Conducts stepwise in-depth research by generating detailed source cards with data, quotes, quality scores, cross-analyzing findings, and producing fully cite...

This skill broadly does what it says (deep research and producing source cards), but several internal inconsistencies and operational risks should be resolved before use: - Fix format mismatch: scripts/quality-score.py expects JSON input but templates and SKILL.md produce markdown source cards. Either convert card files to JSON before scoring, adjust the script to parse the markdown frontmatter, or change the templates to emit JSON. Without this, the quality-check step will fail. - Reconcile source-count requirements: different files mention 15 vs 20 required sources. Decide on a single requirement and update SKILL.md, QUALITY_CRITERIA.md, and RESEARCH_PROTOCOL.md to match; otherwise the self-checks may block report generation. - Be aware of data/exposure and copyright: the skill requires copying at least one direct quote of >=50 words from each source into local cards. That may reproduce copyrighted text; consider extracting paraphrases or storing citation metadata rather than long verbatim quotes. - File-system impact: the skill will create and retain many files. Confirm you trust the workspace and are OK with persistent local data (including possibly scraped content from the web). - Operational testing: run a dry test on non-sensitive topics to confirm the workflow and script behavior. If you rely on automatic quality scoring, ensure the scoring script's assumptions about input format and fields (publication_year, sample_size, etc.) match your card data. If these issues are fixed (script/template alignment, unified requirements, and a clear policy about quoting/copyright), the skill appears coherent and usable. As-is, it's more likely to fail or behave unexpectedly, so proceed only after remediation or with caution.

Type: OpenClaw Skill Name: deep-research-v21 Version: 2.1.0 The skill bundle is a highly structured framework for performing deep academic and industry research. While it instructs the OpenClaw agent to execute local shell commands and a Python script (scripts/quality-score.py), these actions are limited to validating file counts, checking for citations, and calculating research quality scores based on provided metadata. There is no evidence of data exfiltration, unauthorized network access, or malicious prompt injection; the 'mandatory' instructions are standard agentic prompting techniques used to ensure the LLM follows a multi-step workflow.