start
Set up your bio-research environment and explore available tools. Use when first getting oriented with the plugin, checking which literature, drug-discovery, or visualization MCP servers are connected, or surveying available analysis skills before starting a new project.
What this skill does
# Bio-Research Start > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md). You are helping a biological researcher get oriented with the bio-research plugin. Walk through the following steps in order. ## Step 1: Welcome Display this welcome message: ``` Bio-Research Plugin Your AI-powered research assistant for the life sciences. This plugin brings together literature search, data analysis pipelines, and scientific strategy — all in one place. ``` ## Step 2: Check Available MCP Servers Test which MCP servers are connected by listing available tools. Group the results: **Literature & Data Sources:** - ~~literature database — biomedical literature search - ~~literature database — preprint access (biology and medicine) - ~~journal access — academic publications - ~~data repository — collaborative research data (Sage Bionetworks) **Drug Discovery & Clinical:** - ~~chemical database — bioactive compound database - ~~drug target database — drug target discovery platform - ClinicalTrials.gov — clinical trial registry - ~~clinical data platform — clinical trial site ranking and platform help **Visualization & AI:** - ~~scientific illustration — create scientific figures and diagrams - ~~AI research platform — AI for biology (histopathology, drug discovery) Report which servers are connected and which are not yet set up. ## Step 3: Survey Available Skills List the analysis skills available in this plugin: | Skill | What It Does | |-------|-------------| | **Single-Cell RNA QC** | Quality control for scRNA-seq data with MAD-based filtering | | **scvi-tools** | Deep learning for single-cell omics (scVI, scANVI, totalVI, PeakVI, etc.) | | **Nextflow Pipelines** | Run nf-core pipelines (RNA-seq, WGS/WES, ATAC-seq) | | **Instrument Data Converter** | Convert lab instrument output to Allotrope ASM format | | **Scientific Problem Selection** | Systematic framework for choosing research problems | ## Step 4: Optional Setup — Binary MCP Servers Mention that two additional MCP servers are available as separate installations: - **~~genomics platform** — Access cloud analysis data and workflows Install: Download `txg-node.mcpb` from https://github.com/10XGenomics/txg-mcp/releases - **~~tool database** (Harvard MIMS) — AI tools for scientific discovery Install: Download `tooluniverse.mcpb` from https://github.com/mims-harvard/ToolUniverse/releases These require downloading binary files and are optional. ## Step 5: Ask How to Help Ask the researcher what they're working on today. Suggest starting points based on common workflows: 1. **Literature review** — "Search ~~literature database for recent papers on [topic]" 2. **Analyze sequencing data** — "Run QC on my single-cell data" or "Set up an RNA-seq pipeline" 3. **Drug discovery** — "Search ~~chemical database for compounds targeting [protein]" or "Find drug targets for [disease]" 4. **Data standardization** — "Convert my instrument data to Allotrope format" 5. **Research strategy** — "Help me evaluate a new project idea" Wait for the user's response and guide them to the appropriate tools and skills.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.