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Tech Solution Research
作者
_silhouette
· GitHub ↗
· v0.3.0
· MIT-0
122
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install tech-solution-research
功能描述
技术方案调研/框架选型/技术对比/最终报告生成 — multi-source evidence orchestration for technical decision-making
安全使用建议
This skill is coherent and appears to do what it says: orchestrate multi-source technical research and generate a structured report. Before installing or invoking it, check the following: 1) Confirm which platform-native connectors/CLIs (GitHub/gh, feedgrab, xiaohongshu, agent-browser, moltbook, ClawHub, etc.) your agent has access to and whether those connectors require credentials you consider sensitive. 2) If you expect the skill to use internal documents (internal-assets lane), ensure those accesses are intentional and governed by your access controls. 3) Runtime tests imply executing scripts or commands — review how test artifacts, logs, and credentials will be stored or transmitted. 4) Because the SKILL.md enforces specific 'must use' data sources, verify each referenced skill/connector is trusted; otherwise the agent may fall back to alternative sources and must record that downgrade. If any of the above is unacceptable, restrict or audit the agent's connector permissions before using this skill.
功能分析
Type: OpenClaw Skill
Name: tech-solution-research
Version: 0.3.0
The 'Tech Solution Research' skill bundle is a highly structured framework for technical evaluation and decision-making. It implements a 'Multi-Source Evidence Orchestration' workflow across files like SKILL.md and workflow.md, requiring the agent to cross-reference data from GitHub, official docs, and social platforms using specific tools (e.g., feedgrab, gh CLI). The bundle includes comprehensive guardrails against biased reporting, explicit instructions for conflict resolution, and a detailed scoring rubric (scoring-rubric.md). No indicators of malicious intent, data exfiltration, or harmful prompt injection were found; the instructions are professional and strictly aligned with the stated purpose of technical research.
能力评估
Purpose & Capability
The name/description (technical solution research) match the SKILL.md: it defines multi-source evidence lanes, scoring, runtime tests and a report template. The listed source lanes (official docs, GitHub, platform-native, community, runtime tests, internal-assets, ClawHub) are appropriate for technical evaluation. Nothing requested is out-of-scope for a technical research skill.
Instruction Scope
The instructions strictly prescribe data collection, evidence schema, test/score/templating and 'must use' platform-native skills (feedgrab, xiaohongshu, gh/gh CLI, agent-browser, moltbook-global, ClawHub registry, etc.). That is coherent with the goal, but the skill assumes the agent will call other skills or run runtime tests — which may invoke network calls, CLIs, or execute test scripts. The SKILL.md does not instruct reading unrelated host files or environment variables, but it does include an 'internal-assets' lane that implies access to internal docs/code if available — confirm that such access is intentional and permissioned.
Install Mechanism
No install spec and no code files (instruction-only). This minimizes supply-chain risk: nothing will be downloaded or written by the skill itself.
Credentials
The skill declares no required env vars or credentials, but its runtime rules assume availability of platform-native connectors and CLIs (GitHub/gh, feedgrab, agent-browser, xiaohongshu, moltbook, ClawHub). Those connectors typically need credentials or elevated access. It's normal for a research skill to use such tools, but the skill does not declare or constrain them — verify what credentials the agent already has and whether exposing them to these evidence-collection steps is acceptable.
Persistence & Privilege
always:false and no install lifecycle actions. The skill does not request permanent inclusion or write to other skills' configs. Autonomous invocation is allowed by default (platform normal) but not combined with other red flags here.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install tech-solution-research - 安装完成后，直接呼叫该 Skill 的名称或使用
/tech-solution-research触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v0.3.0
Initial public release with platform-native source routing, source coverage gates, and technical-solution research workflow.
元数据
常见问题
Tech Solution Research 是什么？
技术方案调研/框架选型/技术对比/最终报告生成 — multi-source evidence orchestration for technical decision-making. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 122 次。
如何安装 Tech Solution Research？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install tech-solution-research」即可一键安装，无需额外配置。
Tech Solution Research 是免费的吗？
是的，Tech Solution Research 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Tech Solution Research 支持哪些平台？
Tech Solution Research 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Tech Solution Research？
由 _silhouette（@lanyasheng）开发并维护，当前版本 v0.3.0。
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