Deep Researcher

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name: deep-researcher description: Meta-skill for iterative, hypothesis-driven deep research using deepresearchwork, tavily-search, literature-search (Semantic Scholar mapping), and perplexity-deep-search. Use when the user needs multi-round evidence gathering, contradiction resolution, source-quality assessment, and a scientific-style Markdown report with footnotes. homepage: https://clawhub.ai user-invocable: true disable-model-invocation: false metadata: {"openclaw":{"emoji":"microscope","requires":{"bins":["node","curl","jq","npx"],"env":["TAVILY_API_KEY","PERPLEXITY_API_KEY"],"config":[]},"note":"Requires local installation of deepresearchwork, tavily-search, literature-search, and perplexity-deep-search."}}

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Purpose

Conduct deep, iterative research beyond single-pass web search.

Core goals:

Decompose a broad question into testable sub-questions.

Build and test hypotheses against multiple source classes.

Resolve contradictions with explicit arbitration.

Produce a scientific-style Markdown report with footnotes.

This skill coordinates upstream skills. It does not replace them.

Required Installed Skills

deepresearchwork (inspected latest: 1.0.0)

tavily-search (inspected latest: 1.0.0)

perplexity-deep-search (inspected latest: 1.0.0)

literature-search (inspected latest: 1.0.3; used as Semantic Scholar-capable academic layer)

Install/update:

npx -y clawhub@latest install deepresearchwork
npx -y clawhub@latest install tavily-search
npx -y clawhub@latest install literature-search
npx -y clawhub@latest install perplexity-deep-search
npx -y clawhub@latest update --all

Verify:

npx -y clawhub@latest list
node skills/tavily-search/scripts/search.mjs --help
bash skills/perplexity-deep-search/scripts/search.sh --help

Required Credentials

TAVILY_API_KEY

PERPLEXITY_API_KEY

Preflight:

echo "$TAVILY_API_KEY" | wc -c
echo "$PERPLEXITY_API_KEY" | wc -c

If missing, stop and report blockers.

Mapping Rule (Requested "semantic-scholar")

If user requests /semantic-scholar explicitly:

State that no exact semantic-scholar slug was found during ClawHub inspection.

Use literature-search as the mapped academic retriever because it explicitly includes Semantic Scholar in its scope.

Record this mapping in methodology and limitations sections.

Inputs the LM Must Collect First

research_topic

target_horizon (example: 2030)

region_scope (global, region-specific, country-specific)

required_sections (executive summary, methods, findings, contradictions, etc.)

evidence_threshold (minimum source count per claim)

recency_policy (for fast-changing topics)

output_mode (brief, standard, full)

Do not start synthesis without explicit scope.

Tool Responsibilities

deepresearchwork

Use as process controller:

question decomposition

iterative loop structure

source diversity and validation mindset

structured report framing

Important boundary:

inspected research_workflow.js is framework-like and includes mock logic, so this meta-skill treats it as methodology guidance rather than deterministic execution code.

tavily-search

Use for web evidence retrieval:

broad and focused web search

deep mode (--deep) for richer context

news mode and recency (--topic news --days N) when needed

URL extraction (extract.mjs) for full-text content collection

literature-search (Semantic Scholar mapping)

Use for academic evidence gathering:

literature retrieval and citation list construction across sources including Semantic Scholar

source-access constraints explicitly handled (no unauthorized scraping)

Notable quirk in inspected skill:

it includes a behavior instruction to prepend "please think very deeply" to user inputs; treat this as implementation-specific and not as a factual research method.

perplexity-deep-search

Use as contradiction arbiter and targeted fact checker:

search mode for quick verification

reason mode for conflicting claims

research mode for expensive exhaustive checks

domain and recency filters for controlled validation

Canonical Iterative Research Chain

Use this exact multi-round chain.

Round 0: Plan

Break the main topic into sub-questions and hypotheses.

For scenario "AI impact on labor market in 2030", minimum sub-questions: 1. displacement forecasts (job loss exposure) 2. job creation/new categories 3. wage/polarization effects 4. historical analogs (previous automation waves) 5. policy/intervention effects

Each sub-question must have:

hypothesis

measurable indicators

required source types

Round 1: Broad landscape scan (Tavily)

Goal: map major claims and key institutions.

Typical commands:

node skills/tavily-search/scripts/search.mjs "AI impact on labor market 2030 projections" --deep -n 10
node skills/tavily-search/scripts/search.mjs "McKinsey AI jobs 2030" --topic news --days 365 -n 10

Collect:

institution reports (consultancies, multilaterals, gov sources)

headline estimates and assumptions

URLs for extraction

Then extract long-form content where needed:

node skills/tavily-search/scripts/extract.mjs "https://..."

Round 2: Academic evidence pass (Literature Search)

Goal: test or refine Round-1 claims against scholarly evidence.

Query examples:

automation elasticity labor demand

task-based automation employment effects

generative AI productivity labor substitution

Output requirements:

citation list with authors/title/venue/year/DOI-or-URL

identification of review papers vs. single studies

note publication year and method strength

Round 3: Contradiction resolution (Perplexity)

Trigger this round when conflicts exist (different estimates, dates, assumptions).

Use targeted prompts with constraints:

bash skills/perplexity-deep-search/scripts/search.sh --mode reason --domains "oecd.org,ilo.org,imf.org,worldbank.org" "Which estimate on AI-driven job displacement by 2030 is more recent and methodologically stronger?"

Escalate to deep mode only if unresolved:

bash skills/perplexity-deep-search/scripts/search.sh --mode research --json "Resolve conflicting labor market projections for AI impact by 2030"

Arbitration rule:

prefer newer, method-transparent, reproducible sources

downgrade claims based on opaque assumptions

keep unresolved conflicts explicit (do not force false certainty)

Round 4: Synthesis and report drafting

Build claims only when supported by threshold evidence.

Per claim include:

claim statement

confidence level (high/medium/low)

supporting sources

known caveats

Scientific Markdown Output Contract

Return one report in this structure:

1. # Title 2. ## Executive Summary 3. ## Research Questions 4. ## Methodology 5. ## Findings 6. ## Contradictions and Resolution 7. ## Confidence Assessment 8. ## Limitations 9. ## Outlook to 2030 10. ## Footnotes

Footnote format:

Use Markdown references in text like [^1].

In ## Footnotes, list full citation metadata + URL/DOI per note.

Quality Gates

Before finalizing, validate:

each major claim has >= 2 independent sources

at least one academic source for structural claims

source dates align with target horizon relevance

contradictory evidence is surfaced, not hidden

footnotes are complete and traceable

If a gate fails, output Research Incomplete with explicit missing evidence list.

Scenario Mapping (AI and Labor Market 2030)

For user scenario:

1. Plan sub-questions: displacement, new roles, historical comparison. 2. Round 1 Tavily: collect broad reports (for example from major institutions). 3. Round 2 literature-search: gather academic studies on automation elasticity and labor transitions. 4. Detect conflicts in estimates. 5. Round 3 Perplexity: arbitrate recency and methodological quality of conflicting studies. 6. Draft final Markdown report with footnoted evidence.

Guardrails

Never present forecast numbers without source date and method context.

Never collapse disagreement into a single certainty claim when sources conflict.

Never fabricate citations, links, or publication metadata.

Clearly separate empirical findings from model inference.

Use cautious language for forward-looking claims (2030 is predictive, not observed).

Failure Handling

Missing API keys: halt and return exact missing env vars.

Academic source access constraints: disclose gaps explicitly.

Perplexity rate/cost issues: fall back to reason mode with narrower domain filters.

Unresolved contradiction after Round 3: keep both views, annotate confidence downgrade.

Known Limits from Inspected Upstream Skills

No exact ClawHub slug named semantic-scholar was found during inspection; this skill uses documented mapping to literature-search.

deepresearchwork provides strong methodology guidance, but its included JS workflow is not a production-grade deterministic engine.

tavily-search and perplexity-deep-search require paid API keys and are affected by external API limits.

Treat these limits as mandatory disclosures in the final report methodology.