
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)
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| Installs | 467 |
|---|---|
| repo stars | ★ 38.3k |
| Last updated | August 4, 2026 |
| Repository | yeachan-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
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 sessionQuick 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 reportState 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 = /\[CONFIDENCE:(HIGH|MEDIUM|LOW)\]\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 Check | Requirement |
|---|---|
| Evidence present | At least 1 [EVIDENCE] per [FINDING] |
| Confidence stated | Each finding has [CONFIDENCE] |
| Source cited | File paths are absolute and valid |
| Reproducible | Another 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

*Caption: Description of what the figure shows*

*Caption: Description of what the figure shows*Figure Types
| Type | Use For | Generated By |
|---|---|---|
| Architecture diagram | System structure | scientist |
| Flow chart | Process flows | scientist |
| Dependency graph | Module relationships | scientist |
| Timeline | Sequence of events | scientist |
| Comparison table | A vs B analysis | scientist |
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:cancelOr 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