Research Ensemble

Deep Research — Adversarial Ensemble v2

Conduct autonomous multi-agent research on topic: $ARGUMENTS

Argument Parsing

Format: /research-ensemble [topic] or /research-ensemble [topic] [level] [domain]

Examples:

• /research-ensemble kubernetes vs nomad → standard, auto-domain (tech)
• /research-ensemble creatine safety deep → deep, auto-domain (health)
• /research-ensemble creatine safety deep health → deep, health
• /research-ensemble market analysis for SaaS standard business → standard, business

Defaults: level=standard, domain=auto-detect, report_language=English

Report language: Use REPORT_LANGUAGE if set. Otherwise honor an explicit language request from the user. If neither is present, write reports, headings, progress logs, and user summaries in English. Russian output is only for an explicit Russian request or REPORT_LANGUAGE=ru.

Level Definitions

Domain Auto-Detection

If domain not specified, detect by keywords in topic:

Execution

Step 0: Preparation

1. Parse arguments: extract {TOPIC}, {LEVEL}, {DOMAIN}
2. Create slug from topic: lowercase, replace spaces with underscores, truncate to 50 chars
3. Determine {REPORT_LANGUAGE} from REPORT_LANGUAGE, then explicit user request, then default English
4. Create output directory structure:

research_output/{topic_slug}/
├── _PROGRESS_LOG.md
├── streams/
│   └── data/
├── deep_dives/          # deep level only
├── reviews/
├── figures/

5. Initialize _PROGRESS_LOG.md:

# Research Log

**Topic:** {TOPIC}
**Level:** {LEVEL}
**Domain:** {DOMAIN}
**Report language:** {REPORT_LANGUAGE}
**Started:** {TIMESTAMP}
**Output:** research_output/{topic_slug}/

6. Read domain configuration: ${CLAUDE_PLUGIN_ROOT}/skills/research-ensemble/references/domains.md
7. Read Exa search guide: ${CLAUDE_PLUGIN_ROOT}/skills/research-ensemble/references/exa-search-guide.md
8. If domain=health AND level=deep: check for user profile at ./research_profiles/my_profile.md. If not found, research runs in universal mode without personalization.

Step 1: Dispatch Workflow

Based on {LEVEL}:

• standard → Read and follow ${CLAUDE_PLUGIN_ROOT}/skills/research-ensemble/references/standard-workflow.md
• deep → Read and follow ${CLAUDE_PLUGIN_ROOT}/skills/research-ensemble/references/deep-workflow.md

Step 2: Finalization

After workflow completes:

1. Verify all expected files exist (ls the output directory)
2. Read the TL;DR section from synthesis.md
3. Generate PDF report (same language as the report files; default English):

python3 "${CLAUDE_PLUGIN_ROOT}/skills/research-ensemble/references/generate-pdf-report.py" \
  "research_output/{topic_slug}" "{TOPIC}" "{LEVEL}" "{DOMAIN}" "{REPORT_LANGUAGE}"

The script outputs the absolute path to the generated PDF. Save it as {PDF_PATH}.

4. Show user summary:

## Research Complete

**Topic:** {TOPIC}
**Level:** {LEVEL} | **Domain:** {DOMAIN}
**Report language:** {REPORT_LANGUAGE}
**Output:** research_output/{topic_slug}/

### Files
- synthesis.md — main report
- reviews/_fact_check.md — fact check
- reviews/_critic_review.md — critic review
- unknowns_and_next.md — open questions and next steps
- **report.pdf** — final PDF report
[+ any additional files depending on level/domain]

### Key Takeaways
[TL;DR section from synthesis.md]

### PDF Report
{PDF_PATH}

Critical Rules

1. Output language follows {REPORT_LANGUAGE}. All agents write files, reports, conclusions, headings, and comments in the selected report language. Default is English. Use Russian only when the user explicitly asks for Russian or REPORT_LANGUAGE=ru. Exa/WebSearch queries may use English for better coverage. Proper names and technical terms may remain in their original language when useful.
2. Every agent writes to files. Nothing is kept only in memory. If an agent fails to write, ORCHESTRATOR writes from the agent's output.
3. Exa is primary search. WebSearch is a fallback. See exa-search-guide.md for strategies.
4. All subagents launched via Agent tool with subagent_type: "general-purpose" and run_in_background: true.
5. Parallel launches in ONE message. Multiple Agent calls in a single message for independent agents.
6. ORCHESTRATOR reads agent prompt templates from references/agent-prompts/, substitutes variables, and passes as Agent prompt.
7. Variable substitution: Replace {TOPIC}, {DOMAIN}, {OUTPUT_DIR}, {STREAM_FILES}, {REASONING_STYLE}, {USER_PROFILE}, {REPORT_LANGUAGE} in agent prompts before dispatching.

Agent Prompt Loading

To dispatch an agent:

1. Read the agent prompt template: ${CLAUDE_PLUGIN_ROOT}/skills/research-ensemble/references/agent-prompts/{role}.md
2. Read domain-specific config from domains.md for the current {DOMAIN}
3. Substitute all {VARIABLES} in the template with actual values
4. Launch via Agent tool with the substituted prompt
5. After completion, verify output file exists — if not, write from agent's returned output