Research Skill

Orchestrate parallel scientist agents for comprehensive research workflows with optional AUTO mode for fully autonomous execution.

Overview

Research is a multi-stage workflow that decomposes complex research goals into parallel investigations:

1. Decomposition - Break research goal into independent stages/hypotheses
2. Execution - Run parallel scientist agents on each stage
3. Verification - Cross-validate findings, check consistency
4. Synthesis - Aggregate results into comprehensive report

Usage Examples

/oh-my-claudecode:sciomc <goal>                    # Standard research with user checkpoints
/oh-my-claudecode:sciomc AUTO: <goal>              # Fully autonomous until complete
/oh-my-claudecode:sciomc status                    # Check current research session status
/oh-my-claudecode:sciomc resume                    # Resume interrupted research session
/oh-my-claudecode:sciomc list                      # List all research sessions
/oh-my-claudecode:sciomc report <session-id>       # Generate report for session

Quick Examples

/oh-my-claudecode:sciomc What are the performance characteristics of different sorting algorithms?
/oh-my-claudecode:sciomc AUTO: Analyze authentication patterns in this codebase
/oh-my-claudecode:sciomc How does the error handling work across the API layer?

Research Protocol

Stage Decomposition Pattern

When given a research goal, decompose into 3-7 independent stages:

## Research Decomposition

**Goal:** <original research goal>

### Stage 1: <stage-name>
- **Focus:** What this stage investigates
- **Hypothesis:** Expected finding (if applicable)
- **Scope:** Files/areas to examine
- **Tier:** LOW | MEDIUM | HIGH

### Stage 2: <stage-name>
...

Parallel Scientist Invocation

Fire independent stages in parallel via Task tool:

// Stage 1 - Simple data gathering
Task(subagent_type="oh-my-claudecode:scientist", model="haiku", prompt="[RESEARCH_STAGE:1] Investigate...")

// Stage 2 - Standard analysis
Task(subagent_type="oh-my-claudecode:scientist", model="sonnet", prompt="[RESEARCH_STAGE:2] Analyze...")

// Stage 3 - Complex reasoning
Task(subagent_type="oh-my-claudecode:scientist", model="opus", prompt="[RESEARCH_STAGE:3] Deep analysis of...")

Smart Model Routing

CRITICAL: Always pass model parameter explicitly!

Task Complexity	Agent	Model	Use For
Data gathering	scientist (model=haiku)	haiku	File enumeration, pattern counting, simple lookups
Standard analysis	scientist	sonnet	Code analysis, pattern detection, documentation review
Complex reasoning	scientist	opus	Architecture analysis, cross-cutting concerns, hypothesis validation

Routing Decision Guide

Research Task	Tier	Example Prompt
"Count occurrences of X"	LOW	"Count all usages of useState hook"
"Find all files matching Y"	LOW	"List all test files in the project"
"Analyze pattern Z"	MEDIUM	"Analyze error handling patterns in API routes"
"Document how W works"	MEDIUM	"Document the authentication flow"
"Explain why X happens"	HIGH	"Explain why race conditions occur in the cache layer"
"Compare approaches A vs B"	HIGH	"Compare Redux vs Context for state management here"

Verification Loop

After parallel execution completes, verify findings:

// Cross-validation stage
Task(subagent_type="oh-my-claudecode:scientist", model="sonnet", prompt="
[RESEARCH_VERIFICATION]
Cross-validate these findings for consistency:

Stage 1 findings: <summary>
Stage 2 findings: <summary>
Stage 3 findings: <summary>

Check for:
1. Contradictions between stages
2. Missing connections
3. Gaps in coverage
4. Evidence quality

Output: [VERIFIED] or [CONFLICTS:<list>]
")

AUTO Mode

AUTO mode runs the complete research workflow autonomously with loop control.

Loop Control Protocol

[RESEARCH + AUTO - ITERATION {{ITERATION}}/{{MAX}}]

Your previous attempt did not output the completion promise. Continue working.

Current state: {{STATE}}
Completed stages: {{COMPLETED_STAGES}}
Pending stages: {{PENDING_STAGES}}

Promise Tags

Tag	Meaning	When to Use
[PROMISE:RESEARCH_COMPLETE]	Research finished successfully	All stages done, verified, report generated
[PROMISE:RESEARCH_BLOCKED]	Cannot proceed	Missing data, access issues, circular dependency

AUTO Mode Rules

1. Max Iterations: 10 (configurable)
2. Continue until: Promise tag emitted OR max iterations
3. State tracking: Persist after each stage completion
4. Cancellation: /oh-my-claudecode:cancel or "stop", "cancel"

AUTO Mode Example

/oh-my-claudecode:sciomc AUTO: Comprehensive security analysis of the authentication system

[Decomposition]
- Stage 1 (LOW): Enumerate auth-related files
- Stage 2 (MEDIUM): Analyze token handling
- Stage 3 (MEDIUM): Review session management
- Stage 4 (HIGH): Identify vulnerability patterns
- Stage 5 (MEDIUM): Document security controls

[Execution - Parallel]
Firing stages 1-3 in parallel...
Firing stages 4-5 after dependencies complete...

[Verification]
Cross-validating findings...

[Synthesis]
Generating report...

[PROMISE:RESEARCH_COMPLETE]

Parallel Execution Patterns

Independent Dataset Analysis (Parallel)

When stages analyze different data sources:

// All fire simultaneously
Task(subagent_type="oh-my-claudecode:scientist", model="haiku", prompt="[STAGE:1] Analyze src/api/...")
Task(subagent_type="oh-my-claudecode:scientist", model="haiku", prompt="[STAGE:2] Analyze src/utils/...")
Task(subagent_type="oh-my-claudecode:scientist", model="haiku", prompt="[STAGE:3] Analyze src/components/...")

Hypothesis Battery (Parallel)

When testing multiple hypotheses:

// Test hypotheses simultaneously
Task(subagent_type="oh-my-claudecode:scientist", model="sonnet", prompt="[HYPOTHESIS:A] Test if caching improves...")
Task(subagent_type="oh-my-claudecode:scientist", model="sonnet", prompt="[HYPOTHESIS:B] Test if batching reduces...")
Task(subagent_type="oh-my-claudecode:scientist", model="sonnet", prompt="[HYPOTHESIS:C] Test if lazy loading helps...")

Cross-Validation (Sequential)

When verification depends on all findings:

// Wait for all parallel stages
[stages complete]

// Then sequential verification
Task(subagent_type="oh-my-claudecode:scientist", model="opus", prompt="
[CROSS_VALIDATION]
Validate consistency across all findings:
- Finding 1: ...
- Finding 2: ...
- Finding 3: ...
")

Concurrency Limit

Maximum 20 concurrent scientist agents to prevent resource exhaustion.

If more than 20 stages, batch them:

Batch 1: Stages 1-5 (parallel)
[wait for completion]
Batch 2: Stages 6-7 (parallel)

Session Management

Directory Structure

.omc/research/{session-id}/
  state.json              # Session state and progress
  stages/
    stage-1.md            # Stage 1 findings
    stage-2.md            # Stage 2 findings
    ...
  findings/
    raw/                  # Raw findings from scientists
    verified/             # Post-verification findings
  figures/
    figure-1.png          # Generated visualizations
    ...
  report.md               # Final synthesized report

State File Format

{
  "id": "research-20240115-abc123",
  "goal": "Original research goal",
  "status": "in_progress | complete | blocked | cancelled",
  "mode": "standard | auto",
  "iteration": 3,
  "maxIterations": 10,
  "stages": [
    {
      "id": 1,
      "name": "Stage name",
      "tier": "LOW | MEDIUM | HIGH",
      "status": "pending | running | complete | failed",
      "startedAt": "ISO timestamp",
      "completedAt": "ISO timestamp",
      "findingsFile": "stages/stage-1.md"
    }
  ],
  "verification": {
    "status": "pending | passed | failed",
    "conflicts": [],
    "completedAt": "ISO timestamp"
  },
  "createdAt": "ISO timestamp",
  "updatedAt": "ISO timestamp"
}

Session Commands

Command	Action
/oh-my-claudecode:sciomc status	Show current session progress
/oh-my-claudecode:sciomc resume	Resume most recent interrupted session
/oh-my-claudecode:sciomc resume <session-id>	Resume specific session
/oh-my-claudecode:sciomc list	List all sessions with status
/oh-my-claudecode:sciomc report <session-id>	Generate/regenerate report
/oh-my-claudecode:sciomc cancel	Cancel current session (preserves state)

Tag Extraction

Scientists use structured tags for findings. Extract them with these patterns:

Finding Tags

[FINDING:<id>] <title>
<evidence and analysis>
[/FINDING]

[EVIDENCE:<finding-id>]
- File: <path>
- Lines: <range>
- Content: <relevant code/text>
[/EVIDENCE]

[CONFIDENCE:<level>] # HIGH | MEDIUM | LOW
<reasoning for confidence level>

Extraction Regex Patterns

// Finding extraction
const findingPattern = /\[FINDING:(\w+)\]\s*(.*?)\n([\s\S]*?)\[\/FINDING\]/g;

// Evidence extraction
const evidencePattern = /\[EVIDENCE:(\w+)\]([\s\S]*?)\[\/EVIDENCE\]/g;

// Confidence extraction
const confidencePattern = /\[CONFIDENCE:(HIGH|MEDIUM
how to use sciomc
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use sciomc on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add sciomc
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/yeachan-heo/oh-my-claudecode --skill sciomccopy
The skills CLI fetches sciomc from GitHub repository yeachan-heo/oh-my-claudecode and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/sciomc
Reload or restart Cursor to activate sciomc. Access the skill through slash commands (e.g., /sciomc) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?