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Install in OpenClaw
/install deep-research-bak
Description
Conducts enterprise-grade research with multi-source synthesis, citation tracking, and verification. Produces citation-backed reports through a structured pi...
README (SKILL.md)
Deep Research
Core Purpose
Deliver citation-backed, verified research reports through a structured pipeline with source credibility scoring, evidence persistence, and progressive context management.
Autonomy Principle: Operate independently. Infer assumptions from context. Only stop for critical errors or incomprehensible queries.
Decision Tree
Request Analysis
+-- Simple lookup? --> STOP: Use WebSearch
+-- Debugging? --> STOP: Use standard tools
+-- Complex analysis needed? --> CONTINUE
Mode Selection
+-- Initial exploration --> quick (3 phases, 2-5 min)
+-- Standard research --> standard (6 phases, 5-10 min) [DEFAULT]
+-- Critical decision --> deep (8 phases, 10-20 min)
+-- Comprehensive review --> ultradeep (8+ phases, 20-45 min)
Default assumptions: Technical query = technical audience. Comparison = balanced perspective. Trend = recent 1-2 years.
Workflow Overview
| Phase | Name | Quick | Standard | Deep | UltraDeep |
|---|---|---|---|---|---|
| 1 | SCOPE | Y | Y | Y | Y |
| 2 | PLAN | - | Y | Y | Y |
| 3 | RETRIEVE | Y | Y | Y | Y |
| 4 | TRIANGULATE | - | Y | Y | Y |
| 4.5 | OUTLINE REFINEMENT | - | Y | Y | Y |
| 5 | SYNTHESIZE | - | Y | Y | Y |
| 6 | CRITIQUE | - | - | Y | Y |
| 7 | REFINE | - | - | Y | Y |
| 8 | PACKAGE | Y | Y | Y | Y |
Execution
On invocation, load relevant reference files:
- Phase 1-7: Load methodology.md for detailed phase instructions
- Phase 8 (Report): Load report-assembly.md for progressive generation
- HTML/PDF output: Load html-generation.md
- Quality checks: Load quality-gates.md
- Long reports (>18K words): Load continuation.md
Templates:
- Report structure: report_template.md
- HTML styling: mckinsey_report_template.html
Scripts:
python scripts/validate_report.py --report [path]python scripts/verify_citations.py --report [path]python scripts/md_to_html.py [markdown_path]
Output Contract
Required sections:
- Executive Summary (200-400 words)
- Introduction (scope, methodology, assumptions)
- Main Analysis (4-8 findings, 600-2,000 words each, cited)
- Synthesis & Insights (patterns, implications)
- Limitations & Caveats
- Recommendations
- Bibliography (COMPLETE - every citation, no placeholders)
- Methodology Appendix
Output files (all to ~/Documents/[Topic]_Research_[YYYYMMDD]/):
- Markdown (primary source)
- HTML (McKinsey style, auto-opened)
- PDF (professional print, auto-opened)
Quality standards:
- 10+ sources, 3+ per major claim
- All claims cited immediately [N]
- No placeholders, no fabricated citations
- Prose-first (>=80%), bullets sparingly
When to Use / NOT Use
Use: Comprehensive analysis, technology comparisons, state-of-the-art reviews, multi-perspective investigation, market analysis.
Do NOT use: Simple lookups, debugging, 1-2 search answers, quick time-sensitive queries.
Usage Guidance
This package appears to implement the advertised research pipeline, but it gives the agent permission to read and write files in your home directory and to spawn recursive continuation agents that load saved state. Before installing or enabling it: 1) review the remaining code files you haven't inspected (especially research_engine.py and verify_citations.py) for network calls or any calls that read arbitrary paths; 2) consider running the skill in a sandboxed account or VM so its file writes (~/Documents and ~/.claude/research_output) are isolated; 3) do not provide unrelated API keys or system credentials; only configure optional search-cli keys if you trust the provider setup; 4) if you allow the skill to run, audit created continuation_state and sources.json files and confirm the skill deletes or manages them per your data-retention policy. If you want, I can scan the remaining omitted scripts for network or file-access patterns and point out any risky lines.
Capability Analysis
Type: OpenClaw Skill
Name: deep-research-bak
Version: 1.0.0
The skill bundle implements a highly autonomous research pipeline that utilizes risky capabilities, including recursive sub-agent spawning via the Task tool (detailed in methodology.md and continuation.md) and broad filesystem access for report assembly. While these features are aligned with the stated goal of 'Enterprise-grade research,' they introduce significant operational risk. Furthermore, scripts/verify_citations.py contains a potential SSRF vulnerability by performing network requests to arbitrary URLs extracted from research content without validation. The bundle also promotes the installation of an external third-party utility (search-cli) from a specific GitHub repository.
Capability Assessment
Purpose & Capability
Name/description align with the included files: citation manager, report validators, markdown→HTML, source evaluator, and an orchestration engine. Persisting reports and citations to disk and converting to PDF/HTML are coherent with an enterprise research skill.
Instruction Scope
SKILL.md explicitly instructs the agent to run shell commands (e.g., date), read and write files under ~/Documents and ~/.claude/research_output, spawn Task sub-agents that load continuation_state and report files, and 'Grep/Read for local documentation'. Those directives permit reading arbitrary local documents and recursive agent spawning; while plausible for deep research, they broaden what the agent may access and persist beyond the narrow task of web search and synthesis.
Install Mechanism
No install spec (instruction-only) — lowest install risk. README suggests optional external tools (brew search-cli, pip install weasyprint) if the user opts in; those are optional and clearly documented.
Credentials
The skill declares no required env vars or credentials. However README and SKILL.md suggest optional multi-provider search-cli setup requiring provider API keys, and the skill will run network-based citation verification. The absence of declared env vars is consistent but you should not supply unrelated credentials. The skill does persist state to home directory.
Persistence & Privilege
The skill persists continuation_state and sources.json under ~/.claude/research_output and writes reports to ~/Documents. It also instructs spawning continuation agents (Task tool) that read/write those files recursively. 'always' is false, but the persistence and recursive agent spawning increase blast radius if misused or if underlying orchestration code performs unexpected reads or network calls.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install deep-research-bak - After installation, invoke the skill by name or use
/deep-research-bak - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of deep-research: structured, citation-backed enterprise research.
- Produces comprehensive research reports with multi-source synthesis and source credibility scoring.
- Triggers only on complex research needs (e.g., "deep research," "compare X vs Y," "analyze trends").
- Follows a multi-phase workflow (scope, plan, retrieve, triangulate, synthesize, critique, package) with configurable depth.
- Enforces stringent output standards: executive summary, detailed findings, full bibliography, and professional file exports.
- Explicitly not for simple lookups, debugging, or single-answer queries.
Metadata
Frequently Asked Questions
What is Deep Research.Bak?
Conducts enterprise-grade research with multi-source synthesis, citation tracking, and verification. Produces citation-backed reports through a structured pi... It is an AI Agent Skill for Claude Code / OpenClaw, with 66 downloads so far.
How do I install Deep Research.Bak?
Run "/install deep-research-bak" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Deep Research.Bak free?
Yes, Deep Research.Bak is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Deep Research.Bak support?
Deep Research.Bak is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Deep Research.Bak?
It is built and maintained by emiltsoi (@emiltsoi); the current version is v1.0.0.
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