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Sciomc

  • 467 installs
  • 38.3k repo stars
  • Updated August 4, 2026
  • yeachan-heo/oh-my-claudecode

sciomc is an agent skill that orchestrates parallel scientist agents to decompose research goals, cross-validate findings, and synthesize comprehensive reports for developers exploring complex technical questions.

About

sciomc is a level 4 research skill in yeachan-heo/oh-my-claudecode that orchestrates parallel scientist agents for comprehensive analysis with optional AUTO mode for fully autonomous execution. The workflow runs four stages: decomposition of the research goal into independent stages or hypotheses, parallel agent execution on each stage, verification through cross-validation and consistency checks, and synthesis into a comprehensive report. Developers reach for sciomc when a question spans multiple domains, requires competing hypotheses tested in parallel, or when a single agent pass would miss contradictions in source material. AUTO mode enables hands-off runs after the research goal is supplied via the argument-hint research goal parameter. Catalog metadata records 402 installs. Use sciomc for literature-style technical investigations, comparative architecture studies, and evidence-heavy decision memos before committing to build plans. The level 4 skill fits comparative technology evaluations, market scans, and architecture trade-off studies where verified citations matter more than a single-pass summary from one agent.

  • Decomposes research goals into parallel independent investigation stages
  • Spawns multiple scientist sub-agents working simultaneously
  • Cross-validates findings across agents for consistency
  • AUTO mode enables fully autonomous hands-off research
  • Aggregates all results into a comprehensive structured report

Sciomc by the numbers

  • 467 all-time installs (skills.sh)
  • +6 installs in the week ending Jul 27, 2026 (Skillselion tracking)
  • Ranked #1,847 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/yeachan-heo/oh-my-claudecode --skill sciomc

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Listed on Skillselion
Installs467
repo stars38.3k
Last updatedAugust 4, 2026
Repositoryyeachan-heo/oh-my-claudecode

How do you run parallel multi-agent research?

Decompose complex research goals into parallel multi-agent investigations, cross-validate findings, and synthesize a comprehensive report with fully autonomous AUTO mode.

Who is it for?

Developers facing complex, multi-domain research questions who need parallel agent investigations with verification before synthesis.

Skip if: Engineers implementing a single known API endpoint with no open research scope should skip sciomc.

When should I use this skill?

User supplies a broad research goal, requests parallel agent investigation, or enables AUTO mode for autonomous research synthesis.

What you get

Decomposed research stages, cross-validated agent findings, and a synthesized comprehensive research report.

  • Decomposed research plan
  • Cross-validated stage findings
  • Comprehensive synthesis report

By the numbers

  • Level 4 skill in oh-my-claudecode metadata
  • 4-stage research workflow: decomposition, execution, verification, synthesis
  • 402 catalog installs for skill:yeachan-heo/oh-my-claudecode#sciomc

Files

SKILL.mdMarkdownGitHub ↗

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 ComplexityAgentModelUse For
Data gatheringscientist (model=haiku)haikuFile enumeration, pattern counting, simple lookups
Standard analysisscientistsonnetCode analysis, pattern detection, documentation review
Complex reasoningscientistopusArchitecture analysis, cross-cutting concerns, hypothesis validation

Routing Decision Guide

Research TaskTierExample 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

TagMeaningWhen to Use
[PROMISE:RESEARCH_COMPLETE]Research finished successfullyAll stages done, verified, report generated
[PROMISE:RESEARCH_BLOCKED]Cannot proceedMissing 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

CommandAction
/oh-my-claudecode:sciomc statusShow current session progress
/oh-my-claudecode:sciomc resumeResume most recent interrupted session
/oh-my-claudecode:sciomc resume <session-id>Resume specific session
/oh-my-claudecode:sciomc listList all sessions with status
/oh-my-claudecode:sciomc report <session-id>Generate/regenerate report
/oh-my-claudecode:sciomc cancelCancel 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 = /\[CONFIDENCE:(HIGH|MEDIUM|LOW)\]\s*(.*)/g;

// Stage completion
const stageCompletePattern = /\[STAGE_COMPLETE:(\d+)\]/;

// Verification result
const verificationPattern = /\[(VERIFIED|CONFLICTS):?(.*?)\]/;

Evidence Window

When extracting evidence, include context window:

[EVIDENCE:F1]
- File: /src/auth/login.ts
- Lines: 45-52 (context: 40-57)
- Content:

// Lines 45-52 with 5 lines context above/below

[/EVIDENCE]

Quality Validation

Findings must meet quality threshold:

Quality CheckRequirement
Evidence presentAt least 1 [EVIDENCE] per [FINDING]
Confidence statedEach finding has [CONFIDENCE]
Source citedFile paths are absolute and valid
ReproducibleAnother agent could verify

Report Generation

Report Template

# Research Report: {{GOAL}}

**Session ID:** {{SESSION_ID}}
**Date:** {{DATE}}
**Status:** {{STATUS}}

## Executive Summary

{{2-3 paragraph summary of key findings}}

## Methodology

### Research Stages

| Stage | Focus | Tier | Status |
|-------|-------|------|--------|
{{STAGES_TABLE}}

### Approach

{{Description of decomposition rationale and execution strategy}}

## Key Findings

### Finding 1: {{TITLE}}

**Confidence:** {{HIGH|MEDIUM|LOW}}

{{Detailed finding with evidence}}

#### Evidence

{{Embedded evidence blocks}}

### Finding 2: {{TITLE}}
...

## Visualizations

{{FIGURES}}

## Cross-Validation Results

{{Verification summary, any conflicts resolved}}

## Limitations

- {{Limitation 1}}
- {{Limitation 2}}
- {{Areas not covered and why}}

## Recommendations

1. {{Actionable recommendation}}
2. {{Actionable recommendation}}

## Appendix

### Raw Data

{{Links to raw findings files}}

### Session State

{{Link to state.json}}

Figure Embedding Protocol

Scientists generate visualizations using this marker:

[FIGURE:path/to/figure.png]
Caption: Description of what the figure shows
Alt: Accessibility description
[/FIGURE]

Report generator embeds figures:

## Visualizations

![Figure 1: Description](figures/figure-1.png)
*Caption: Description of what the figure shows*

![Figure 2: Description](figures/figure-2.png)
*Caption: Description of what the figure shows*

Figure Types

TypeUse ForGenerated By
Architecture diagramSystem structurescientist
Flow chartProcess flowsscientist
Dependency graphModule relationshipsscientist
TimelineSequence of eventsscientist
Comparison tableA vs B analysisscientist

Configuration

Optional settings in .claude/settings.json:

{
  "omc": {
    "research": {
      "maxIterations": 10,
      "maxConcurrentScientists": 5,
      "defaultTier": "MEDIUM",
      "autoVerify": true,
      "generateFigures": true,
      "evidenceContextLines": 5
    }
  }
}

Cancellation

/oh-my-claudecode:cancel

Or say: "stop research", "cancel research", "abort"

Progress is preserved in .omc/research/{session-id}/ for resume.

Troubleshooting

Stuck in verification loop?

  • Check for conflicting findings between stages
  • Review state.json for specific conflicts
  • May need to re-run specific stages with different approach

Scientists returning low-quality findings?

  • Check tier assignment - complex analysis needs HIGH tier
  • Ensure prompts include clear scope and expected output format
  • Review if research goal is too broad

AUTO mode exhausted iterations?

  • Review state to see where it's stuck
  • Check if goal is achievable with available data
  • Consider breaking into smaller research sessions

Missing figures in report?

  • Verify figures/ directory exists
  • Check [FIGURE:] tags in findings
  • Ensure paths are relative to session directory

Related skills

How it compares

Pick sciomc over single-agent research prompts when questions need parallel investigations and cross-validated synthesis.

FAQ

What stages does sciomc run?

sciomc runs four stages—decomposition, parallel scientist-agent execution, verification through cross-validation, and synthesis into a comprehensive report—with optional AUTO mode for fully autonomous research workflows inside yeachan-heo/oh-my-claudecode as a level 4 orchestrati

What is sciomc AUTO mode?

sciomc AUTO mode executes the full parallel research workflow autonomously after the developer supplies a research goal via the argument hint, removing manual orchestration between decomposition, execution, verification, and final synthesis report generation across scientist agen

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