Skill: h4gen/deep-researcher (version 1.0.0)

--- 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."}} --- # 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: ```bash 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: ```bash 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: ```bash 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: ```bash 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: ```bash 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 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 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.

path: references/inspected-skills.md
content: # Inspected Upstream Skills

Directly inspected from ClawHub:

- `deepresearchwork` latest `1.0.0`
- `tavily-search` latest `1.0.0`
- `perplexity-deep-search` latest `1.0.0`
- `literature-search` latest `1.0.3`

## Mapping note for requested stack

Requested tool: `semantic-scholar`

ClawHub status during inspection: no exact `semantic-scholar` slug found.

Operational mapping used in this meta-skill:
- Use `literature-search` as the academic retrieval layer because its scope explicitly includes Semantic Scholar alongside PubMed/arXiv/IEEE/ACM/others.

This mapping must be disclosed in final output whenever used.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)