plugins/cache/omc/oh-my-claudecode/4.8.2/skills/sciomc/SKILL.md
Orchestrate parallel scientist agents for comprehensive analysis with AUTO mode
npx skillsauth add moliboy5000/.claude sciomcInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Orchestrate parallel scientist agents for comprehensive research workflows with optional AUTO mode for fully autonomous execution.
Research is a multi-stage workflow that decomposes complex research goals into parallel investigations:
/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
/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?
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>
...
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...")
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 |
| 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" |
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 runs the complete research workflow autonomously with loop control.
[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}}
| 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 |
/oh-my-claudecode:cancel or "stop", "cancel"/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]
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/...")
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...")
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: ...
")
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)
.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
{
"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"
}
| 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) |
Scientists use structured tags for findings. Extract them with these patterns:
[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>
// 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 = /\[(VERIFIED|CONFLICTS):?(.*?)\]/;
When extracting evidence, include context window:
[EVIDENCE:F1]
- File: /src/auth/login.ts
- Lines: 45-52 (context: 40-57)
- Content:
```typescript
// 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
```markdown
# 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}}
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*
| 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 |
Optional settings in .claude/settings.json:
{
"omc": {
"research": {
"maxIterations": 10,
"maxConcurrentScientists": 5,
"defaultTier": "MEDIUM",
"autoVerify": true,
"generateFigures": true,
"evidenceContextLines": 5
}
}
}
/oh-my-claudecode:cancel
Or say: "stop research", "cancel research", "abort"
Progress is preserved in .omc/research/{session-id}/ for resume.
Stuck in verification loop?
Scientists returning low-quality findings?
AUTO mode exhausted iterations?
Missing figures in report?
tools
MANDATORY prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).
development
DEFAULT PIPELINE for all tasks requiring execution. You (Claude) are the strategic orchestrator. Codex agents are your implementation army - hyper-focused coding specialists. Trigger on ANY task involving code, file modifications, codebase research, multi-step work, or implementation. This is NOT optional - Codex agents are the default for all execution work. Only skip if the user explicitly asks you to do something yourself.
development
This skill should be used when the user asks to analyze a UI screen recording and map interaction states into Figma. Trigger for requests such as "put video frames in Figma", "extract states from my recording", "map interactions from video to Figma", "analyze this screen recording", "create a storyboard from my video", "deconstruct this interaction in Figma", "annotate the UI states in my recording", or "pull the key moments from this video into Figma". Also trigger when the user references a video file (.mp4, .mov, .webm, .avi) together with Figma, design review, interaction analysis, prototypes, or UI states. The skill extracts key visual moments from a video, infers interaction triggers, and builds an annotated Figma Design storyboard using native Figma annotations and uploaded screenshot assets.
development
Generate a FigJam project plan board from a PRD plus codebase context. Interactive flow: research → propose sections → per-section deep research → per-section content + block-shape proposal → create FigJam → skeleton → fill → diagrams → wrap. Each content block (section, nested section, intro callout, table, multi-column text, sticky column, diagram section, metadata strip) has its own subskill reference file. Use when the user asks for 'project plan in FigJam', 'interactive project plan', '/generate-project-plan', or provides a PRD and wants per-section confirmation on content + rendering.