibility using coarse-grained models.
 - [cmd-viewer](https://github.com/Kopec-Lab/cmd-viewer) - Tool for visualizing and analyzing MD simulation trajectories and structural data.
-- [Pharmacon](https://github.com/k-georgiou/pharmacon) - Open-source toolkit for molecular dynamics simulation analysis in medicinal chemistry.
 
 ---
 
@@ -297,7 +295,7 @@ A meticulously curated resource list focused on computational methods for drug d
 - [AiZynthFinder](https://github.com/MolecularAI/aizynthfinder) - Monte Carlo tree search-based retrosynthesis using trained neural networks.
 - [ASKCOS](https://askcos.mit.edu/) - Synthesis route prediction with ML, developed by MIT.
 - [IBM RoboRXN](https://rxn.res.ibm.com/rxn/robo-rxn/welcome) - Automated reaction prediction using transformer models.
-- [MANIFOLD](https://postera.ai/) - Search engine for synthetically accessible molecules and building blocks.
+- [MANIFOLD](https://app.postera.ai/manifold/) - Search engine for synthetically accessible molecules and building blocks.
 - [onepot.ai](https://www.onepot.ai/) - AI-enabled molecular editor and synthesis planning platform with an encrypted structure environment.
 
 ---
@@ -310,8 +308,8 @@ A meticulously curated resource list focused on computational methods for drug d
 
 ### Peptide Design
 - [PepDraw](https://pepdraw.com/) - Peptide visualization with annotated physicochemical properties.
-- [PEP-SiteFinder](https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-SiteFinder/) - Predicts peptide-binding sites on protein structures using drug-like ligand mapping.
-- [PEP-FOLD3](https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3/) - De novo peptide structure prediction framework.
+- [PepSite](http://pepsite2.russelllab.org/) - Predict peptide binding sites on protein surfaces using structural data.
+- [Peptimap](https://peptimap.bu.edu/) - Peptide mapping and binding hotspots identification.
 
 ---
 
@@ -329,21 +327,19 @@ A meticulously curated resource list focused on computational methods for drug d
 
 ### Chemistry-focused ML Frameworks
 - [DeepChem](https://github.com/deepchem/deepchem) - Open-source deep learning framework for chemistry and biology.
-- [scikit-mol](https://github.com/EBjerrum/scikit-mol) - Open-source toolkit bridging RDKit and scikit-learn for molecular ML workflows.  
+- [scikit-mol](https://github.com/datamol-io/scikit-mol) - Open-source toolkit bridging RDKit and scikit-learn for molecular ML workflows.  
 - [Chemprop](https://github.com/chemprop/chemprop) - Directed message passing neural networks for molecular property prediction.
 - [ChemML](https://github.com/hachmannlab/chemml) - Machine learning and informatics suite for analyzing, mining, and modeling chemical and materials data.
-- [Oloren ChemEngine](https://pypi.org/project/olorenchemengine/) - Unified API for molecular property prediction with uncertainty quantification, interpretability, and model tuning.
+- [Oloren ChemEngine](https://github.com/Oloren-AI/olorenchemengine) - Unified API for molecular property prediction with uncertainty quantification, interpretability, and model tuning.
 - [TorchDrug](https://torchdrug.ai/) - A machine learning library for drug discovery with support for GNNs and molecular datasets.
 - [DGL-LifeSci](https://github.com/awslabs/dgl-lifesci) - Graph deep learning toolkit for life sciences using the Deep Graph Library.
 - [iChem](https://github.com/mqcomplab/iChem) - Python cheminformatics package for molecular comparisons, fingerprints, and chemical data analysis.
 - [LigandForge](https://github.com/HTS-Oracle/LigandForge) - ML-based structure-guided de novo ligand generation and optimization for hit identification.
+- [LigandForge Web](https://ligandforge.onrender.com/) - Web interface for LigandForge with interactive 3D visualization of lead compound candidates.
 
 ### Pretrained Models
 - [MolBERT](https://github.com/BenevolentAI/MolBERT) - Transformer-based molecular representation learning.
 - [ChemBERTa](https://huggingface.co/seyonec/ChemBERTa-zinc-base-v1) - Pretrained BERT-like models for molecules from SMILES.
-- [Chai-1](https://github.com/chaidiscovery/chai-lab) - Multi-modal foundation model for biomolecular structure prediction of proteins, nucleic acids, and ligands.
-- [ESM3](https://github.com/evolutionaryscale/esm) - Generative biology foundation model for designing novel proteins across sequence, structure, and function.
-- [ESMc](https://biohub.ai/models/esmc) - A family of open protein language foundation models for sequence generation and design.
 - [Uni-Mol](https://github.com/dptech-corp/Uni-Mol) - 3D molecular representation learning framework.
 - [Boltz-2](https://github.com/jwohlwend/boltz) - A foundation model that jointly predicts structure and binding affinity, rivaling physics-based FEP methods in accuracy.
 - [Zatom](https://github.com/Zatom-AI/zatom) - AI-driven generative chemistry platform for discovering and analyzing molecular structures.
@@ -364,9 +360,9 @@ A meticulously curated resource list focused on computational methods for drug d
 - [OPSIN](https://opsin.ch.cam.ac.uk) - Convert IUPAC names to chemical structures.
 - [OSRA](https://cactus.nci.nih.gov/cgi-bin/osra/index.cgi) - Extract chemical structures from images.
 - [ChemPlot](https://chemplot.streamlit.app/) - Chemical space visualization.
-- [ChemDB](https://chemdb.igb.uci.edu/) - Chemoinformatics portal with compound data and tools.
+- [ChemDB](http://cdb.ics.uci.edu/) - Chemoinformatics portal with compound data and tools.
 - [Screening Explorer](http://stats.drugdesign.fr/) - Analyze screening datasets and hit distributions.
-- [spyrmsd](https://github.com/RMeli/spyrmsd) - Python tool for symmetry-corrected RMSD calculations using graph isomorphism.
+- [LigRMSD](https://ligrmsd.appsbio.utalca.cl/) - Calculate RMSD between ligand poses.
 - [NERDD](https://nerdd.univie.ac.at/) - Curated drug discovery resources.
 - [LigBuilder3](http://www.pkumdl.cn:8080/ligbuilder3/) - De novo ligand design.
 - [ChemMine Tools](https://chemminetools.ucr.edu/) - Web-based cheminformatics toolkit for compound analysis.
@@ -401,11 +397,11 @@ A meticulously curated resource list focused on computational methods for drug d
 - [BIGCHEM](https://bigchem.eu/node/63) - Online course on big data applications in chemistry.
 - [Drug Discovery Course](https://www.stereoelectronics.org/webDD/DD_home.html) - Foundations of drug discovery and development.
 - [drugdesign.org](https://www.drugdesign.org/) - Free courses on drug design and cheminformatics.
-- [Learn CADD](https://learn-cadd.vercel.app/) - An interactive, visual, first-principles guide to computer-aided drug design.
 - [Cheminformatics OLCC](https://chem.libretexts.org/Courses/Intercollegiate_Courses/Cheminformatics) - Intercollegiate course on cheminformatics theory and coding.
 - [Python For Cheminformatics Docking](https://pdb101.rcsb.org/train/training-events/python4) - Python tutorials for molecular docking via RCSB.
 - [DDA CDD Workshop](https://wcair.dundee.ac.uk/training/training-resources/computational-drug-design/) - Workshop on generative and computational drug design.
 - [MDTutorials](http://www.mdtutorials.com/gmx/) - Step-by-step tutorials for MD simulations using GROMACS.
+- [Computer Aided Drug Design](https://courses.ebo-bio-solution.co.uk/courses/introduction-to-chemoinformatics-and-computational-drug-discovery/lessons/1-computer-aided-drug-design/) - Foundational introduction to chemoinformatics and computational drug design.
 - [Resources for Learning Bioinformatics](https://learnbioinformatics.org/) - Curated collection of tutorials and materials for bioinformatics and computational biology.
 - [Synthesis Workshop](https://synthesis-workshop.com/) - Open-access video podcast on advanced organic synthesis and medicinal chemistry.