Deep Research

Deep Research

Agent-orchestrated research pipeline. Takes input text, decomposes into parallel agent tasks, synthesizes results into a citation-backed report.

Context budget: Orchestrator stays lean. Agents write to files, return summaries. Synthesis is delegated to a sub-agent. NEVER read agent output files or reference files into orchestrator context — pass paths to sub-agents. NEVER use run_in_background — it causes TaskOutput to return full agent logs. If context approaches limit before synthesis, the research is lost.

Workflow

1. Analyze Input

Classify the input:

• Question: Direct research question → extract core topic + angles
• Brief: Context document with research directive → extract what to investigate
• Seed context: Background text that needs expansion → identify knowledge gaps

Determine complexity (drives agent count):

• Focused (3 agents): Single topic, clear boundaries
• Broad (4-5 agents): Multi-faceted topic, comparison, or trend analysis

2. Decompose into Agent Tasks

Break research into 3-5 independent investigation angles. Each angle becomes a Task agent.

Decomposition heuristics:

• One angle per distinct sub-question or perspective
• Separate factual retrieval from opinion/analysis sources
• Include at least one critical/contrarian angle
• If project context is relevant, dedicate one agent to local analysis

Agent types (see agent-templates for full prompts):

3. Deploy Agents (Parallel)

CRITICAL: Launch ALL agents in a single message with multiple Task tool calls.

CRITICAL: Do NOT use run_in_background: true. Launch all agents as parallel Task calls in a single message. They execute concurrently, and each returns ONLY the agent's final message (the 3-5 line summary). Background agents write full conversation logs to output files — reading those with TaskOutput will overflow orchestrator context.

Context management: Do NOT read reference files into orchestrator context. Instead, inline the relevant template from agent-templates directly into each agent's prompt.

Output directory: Before launching agents, create an output directory: [report-directory]/research-data/

Each agent prompt MUST include these instructions:

1. Write full findings to a file: [output-dir]/agent-[angle-slug].md using the Write tool
2. Return ONLY a 3-5 line summary to the orchestrator containing:
   • File path where findings were written
   • Top 3 key findings (one line each)
   • Overall confidence level (high/medium/low)

[Single message — all parallel]
Task(subagent_type="general-purpose", description="Research angle A", prompt=<template with OUTPUT_DIR + file-write instructions>)
Task(subagent_type="general-purpose", description="Research angle B", prompt=<template with OUTPUT_DIR + file-write instructions>)
Task(subagent_type="general-purpose", description="Research angle C", prompt=<template with OUTPUT_DIR + file-write instructions>)
Task(subagent_type="Explore", description="Local context analysis", prompt=<template with OUTPUT_DIR + file-write instructions>)
...

Each agent returns ONLY a concise summary (NOT full findings) — see return format in agent-templates. Full findings are written to the agent's output file.

4. Synthesize & Write Report (Delegated)

CRITICAL: Do NOT synthesize in main context. Delegate to a synthesis sub-agent.

Output location: [relevant-project-or-area-folder]/research-[topic-slug]-[YYYY-MM-DD].md If no clear project context, ask the user where to save.

Spawn a single general-purpose synthesis agent using the prompt from synthesis-templates. Fill in:

• RESEARCH BRIEF: the original input context
• AGENT SUMMARIES: the 3-5 line summaries returned by each agent
• OUTPUT DIRECTORY: path to research-data/
• REPORT PATH: final report location
• REPORT TEMPLATE PATH: ~/.claude/skills/deep-research/templates/report_template.md

The orchestrator receives only a summary — the full report is written to disk by the sub-agent.

4b. Handle Late Agents

If agents complete after synthesis, spawn a patch agent using the prompt from synthesis-templates. Fill in the late agent's output file path and the existing report path.

5. Validate

Run validation after writing:

python ~/.claude/skills/deep-research/scripts/validate_report.py --report [path]

Optionally verify citations:

python ~/.claude/skills/deep-research/scripts/verify_citations.py --report [path]

If validation fails: fix and re-validate (max 2 attempts).

Anti-Hallucination Protocol

• Source grounding: Every factual claim cites a specific source [N]
• No fabricated citations: If unsure a source says X, do NOT cite it
• Label inference: "This suggests..." not "Research shows..."
• Admit uncertainty: "No sources found" over invented references

Error Handling

• <5 sources after exhaustive search → note limitation, proceed with extra verification
• Agent returns empty/low-quality → spawn replacement with refined query
• 2 validation failures → stop, report issues, ask user

Scripts

• scripts/validate_report.py — Report quality validation
• scripts/verify_citations.py — Citation verification (DOI + URL checks)
• scripts/source_evaluator.py — Source credibility scoring (0-100)
• scripts/citation_manager.py — Citation tracking utilities

References (for sub-agents, not orchestrator)

• Agent Templates — Structured prompts for research agents. Pass to sub-agents or inline into their prompts.
• Synthesis Templates — Prompts for synthesis and patch agents.
• Report Template — Report output structure. Synthesis agent reads this from disk.