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Install in OpenClaw
/install verified-research
Description
执行多源核实的深入研究,拆解复杂问题,优先采集高可信度来源,核实信息时效性并生成结构化报告,支持三天缓存。
Usage Guidance
This skill is broadly coherent with its stated purpose (multi-source research), but it has a few important discrepancies you should consider before installing:
- The scripts write summaries into /root/.openclaw/workspace/MEMORY.md automatically during cleanup. That means research summaries will be appended to the agent's global memory without an extra consent step; if you do not want your research summaries persisted globally, do not install or edit the scripts to disable that behavior.
- The metadata declares no required binaries or config paths, but the scripts require Python 3 and common CLI tools (md5sum, stat, sed, xxd, tail, etc.) and expect the workspace path /root/.openclaw/workspace to exist. Consider adding these as declared requirements or run in an environment where these are available.
- cleanup.sh deletes cached research directories after archiving the summary. If you want to keep full reports, explicitly copy them to your workspace before the 3-day window expires.
- If you plan to use this skill in a multi-tenant or sensitive environment, review/modify the scripts to: (a) prompt before writing to global MEMORY.md, (b) avoid hard-coded /root paths, and (c) explicitly declare runtime dependencies in the skill metadata.
- Suggested mitigations: run the skill in a sandboxed agent instance first, inspect/modify the code to change the MEMORY.md write behavior or target a user-controlled path, and confirm Python 3 is available. If these points are acceptable or are intentionally intended behavior, the skill appears functionally coherent; otherwise treat it as suspicious and proceed with caution.
Capability Analysis
Type: OpenClaw Skill
Name: verified-research
Version: 1.2.1
The skill implements a structured multi-source research methodology with a local caching and automated cleanup system. It uses a series of shell and Python scripts (research.sh, manifest.sh, finalize.sh, cleanup.sh) to manage research 'evidence cards' in /tmp/deep-research-cache/ and archive summaries to the workspace's MEMORY.md file. The logic is transparent, well-documented, and lacks any indicators of data exfiltration, unauthorized persistence, or malicious prompt injection.
Capability Assessment
Purpose & Capability
The skill's name/description (deep multi-source research with 3-day cache) aligns with the provided scripts: research.sh, claim-card.sh, manifest.sh, finalize.sh, cleanup.sh implement caching, manifesting, report generation, and auto-archive. However, the skill touches /root/.openclaw/workspace/MEMORY.md (a global workspace memory), which is not declared under required config paths. Also the scripts require Python 3 and common CLI tools (md5sum, stat, sed, xxd, etc.) but the registry lists no required binaries — a mismatch between claimed requirements and actual runtime needs.
Instruction Scope
SKILL.md and the scripts instruct the agent to create per-session cache dirs under /tmp/deep-research-cache, produce reports, and schedule a 3-day cleanup. cleanup.sh will append a summary into /root/.openclaw/workspace/MEMORY.md and then delete the cached directories. This means (a) user research summaries are automatically persisted into the agent's global MEMORY.md without a separate explicit consent step at cleanup time, and (b) cached data is irreversibly deleted after the retention window. SKILL.md stated the full report is not copied to the workspace unless asked, but cleanup.sh still writes a summary to MEMORY.md — a documented contradiction that users should notice.
Install Mechanism
There is no install spec (instruction-only with shipped scripts), which minimizes supply-chain risk. The only risk is runtime: the scripts rely on Python 3 and common POSIX utilities but the skill metadata does not declare these as required binaries. No remote downloads or archive extraction are used.
Credentials
The skill declares no required environment variables or config paths, yet the scripts read/write /root/.openclaw/workspace/MEMORY.md and create /tmp/deep-research-cache/* directories. Writing into a global MEMORY.md (workspace memory) is effectively granting persistent storage access and could surface user data across sessions. No credentials are requested, which is good, but the implicit access to the agent's workspace is not disclosed in metadata.
Persistence & Privilege
always:false (normal). The skill does persist data to disk (temporary cache in /tmp) and appends summaries to the global MEMORY.md on cleanup. Cached directories are removed after the retention period. The automatic write to MEMORY.md and automatic deletion are significant behavioral privileges but are consistent with the skill's stated 3-day cache design — the concern is that this behavior is not declared in the metadata and may be unexpected to users.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install verified-research - After installation, invoke the skill by name or use
/verified-research - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.2.1
Republish with English summary; add 3-day research cache + auto-archive to MEMORY.md
Metadata
Frequently Asked Questions
What is Verified Research?
执行多源核实的深入研究,拆解复杂问题,优先采集高可信度来源,核实信息时效性并生成结构化报告,支持三天缓存。 It is an AI Agent Skill for Claude Code / OpenClaw, with 84 downloads so far.
How do I install Verified Research?
Run "/install verified-research" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Verified Research free?
Yes, Verified Research is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Verified Research support?
Verified Research is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Verified Research?
It is built and maintained by ashanzzz (@ashanzzz); the current version is v1.2.1.
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