---

name: research-project description: This skill should be used when initializing a new bioinformatics research project, checking project status, updating project phase, or getting research best practices guidance. Triggered by requests like "initialize project", "check status", "update phase", or "research best practices".

Research Project Management

Overview

Provides comprehensive project steering and management for bioinformatics research projects. Handles initialization, phase tracking, status monitoring, and best practices guidance.

Core Capabilities

1. Project Initialization

Initialize a new research project structure using scripts/init_project.py.

When to use: When starting a new research project or setting up a standardized structure.

Workflow:

1. Confirm project location (current directory or specified path)
2. Run initialization script from the plugin directory:

   python "${CLAUDE_PLUGIN_ROOT}/scripts/init_project.py" --path /path/to/target/project
   

   Important: Use ${CLAUDE_PLUGIN_ROOT} to reference the plugin's installation directory. The --path argument specifies where the project structure will be created.
3. Verify created structure
4. Guide user to next steps (edit STEERING.md, create first experiment)

Created structure:

project/
├── STEERING.md                             # Project progress tracker
├── notebook/
│   ├── tasks.md                            # Task management
│   ├── labnote/
│   │   ├── Exp00_TEMPLATE_labnote.ipynb    # Jupyter template
│   │   └── Exp00_TEMPLATE_labnote.md       # Markdown template
│   ├── report/
│   │   └── Exp00_TEMPLATE_report.md        # Report template
│   └── knowledge/                          # Reusable procedures
├── inbox/                                  # User input files
│   └── archive/                            # Processed files
├── data/raw/                               # Raw data (gitignored)
└── results/                                # Outputs (gitignored)

Command: /research-init

2. Status Checking

Check current project status, phase, and next actions.

When to use: When user asks "what's the status?", "where are we?", or "what should I do next?"

Workflow:

1. Read STEERING.md for current phase and priorities
2. Read notebook/tasks.md for experiment progress
3. Summarize:
   • Current phase
   • Active experiments
   • Completed milestones
   • Next recommended actions

Command: /research-status

3. Phase Management

Guide transitions between research phases using references/phases.md.

Research phases:

• Planning: Define research questions and hypotheses
• Exploration: Initial data analysis and hypothesis refinement
• Execution: Systematic experimentation
• Integration: Synthesize results into reports
• Publication: Prepare manuscripts and documentation

When to use: When project reaches a natural transition point or user requests phase update.

Workflow:

1. Review current phase from STEERING.md
2. Check phase completion criteria from references/phases.md
3. If criteria met, suggest phase transition
4. Update STEERING.md with new phase and priorities

4. Best Practices Guidance

Provide research best practices from references/best-practices.md and references/quality-standards.md.

When to use: When user needs guidance on:

• Hypothesis formulation
• Experimental design
• Scientific writing
• Data interpretation
• Quality standards

Key principles:

• Hypothesis-driven: Always start with testable hypotheses
• Reproducibility: Document everything for reproducibility
• Fact/interpretation separation: Keep observations separate from conclusions
• Progressive disclosure: Structure information hierarchically

5. Content Review

Proactively review user-created content against quality standards.

When to use: When user presents:

• Draft reports or conclusions
• Lab notebook entries
• Any scientific claims or findings

Action: Review content against references/quality-standards.md checklist:

1. Fact vs. Interpretation Check:

   • Are observations (facts) clearly separated from interpretation?
   • Are claims properly qualified with uncertainty level?
   • Are conclusions supported by cited evidence?

2. Evidence Traceability Check:

   • Does each claim link to a notebook/figure/table?
   • Are statistics complete (test name, n, effect size, p-value)?
   • Are figure references valid and accessible?

3. Reproducibility Check:

   • Are methods detailed enough for replication?
   • Are software/data versions specified?
   • Are random seeds documented?

Output: Provide constructive feedback with specific improvement suggestions.

Example feedback:

### Review Feedback

**Fact/Interpretation Issues**:
- Line 23: "Gene X regulates pathway Y" - This is interpretation, not fact.
  Suggest: "Gene X expression correlated with pathway Y activity (r=0.85, p<0.01)"

**Missing Evidence Links**:
- Finding 2 has no figure reference. Add: "(Figure 2B, Exp03_analysis.ipynb)"

**Statistics Incomplete**:
- Effect size missing for differential expression claim. Add fold-change and CI.

Resources

scripts/

• init_project.py: Project initialization script (executable)

commands/

• research-init.md: Project initialization command (/research-init)
• research-status.md: Status checking command (/research-status)

references/

• phases.md: Detailed phase definitions and transition criteria
• best-practices.md: Hypothesis-driven research guidelines
• quality-standards.md: Scientific quality standards (fact/interpretation/conclusion separation)

Usage Notes

• Always confirm project path before initialization
• Check for existing files to avoid overwriting
• Guide users through post-initialization steps
• Proactively suggest phase transitions when criteria are met
• Reference best practices when users show uncertainty