Deep Research

Deep Research

Deterministic research pipeline that produces reproducible, schema-validated output. Same input + same state = same output.

Triggers

Activate when the user asks for rigorous multi-source research or uses any of these commands:

• /deep-research [topic]
• /deepresearch [topic]
• /dpr [topic]
• /dp [topic]
• /dr [topic]

Quick Start

S="{baseDir}/scripts"
B="{baseDir}"

# Full deterministic run (after claims + evidence are collected):
python3 $S/run_deep_research.py \
  --topic "Does caffeine improve focus?" \
  --topic-summary "Common belief that caffeine enhances concentration." \
  --claims-file /tmp/dr-claims.json \
  --evidence-file /tmp/dr-evidence.json \
  --policy $B/policy.json \
  --schema $B/output-schema.json \
  --state $B/state.json \
  --output-json /tmp/dr-output.json \
  --output-md /tmp/dr-output.md

Exit codes: 0 success, 1 validation/policy error, 2 partial retrieval, 3 no-evidence fallback.

Workflow

Phase 1: General Research (Agent-Driven)

Search the web to understand the topic. Identify up to 5 key claims to fact-check.

Write claims to a JSON file using this exact schema:

[
  {
    "claim_text": "Caffeine (100-300mg) improves sustained attention",
    "evidence_required": "RCTs or meta-analyses on caffeine and attention",
    "confidence_target": 0.7
  }
]

Required fields per claim: claim_text, evidence_required, confidence_target.

Phase 2: Scientific Fact-Check (Agent-Driven)

For each claim, search source databases in tier order:

Evidence budget per claim: 2 peer-reviewed + 1 fallback (max 5 total).

Write evidence to a JSON file:

[
  {
    "claim_id": "C001",
    "sources": [
      {
        "title": "Effects of caffeine on cognitive performance",
        "authors": "Smith et al.",
        "year": 2020,
        "tier": "peer_reviewed",
        "url": "https://pubmed.ncbi.nlm.nih.gov/12345678",
        "citation": "Smith et al., \"Effects of caffeine on cognitive performance\", J. Neuroscience, 2020. https://pubmed.ncbi.nlm.nih.gov/12345678",
        "supports_claim": true,
        "relevance_note": "RCT showing improved reaction time at 200mg dose",
        "retrieved_at": "2026-02-26T10:00:00+00:00"
      }
    ]
  }
]

Important: Always trace secondary-source citations back to original papers.

Phase 3: Deterministic Classification (Runner)

Run the deterministic runner. It applies these verdict rules from policy.json:

Every verdict includes a decision_trace entry with rule_id, inputs, and result.

Fallback Behaviors

All fallbacks are explicit and policy-defined. There is no implicit "best effort" path.

Fallback Text Blocks (Verbatim)

No peer-reviewed sources:

No peer-reviewed papers found on this specific claim. Checking alternative sources.

No sources at all:

No evidence found across any source tier for this claim.

Retrieval failure:

One or more source databases were unreachable. Results may be incomplete.

Claim extraction failure:

Could not extract structured claims from the topic. Provide explicit claims.

Output Contract

All outputs validate against output-schema.json. Required top-level fields: topic, run_id, policy_version, claims[], verdict_summary, decision_trace[], failures[].

Run IDs follow the pattern: dr-YYYYMMDDTHHMMSSZ-<8-char topic hash>.

Deterministic Ordering

• Claims ordered by claim_id ascending (C001, C002, ...).
• Evidence per claim ordered by: tier (highest first) -> recency (newest first) -> URL lexical.
• Sections in fixed order: topic_summary, claims, evidence_matrix, verdict_summary, decision_trace, failures.

Source Priority

1. Highest: Peer-reviewed journals (open access)
2. Medium: Preprints (arXiv, bioRxiv) — flag as "not peer-reviewed"
3. Low: Community discussions — flag as anecdotal
4. Lowest: Social media — flag as unverified

Deduplication

Sources are deduped by composite key: title_normalized + canonical_url_host + publication_year. URLs are normalized by stripping query parameters and fragments.

File Inventory

skills/deep-research/
  SKILL.md              # This file
  policy.json           # Deterministic decision rules
  output-schema.json    # JSON Schema for output validation
  state.json            # Run history, dedupe fingerprints, claim canonicalization
  scripts/
    ./scripts/run_deep_research.py  # Deterministic runner (collect/normalize/classify/render/validate)
  tests/
    fixtures/             # Test input fixtures
    ./tests/test_determinism.py   # Reproducibility and schema tests

Scripts Reference

./scripts/run_deep_research.py