A self-configuring, executable algorithm for AI agents to perform research and planning with zero hallucinations and total source traceability.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install zeroloss
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install zeroloss using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Zero-Loss Research & Planning Methodology?
The Zero-Loss Research & Planning Methodology v2.1 is a high-integrity framework designed for Openclaw Skills to handle complex, multi-document tasks without losing information or inventing facts. By treating research as a self-configuring executable algorithm, it guarantees that output word counts meet or exceed source volume and every claim is traceable to a verified authority.
This methodology is essential for high-stakes environments like legal, healthcare, or fintech where data integrity is non-negotiable. It provides a structured 11-process pipeline that guides an AI agent through bootstrapping, validation, and consolidation, ensuring that every decision is logged and every fact is verified against a strict authority hierarchy.
Zero-Loss Research & Planning Methodology Use Cases
- Performing deep gap analysis across multiple technical, legal, or regulatory documents.
- Generating hallucination-free deliverables for funding, compliance, or strategic decision-making.
- Consolidating large volumes of documentation into a single, verified master file without content loss.
- Executing zero-loss translations where numeric accuracy and proper noun preservation are critical.
- Creating audit-ready project scaffolds with automated traceability matrices and process histories.
How Zero-Loss Research & Planning Methodology Works
- The agent bootstraps the project by auto-detecting the domain (e.g., Fintech, SaaS) and creating a structured directory scaffold.
- Source documents are ingested, cataloged, and cross-referenced to identify overlapping topics and potential contradictions.
- A critical review is performed to identify gaps, followed by source validation of claims against an L1-L5 authority hierarchy.
- Identified gaps are prioritized into work packages (P0-P3) based on urgency and dependencies.
- Deliverables are generated following strict anti-hallucination rules and verified against the initial plan.
- Content is refined through multi-pass gap closure loops until 100% plan coverage is achieved.
- The final phase produces a consolidated package including a manifest, build scripts, and comprehensive compliance artifacts.
Zero-Loss Research & Planning Methodology Setup
To utilize this methodology within your Openclaw Skills workflow, ensure the skill is active in your agent configuration. The methodology triggers automatically on research or planning tasks. You can initialize a project manually by providing the source files and the following command:
# Trigger the bootstrap process
initialize zero-loss-research --sources ./input_docs/ --domain auto
Zero-Loss Research & Planning Methodology Data Schema & Taxonomy
The skill organizes its output into a highly structured 3-directory format for maximum traceability:
| Directory | Contents | Purpose |
|---|---|---|
/Deliverables/ |
Reports, spreadsheets, and documents | Final project outputs |
/ProcessArtifacts/ |
Traceability-Matrix, Validated-Claims, Source-Registry | Compliance and audit logs |
/Sources/ |
Original input files | Preservation of source truth |
/ProcessArtifacts/BuildScripts/ |
Archived generation scripts | Reproducibility and automation |
Zero-Loss Research & Planning Methodology Advanced Features
- XML-level document fusion that prevents the 33-85% content loss common in standard LLM rewrites.
- Domain-specific authority source pre-population for industries like Healthcare (FDA/EMA) and FinTech (SEC/FCA).
- Mandatory 7-gate decision logic that prevents the agent from proceeding without explicit user confirmation.
- Automated 4-check verification protocol for translation and localization tasks to ensure semantic and structural parity.
- Quantitative verification metrics, including word count parity checks and plan-vs-content coverage percentages.
SKILL.md
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